File: diffusion.bib

package info (click to toggle)
dipy 0.7.1-2
links: PTS, VCS
area: main
in suites: jessie, jessie-kfreebsd
size: 43,276 kB
ctags: 9,823
sloc: python: 22,407; makefile: 260; pascal: 155; sh: 15; ansic: 11
file content (10001 lines) | stat: -rw-r--r-- 537,291 bytes
parent folder | download | duplicates (2)
@comment{This file has been generated by Pybliographer}


@Article{Garyfallidis2009b,
  Author         = {Garyfallidis, Eleftherios and Brett, Matthew and
                   Nimmo-smith, Ian},
  Title          = {{Fast Dimensionality Reduction for Brain Tractography}},
  Journal        = {Computer},
  Volume         = {15},
  Number         = {6},
  Pages          = {2009--2009},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Garyfallidis, Brett, Nimmo-smith -
                   2009 - Fast Dimensionality Reduction for Brain
                   Tractography.pdf:pdf},
  year           = 2009
}

@Article{Ese2006,
  Author         = {Ese, T H},
  Title          = {{Analysis and Classification of EEG Signals using
                   Probabilistic Models for Brain Computer Interfaces
                   Ecole Polytechnique F ´ ed ´ erale de Lausanne Silvia
                   Chiappa}},
  Journal        = {Learning},
  Volume         = {3547},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ese - 2006 - Analysis and
                   Classification of EEG Signals using Probabilistic
                   Models for Brain Computer Interfaces Ecole
                   Polytechnique F ´ ed ´ erale de Lausanne Silvia
                   Chiappa.pdf:pdf},
  year           = 2006
}

@Article{Oliphant2010,
  Author         = {Oliphant, Travis E},
  Title          = {{Guide to NumPy}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Oliphant - 2010 - Guide to
                   NumPy.pdf:pdf},
  year           = 2010
}

@Article{yamamoto2007dtf,
  Author         = {Yamamoto, A. and Miki, Y. and Urayama, S. and Fushimi,
                   Y. and Okada, T. and Hanakawa, T. and Fukuyama, H. and
                   Togashi, K.},
  Title          = {{Diffusion tensor fiber tractography of the optic
                   radiation: analysis with 6-, 12-, 40-, and
                   81-directional motion-probing gradients, a preliminary
                   study}},
  Journal        = {American Journal of Neuroradiology},
  Volume         = {28},
  Number         = {1},
  Pages          = {92},
  publisher      = {Am Soc Neuroradiology},
  year           = 2007
}

@Article{FW05,
  Author         = {Friman, O. and Westin, C. F.},
  Title          = {Uncertainty in white matter fiber tractography.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {8},
  Number         = {Pt 1},
  Pages          = {107-14},
  abstract       = {In this work we address the uncertainty associated
                   with fiber paths obtained in white matter fiber
                   tractography. This uncertainty, which arises for
                   example from noise and partial volume effects, is
                   quantified using a Bayesian modeling framework. The
                   theory for estimating the probability of a connection
                   between two areas in the brain is presented, and a new
                   model of the local water diffusion profile is
                   introduced. We also provide a theorem that facilitates
                   the estimation of the parameters in this diffusion
                   model, making the presented method simple to implement.},
  authoraddress  = {Laboratory of Mathematics in Imaging, Department of
                   Radiology Brigham and Women's Hospital, Harvard Medical
                   School, USA.},
  keywords       = {*Algorithms ; Artificial Intelligence ; Brain/*anatomy
                   \& histology ; Diffusion Magnetic Resonance
                   Imaging/*methods ; Humans ; Image Enhancement/*methods
                   ; Image Interpretation, Computer-Assisted/*methods ;
                   Imaging, Three-Dimensional/*methods ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Pattern Recognition,
                   Automated/methods ; Reproducibility of Results ;
                   Sensitivity and Specificity},
  language       = {eng},
  medline-crdt   = {2006/05/12 09:00},
  medline-da     = {20060511},
  medline-dcom   = {20060609},
  medline-edat   = {2006/05/12 09:00},
  medline-fau    = {Friman, Ola ; Westin, Carl-Fredrik},
  medline-gr     = {P41-RR13218/RR/NCRR NIH HHS/United States},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-lr     = {20071114},
  medline-mhda   = {2006/06/10 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {16685835},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2005;8(Pt 1):107-14.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16685835},
  year           = 2005
}

@Article{BaoPMB2009,
  Author         = {Bao, LJ and Zhu, YM and Liu, WY and Croisille, P. and
                   Pu, ZB and Robini, M. and Magnin, IE},
  Title          = {{Denoising human cardiac diffusion tensor magnetic
                   resonance images using sparse representation combined
                   with segmentation}},
  Journal        = {Physics in Medicine and Biology},
  Volume         = {54},
  Number         = {6},
  Pages          = {1435--1456},
  abstract       = {Cardiac diffusion tensor magnetic resonance imaging
                   (DT-MRI) is noise sensitive, and the noise can induce
                   numerous systematic errors in subsequent parameter
                   calculations. This paper proposes a sparse
                   representation-based method for denoising cardiac
                   DT-MRI images. The method first generates a dictionary
                   of multiple bases according to the features of the
                   observed image. A segmentation algorithm based on
                   nonstationary degree detector is then introduced to
                   make the selection of atoms in the dictionary adapted
                   to the image's features. The denoising is achieved by
                   gradually approximating the underlying image using the
                   atoms selected from the generated dictionary. The
                   results on both simulated image and real cardiac DT-MRI
                   images from ex vivo human hearts show that the proposed
                   denoising method performs better than conventional
                   denoising techniques by preserving image contrast and
                   fine structures.},
  year           = 2009
}

@Article{Baldi,
  Author         = {Baldi, P and Kerkyacharian, G and Matematica,
                   Dipartimento and Tor, Roma},
  Title          = {{arXiv : 0807 . 5059v1 [ math . ST ] 31 Jul 2008
                   Adaptive density estimation for directional data using
                   needlets}},
  arxivid        = {arXiv:0807.5059v1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Baldi et al. - Unknown - arXiv 0807
                   . 5059v1 math . ST 31 Jul 2008 Adaptive density
                   estimation for directional data using needlets.pdf:pdf},
  keywords       = {and phrases,density estimation,needlets,spherical and
                   directional data,thresholding}
}

@Article{Science2008,
  Author         = {Science, Computer and Supervisor, Thesis and Wells,
                   William M and Westin, Carl-fredrik and Orlando, Terry P},
  Title          = {{Quantitative Analysis of Cerebral White Matter
                   Anatomy from Diffusion MRI by}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Science et al. - 2008 - Quantitative
                   Analysis of Cerebral White Matter Anatomy from
                   Diffusion MRI by.pdf:pdf},
  year           = 2008
}

@Article{Simon2005NeuroImage,
  Author         = {Simon, Tony J. and Ding, Lijun and Bish, Joel P. and
                   McDonald-McGinn, Donna M. and Zackai, Elaine H. and
                   Geeb, James},
  Title          = {Volumetric, connective, and morphologic changes in the
                   brains of children with chromosome 22q11.2 deletion
                   syndrome: an integrative study},
  Journal        = {NeuroImage},
  Volume         = {25},
  Pages          = {169-180},
  abstract       = {Chromosome 22q11.2 deletion syndrome is a highly
                   prevalent genetic disorder whose manifestations include
                   developmental disability and sometimes mental
                   retardation. The few studies that have examined brain
                   morphology in different samples from this population
                   have found similar general patterns, mostly using
                   region of interest measures. We employed voxel-based
                   techniques to concurrently examine specific morphologic
                   changes in multiple brain tissue measures. Results were
                   similar to previous findings of volumetric reductions
                   in the posterior brain. They also extended them in two
                   ways. First, our methods provided greater specificity
                   in the localization of changes detected. Second, the
                   combination of our measures of gray and white matter
                   along with cerebrospinal fluid volume and fractional
                   anisotropy, which indicates the structure of white
                   matter, showed a posterior displacement of and
                   morphologic changes to the corpus callosum in affected
                   children.},
  doi            = {j.neuroimage.2004.11.018},
  file           = {attachment\:Simon2005NeuroImage.pdf:attachment\:Simon2005NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2005
}

@Article{Hagmann2008PLoSBiol,
  Author         = {Hagmann, P and Cammoun, L and Gigandet, X and Meuli, R
                   and Honey, C J and Wedeen, Van J. and Sporns, Olaf },
  Title          = {Mapping the structural core of human cerebral cortex},
  Journal        = {PLoS Biol},
  Volume         = {6},
  Number         = {7},
  Pages          = {e159},
  abstract       = {Structurally segregated and functionally specialized
                   regions of the human cerebral cortex are interconnected
                   by a dense network of cortico-cortical axonal pathways.
                   By using diffusion spectrum imaging, we noninvasively
                   mapped these pathways within and across cortical
                   hemispheres in individual human participants. An
                   analysis of the resulting large-scale structural brain
                   networks reveals a structural core within posterior
                   medial and parietal cerebral cortex, as well as several
                   distinct temporal and frontal modules. Brain regions
                   within the structural core share high degree, strength,
                   and betweenness centrality, and they constitute
                   connector hubs that link all major structural modules.
                   The structural core contains brain regions that form
                   the posterior components of the human default network.
                   Looking both within and outside of core regions, we
                   observed a substantial correspondence between
                   structural connectivity and resting-state functional
                   connectivity measured in the same participants. The
                   spatial and topological centrality of the core within
                   cortex suggests an important role in functional
                   integration.},
  doi            = {doi:10.1371/journal.pbio.0060159},
  file           = {attachment\:Hagmann2008PLoSBiol.pdf:attachment\:Hagmann2008PLoSBiol.pdf:PDF},
  year           = 2008
}

@Article{menzies2008wma,
  Author         = {Menzies, L. and Williams, G.B. and Chamberlain, S.R.
                   and Ooi, C. and Fineberg, N. and Suckling, J. and
                   Sahakian, B.J. and Robbins, T.W. and Bullmore, E.T.},
  Title          = {{White matter abnormalities in patients with
                   obsessive-compulsive disorder and their first-degree
                   relatives}},
  Journal        = {American Journal of Psychiatry},
  Volume         = {165},
  Number         = {10},
  Pages          = {1308},
  publisher      = {Am Psychiatric Assoc},
  year           = 2008
}

@Article{Gong2008CerebralCortex,
  Author         = {Gong, Gaolang and He, Yong and Concha, Luis and Lebel,
                   Catherine and Gross, Donald W. and Evans, Alan C. and
                   Beaulieu, Christian},
  Title          = {{Mapping Anatomical Connectivity Patterns of Human
                   Cerebral Cortex Using In Vivo Diffusion Tensor Imaging
                   Tractography}},
  Journal        = {Cereb. Cortex},
  Pages          = {bhn102},
  abstract       = {The characterization of the topological architecture
                   of complex networks underlying the structural and
                   functional organization of the brain is a basic
                   challenge in neuroscience. However, direct evidence for
                   anatomical connectivity networks in the human brain
                   remains scarce. Here, we utilized diffusion tensor
                   imaging deterministic tractography to construct a
                   macroscale anatomical network capturing the underlying
                   common connectivity pattern of human cerebral cortex in
                   a large sample of subjects (80 young adults) and
                   further quantitatively analyzed its topological
                   properties with graph theoretical approaches. The
                   cerebral cortex was divided into 78 cortical regions,
                   each representing a network node, and 2 cortical
                   regions were considered connected if the probability of
                   fiber connections exceeded a statistical criterion. The
                   topological parameters of the established cortical
                   network (binarized) resemble that of a "small-world"
                   architecture characterized by an exponentially
                   truncated power-law distribution. These characteristics
                   imply high resilience to localized damage. Furthermore,
                   this cortical network was characterized by major hub
                   regions in association cortices that were connected by
                   bridge connections following long-range white matter
                   pathways. Our results are compatible with previous
                   structural and functional brain networks studies and
                   provide insight into the organizational principles of
                   human brain anatomical networks that underlie
                   functional states.},
  doi            = {10.1093/cercor/bhn102},
  eprint         = {http://cercor.oxfordjournals.org/cgi/reprint/bhn102v1.pdf},
  file           = { attachment\:Gong2008CerebralCortex.pdf:
                   attachment\:Gong2008CerebralCortex.pdf:PDF},
  url            = {http://cercor.oxfordjournals.org/cgi/content/abstract/bhn102v1},
  year           = 2008
}

@Article{Carlsson2009,
  Author         = {Carlsson, Gunnar and Emoli, Facundo M},
  Title          = {{Characterization, stability and convergence of
                   hierarchical clustering methods ´}},
  Journal        = {Methods},
  Number         = {April},
  Pages          = {1--23},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Carlsson, Emoli - 2009 -
                   Characterization, stability and convergence of
                   hierarchical clustering methods ´.pdf:pdf},
  year           = 2009
}

@Article{Avram2008NMRBiomed,
  Author         = {Avram, Liat and \{O} zarslan, Evren and Assaf, Yaniv
                   and Bar-Shir, Amnon and Cohen, Yoram and Basser, Peter
                   J.},
  Title          = {Three-dimensional water diffusion in impermeable
                   cylindrical tubes: theory versus experiments},
  Journal        = {NMR IN BIOMEDICINE},
  Volume         = {21},
  Pages          = {888898},
  abstract       = {Characterizing diffusion of gases and liquids within
                   pores is important in understanding numerous transport
                   processes and affects a wide range of practical
                   applications. Previous measurements of the pulsed
                   gradient stimulated echo (PGSTE) signal attenuation,
                   E(q), of water within nerves and impermeable
                   cylindrical microcapillary tubes showed it to be
                   exquisitely sensitive to the orientation of the applied
                   wave vector, q, with respect to the tube axis in the
                   high-q regime. Here, we provide a simple
                   three-dimensional model to explain this angular
                   dependence by decomposing the average propagator, which
                   describes the net displacement of water molecules, into
                   components parallel and perpendicular to the tube wall,
                   in which axial diffusion is free and radial diffusion
                   is restricted. The model faithfully predicts the
                   experimental data, not only the observed diffraction
                   peaks in E(q) when the diffusion gradients are
                   approximately normal to the tube wall, but their sudden
                   disappearance when the gradient orientation possesses a
                   small axial component. The model also successfully
                   predicts the dependence of E(q) on gradient pulse
                   duration and on gradient strength as well as tube inner
                   diameter. To account for the deviation from the narrow
                   pulse approximation in the PGSTE sequence, we use
                   Callaghans matrix operator framework, which this study
                   validates experimentally for the first time. We also
                   show how to combine average propagators derived for
                   classical one-dimensional and two-dimensional models of
                   restricted diffusion (e.g. between plates, within
                   cylinders) to construct composite three-dimensional
                   models of diffusion in complex media containing pores
                   (e.g. rectangular prisms and/ or capped cylinders)
                   having a distribution of orientations, sizes, and
                   aspect ratios. This three-dimensional modeling
                   framework should aid in describing diffusion in
                   numerous biological systems and in a myriad of
                   materials sciences applications.},
  owner          = {ian},
  timestamp      = {2009.03.05},
  year           = 2008
}

@Article{Barmpoutis2007IEEETransMedImag,
  Author         = {Barmpoutis, A. and Vemuri, B. C. and Shepherd, T. M.
                   and Forder, J. R.},
  Title          = {Tensor splines for interpolation and approximation of
                   \{{D}{T}-{MRI}\} with applications to segmentation of
                   isolated rat hippocampi},
  Journal        = {IEEE Transactions on Medical Imaging},
  Volume         = {26},
  Number         = {11},
  Pages          = {1537-1546},
  abstract       = {In this paper, we present novel algorithms for
                   statistically robust interpolation and approximation of
                   diffusion tensors-which are symmetric positive definite
                   (SPD) matrices-and use them in developing a significant
                   extension to an existing probabilistic algorithm for
                   scalar field segmentation, in order to segment
                   diffusion tensor magnetic resonance imaging (DT-MRI)
                   datasets. Using the Riemannian metric on the space of
                   SPD matrices, we present a novel and robust higher
                   order (cubic) continuous tensor product of -splines
                   algorithm to approximate the SPD diffusion tensor
                   fields. The resulting approximations are appropriately
                   dubbed tensor splines. Next, we segment the diffusion
                   tensor field by jointly estimating the label (assigned
                   to each voxel) field, which is modeled by a Gauss
                   Markov measure field (GMMF) and the parameters of each
                   smooth tensor spline model representing the labeled
                   regions. Results of interpolation, approximation, and
                   segmentation are presented for synthetic data and real
                   diffusion tensor fields from an isolated rat
                   hippocampus, along with validation. We also present
                   comparisons of our algorithms with existing methods and
                   show significantly improved results in the presence of
                   noise as well as outliers. },
  doi            = {10.1109/TMI.2007.903195},
  year           = 2007
}

@Article{Kanaan2006,
  Author         = {Kanaan, Richard a and Shergill, Sukhwinder S and
                   Barker, Gareth J and Catani, Marco and Ng, Virginia W
                   and Howard, Robert and McGuire, Philip K and Jones,
                   Derek K},
  Title          = {{Tract-specific anisotropy measurements in diffusion
                   tensor imaging.}},
  Journal        = {Psychiatry research},
  Volume         = {146},
  Number         = {1},
  Pages          = {73--82},
  abstract       = {Diffusion tensor magnetic resonance imaging (DT-MRI)
                   has been used to examine the microstructure of
                   individual white matter tracts, often in
                   neuropsychiatric conditions without identifiable focal
                   pathology. However, the voxel-based group-mapping and
                   region-of-interest (ROI) approaches used to analyse the
                   data have inherent conceptual and practical
                   difficulties. Taking the example of the genu of the
                   corpus callosum in a sample of schizophrenic patients,
                   we discuss the difficulties in attempting to replicate
                   a voxel-based finding of reduced anisotropy using two
                   ROI methods. Firstly we consider conventional ROIs;
                   secondly, we present a novel tractography-based
                   approach. The problems of both methods are explored,
                   particularly of high variance and ROI definition. The
                   potential benefits of the tractographic method for
                   neuropsychiatric conditions with subtle and diffuse
                   pathology are outlined.},
  doi            = {10.1016/j.pscychresns.2005.11.002},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kanaan et al. - 2006 -
                   Tract-specific anisotropy measurements in diffusion
                   tensor imaging..pdf:pdf},
  issn           = {0165-1781},
  keywords       = {Adult,Anisotropy,Brain,Brain: pathology,Diffusion
                   Magnetic Resonance Imaging,Female,Humans,Male,Middle
                   Aged,Schizophrenia,Schizophrenia: pathology},
  pmid           = {16376059},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/16376059},
  year           = 2006
}

@Article{MCC+99,
  Author         = {Mori, S. and Crain, B. J. and Chacko, V. P. and van
                   Zijl, P. C.},
  Title          = {Three-dimensional tracking of axonal projections in
                   the brain by magnetic resonance imaging.},
  Journal        = {Ann Neurol},
  Volume         = {45},
  Number         = {2},
  Pages          = {265-9},
  abstract       = {The relationship between brain structure and complex
                   behavior is governed by large-scale neurocognitive
                   networks. The availability of a noninvasive technique
                   that can visualize the neuronal projections connecting
                   the functional centers should therefore provide new
                   keys to the understanding of brain function. By using
                   high-resolution three-dimensional diffusion magnetic
                   resonance imaging and a newly designed tracking
                   approach, we show that neuronal pathways in the rat
                   brain can be probed in situ. The results are validated
                   through comparison with known anatomical locations of
                   such fibers.},
  authoraddress  = {Department of Radiology, Johns Hopkins Medical School,
                   Baltimore, MD, USA.},
  keywords       = {Animals ; Axons/*physiology ; Brain/*anatomy \&
                   histology ; Magnetic Resonance Imaging/*methods ; Rats},
  language       = {eng},
  medline-crdt   = {1999/02/16 00:00},
  medline-da     = {19990329},
  medline-dcom   = {19990329},
  medline-edat   = {1999/02/16},
  medline-fau    = {Mori, S ; Crain, B J ; Chacko, V P ; van Zijl, P C},
  medline-is     = {0364-5134 (Print)},
  medline-jid    = {7707449},
  medline-jt     = {Annals of neurology},
  medline-lr     = {20061115},
  medline-mhda   = {1999/02/16 00:01},
  medline-own    = {NLM},
  medline-pl     = {UNITED STATES},
  medline-pmid   = {9989633},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {Ann Neurol. 1999 Feb;45(2):265-9.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=9989633},
  year           = 1999
}

@Article{Parker2003,
  Author         = {Parker, Geoffrey J M and Haroon, Hamied a and
                   Wheeler-Kingshott, Claudia a M},
  Title          = {{A framework for a streamline-based probabilistic
                   index of connectivity (PICo) using a structural
                   interpretation of MRI diffusion measurements.}},
  Journal        = {Journal of magnetic resonance imaging : JMRI},
  Volume         = {18},
  Number         = {2},
  Pages          = {242--54},
  abstract       = {PURPOSE: To establish a general methodology for
                   quantifying streamline-based diffusion fiber tracking
                   methods in terms of probability of connection between
                   points and/or regions. MATERIALS AND METHODS: The
                   commonly used streamline approach is adapted to exploit
                   the uncertainty in the orientation of the principal
                   direction of diffusion defined for each image voxel.
                   Running the streamline process repeatedly using Monte
                   Carlo methods to exploit this inherent uncertainty
                   generates maps of connection probability. Uncertainty
                   is defined by interpreting the shape of the diffusion
                   orientation profile provided by the diffusion tensor in
                   terms of the underlying microstructure. RESULTS: Two
                   candidates for describing the uncertainty in the
                   diffusion tensor are proposed and maps of probability
                   of connection to chosen start points or regions are
                   generated in a number of major tracts. CONCLUSION: The
                   methods presented provide a generic framework for
                   utilizing streamline methods to generate probabilistic
                   maps of connectivity.},
  doi            = {10.1002/jmri.10350},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Parker, Haroon, Wheeler-Kingshott -
                   2003 - A framework for a streamline-based probabilistic
                   index of connectivity (PICo) using a structural
                   interpretation of MRI diffusion measurements..pdf:pdf},
  issn           = {1053-1807},
  keywords       = {Anisotropy,Brain,Brain: anatomy \&
                   histology,Diffusion,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Echo-Planar Imaging,Humans,Models,
                   Statistical,Monte Carlo Method,Probability,Uncertainty},
  pmid           = {12884338},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/12884338},
  year           = 2003
}

@Article{December2006,
  Author         = {December, Draft},
  Title          = {{A n I n t r o d u c t i o n t o P r o g r a m m i n g
                   f o r M e d i c a l I m a g e A n a l y s i s w i t h T
                   h e V i s u a l i z a t i o n T o o l k i t X e n o p h
                   o n P a p a d e m e t r i s}},
  Journal        = {Control},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/December - 2006 - A n I n t r o d u
                   c t i o n t o P r o g r a m m i n g f o r M e d i c a l
                   I m a g e A n a l y s i s w i t h T h e V i s u a l i z
                   a t i o n T o o l k i t X e n o p h o n P a p a d e m e
                   t r i s.pdf:pdf},
  year           = 2006
}

@Article{Komodakis2006,
  Author         = {Komodakis, Nikos},
  Title          = {{Optimization Algorithms for Discrete Markov Random
                   Fields , with Applications to Computer Vision}},
  Journal        = {Optimization},
  Number         = {May},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Komodakis - 2006 - Optimization
                   Algorithms for Discrete Markov Random Fields , with
                   Applications to Computer Vision.pdf:pdf},
  year           = 2006
}

@Article{Duru2010a,
  Author         = {Duru, Dilek G\"{o}ksel and Ozkan, Mehmed},
  Title          = {{Determination of neural fiber connections based on
                   data structure algorithm.}},
  Journal        = {Computational intelligence and neuroscience},
  Volume         = {2010},
  Pages          = {251928},
  abstract       = {The brain activity during perception or cognition is
                   mostly examined by functional magnetic resonance
                   imaging (fMRI). However, the cause of the detected
                   activity relies on the anatomy. Diffusion tensor
                   magnetic resonance imaging (DTMRI) as a noninvasive
                   modality providing in vivo anatomical information
                   allows determining neural fiber connections which leads
                   to brain mapping. Still a complete map of fiber paths
                   representing the human brain is missing in literature.
                   One of the main drawbacks of reliable fiber mapping is
                   the correct detection of the orientation of multiple
                   fibers within a single imaging voxel. In this study a
                   method based on linear data structures is proposed to
                   define the fiber paths regarding their diffusivity.
                   Another advantage of the proposed method is that the
                   analysis is applied on entire brain diffusion tensor
                   data. The implementation results are promising, so that
                   the method will be developed as a rapid fiber
                   tractography algorithm for the clinical use as future
                   study.},
  doi            = {10.1155/2010/251928},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Duru, Ozkan - 2010 - Determination
                   of neural fiber connections based on data structure
                   algorithm..pdf:pdf},
  issn           = {1687-5273},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion
                   Tensor Imaging,Diffusion Tensor Imaging:
                   methods,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Linear Models,Neural Pathways,Neural Pathways:
                   anatomy \& histology,Uncertainty},
  month          = jan,
  pmid           = {20069047},
  url            = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2801001\&tool=pmcentrez\&rendertype=abstract},
  year           = 2010
}

@Article{Cook2006,
  Author         = {Cook, P A and Bai, Y and Seunarine, K K and Hall, M G
                   and Parker, G J and Alexander, D C},
  Title          = {{Camino : Open-Source Diffusion-MRI Reconstruction and
                   Processing}},
  Journal        = {Statistics},
  Volume         = {14},
  Pages          = {22858--22858},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Cook et al. - 2006 - Camino
                   Open-Source Diffusion-MRI Reconstruction and
                   Processing.pdf:pdf},
  year           = 2006
}

@Article{CLC+99,
  Author         = {Conturo, T. E. and Lori, N. F. and Cull, T. S. and
                   Akbudak, E. and Snyder, A. Z. and Shimony, J. S. and
                   McKinstry, R. C. and Burton, H. and Raichle, M. E.},
  Title          = {Tracking neuronal fiber pathways in the living human
                   brain.},
  Journal        = {Proc Natl Acad Sci U S A},
  Volume         = {96},
  Number         = {18},
  Pages          = {10422-7},
  abstract       = {Functional imaging with positron emission tomography
                   and functional MRI has revolutionized studies of the
                   human brain. Understanding the organization of brain
                   systems, especially those used for cognition, remains
                   limited, however, because no methods currently exist
                   for noninvasive tracking of neuronal connections
                   between functional regions [Crick, F. \& Jones, E.
                   (1993) Nature (London) 361, 109-110]. Detailed
                   connectivities have been studied in animals through
                   invasive tracer techniques, but these invasive studies
                   cannot be done in humans, and animal results cannot
                   always be extrapolated to human systems. We have
                   developed noninvasive neuronal fiber tracking for use
                   in living humans, utilizing the unique ability of MRI
                   to characterize water diffusion. We reconstructed fiber
                   trajectories throughout the brain by tracking the
                   direction of fastest diffusion (the fiber direction)
                   from a grid of seed points, and then selected tracks
                   that join anatomically or functionally (functional MRI)
                   defined regions. We demonstrate diffusion tracking of
                   fiber bundles in a variety of white matter classes with
                   examples in the corpus callosum, geniculo-calcarine,
                   and subcortical association pathways. Tracks covered
                   long distances, navigated through divergences and tight
                   curves, and manifested topological separations in the
                   geniculo-calcarine tract consistent with tracer studies
                   in animals and retinotopy studies in humans.
                   Additionally, previously undescribed topologies were
                   revealed in the other pathways. This approach enhances
                   the power of modern imaging by enabling study of fiber
                   connections among anatomically and functionally defined
                   brain regions in individual human subjects.},
  authoraddress  = {Department of Radiology and Neuroimaging Laboratory,
                   Mallinckrodt Institute of Radiology, Washington
                   University School of Medicine, 4525 Scott Avenue, St.
                   Louis, MO 63110, USA. tconturo@npg.wustl.edu},
  keywords       = {Brain/anatomy \& histology/*physiology ; *Brain
                   Mapping ; Humans ; Magnetic Resonance Imaging ; Nerve
                   Fibers/*physiology ; Neural Pathways/physiology ;
                   Neurons/*physiology},
  language       = {eng},
  medline-crdt   = {1999/09/01 00:00},
  medline-da     = {19991007},
  medline-dcom   = {19991007},
  medline-edat   = {1999/09/01},
  medline-fau    = {Conturo, T E ; Lori, N F ; Cull, T S ; Akbudak, E ;
                   Snyder, A Z ; Shimony, J S ; McKinstry, R C ; Burton, H
                   ; Raichle, M E},
  medline-gr     = {P01 NS06833/NS/NINDS NIH HHS/United States},
  medline-is     = {0027-8424 (Print)},
  medline-jid    = {7505876},
  medline-jt     = {Proceedings of the National Academy of Sciences of the
                   United States of America},
  medline-lr     = {20081120},
  medline-mhda   = {1999/09/01 00:01},
  medline-oid    = {NLM: PMC17904},
  medline-own    = {NLM},
  medline-pl     = {UNITED STATES},
  medline-pmc    = {PMC17904},
  medline-pmid   = {10468624},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't ;
                   Research Support, U.S. Gov't, P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Proc Natl Acad Sci U S A. 1999 Aug 31;96(18):10422-7.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=10468624},
  year           = 1999
}

@Article{Bradski,
  Author         = {Bradski, Gary and Kaehler, Adrian},
  Title          = {{No Title}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Bradski, Kaehler - Unknown - No
                   Title.pdf:pdf}
}

@Article{Qazi_Neuroimage08,
  Author         = {Qazi, A. A. and Radmanesh, A. and O'Donnell, L. and
                   Kindlmann, G. and Peled, S. and Whalen, S. and Westin,
                   C. F. and Golby, A. J.},
  Title          = {Resolving crossings in the corticospinal tract by
                   two-tensor streamline tractography: {M}ethod and
                   clinical assessment using f{MRI}.},
  Journal        = {Neuroimage},
  abstract       = {An inherent drawback of the traditional diffusion
                   tensor model is its limited ability to provide detailed
                   information about multidirectional fiber architecture
                   within a voxel. This leads to erroneous fiber
                   tractography results in locations where fiber bundles
                   cross each other. This may lead to the inability to
                   visualize clinically important tracts such as the
                   lateral projections of the corticospinal tract. In this
                   report, we present a deterministic two-tensor eXtended
                   Streamline Tractography (XST) technique, which
                   successfully traces through regions of crossing fibers.
                   We evaluated the method on simulated and in vivo human
                   brain data, comparing the results with the traditional
                   single-tensor and with a probabilistic tractography
                   technique. By tracing the corticospinal tract and
                   correlating with fMRI-determined motor cortex in both
                   healthy subjects and patients with brain tumors, we
                   demonstrate that two-tensor deterministic streamline
                   tractography can accurately identify fiber bundles
                   consistent with anatomy and previously not detected by
                   conventional single-tensor tractography. When compared
                   to the dense connectivity maps generated by
                   probabilistic tractography, the method is
                   computationally efficient and generates discrete
                   geometric pathways that are simple to visualize and
                   clinically useful. Detection of crossing white matter
                   pathways can improve neurosurgical visualization of
                   functionally relevant white matter areas.},
  authoraddress  = {Department of Radiology, Brigham and Women's Hospital,
                   Harvard Medical School, USA; University of Copenhagen,
                   Denmark.},
  language       = {ENG},
  medline-aid    = {S1053-8119(08)00779-9 [pii] ;
                   10.1016/j.neuroimage.2008.06.034 [doi]},
  medline-crdt   = {2008/07/29 09:00},
  medline-da     = {20080811},
  medline-dep    = {20080708},
  medline-edat   = {2008/07/29 09:00},
  medline-is     = {1095-9572 (Electronic)},
  medline-jid    = {9215515},
  medline-jt     = {NeuroImage},
  medline-mhda   = {2008/07/29 09:00},
  medline-own    = {NLM},
  medline-phst   = {2008/04/30 [received] ; 2008/06/19 [revised] ;
                   2008/06/19 [accepted]},
  medline-pmid   = {18657622},
  medline-pst    = {aheadofprint},
  medline-pt     = {JOURNAL ARTICLE},
  medline-so     = {Neuroimage. 2008 Jul 8.},
  medline-stat   = {Publisher},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18657622},
  year           = 2008
}

@Article{Nannen2003,
  Author         = {Nannen, Volker},
  Title          = {{The Paradox of Overfitting}},
  Journal        = {Computer},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Nannen - 2003 - The Paradox of
                   Overfitting.pdf:pdf},
  year           = 2003
}

@Article{Klein2007NeuroImage,
  Author         = {Klein, J. C. and Behrens, T. E. and Robson, M. D. and
                   Mackay, C. E. and Higham, D.J. and Johansen-Berg, H.},
  Title          = {Connectivity-based parcellation of human cortex using
                   diffusion \{{M}{RI}\}: establishing reproducibility,
                   validity and observer independence in \{{B}{A}\} 44/45
                   and \{{S}{MA}\}/pre-\{{S}{MA}\}},
  Journal        = {NeuroImage},
  Volume         = {34},
  Number         = {1},
  Pages          = {204-211},
  abstract       = {The identification of specialized, functional regions
                   of the human cortex is a vital precondition for
                   neuroscience and clinical neurosurgery. Functional
                   imaging modalities are used for their delineation in
                   living subjects, but these methods rely on subject
                   cooperation, and many regions of the human brain cannot
                   be activated specifically. Diffusion tractography is a
                   novel tool to identify such areas in the human brain,
                   utilizing underlying white matter pathways to separate
                   regions of differing specialization. We explore the
                   reproducibility, generalizability and validity of
                   diffusion tractography-based localization in four
                   functional areas across subjects, timepoints and
                   scanners, and validate findings against fMRI and
                   post-mortem cytoarchitectonic data. With
                   reproducibility across modalities, clustering methods,
                   scanners, timepoints, and subjects in the order of
                   80-90%, we conclude that diffusion tractography
                   represents a useful and objective tool for parcellation
                   of the human cortex into functional regions, enabling
                   studies into individual functional anatomy even when
                   there are no specific activation paradigms available.},
  file           = {attachment\:Klein2007NeuroImage.pdf:attachment\:Klein2007NeuroImage.pdf:PDF},
  year           = 2007
}

@Article{McNab2008MRM,
  Author         = {Jennifer A. McNab and Karla L. Miller},
  Title          = {Sensitivity of diffusion weighted steady state free
                   precession to anisotropic diffusion},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {60},
  Number         = {2},
  Pages          = {405-413},
  abstract       = {Diffusion-weighted steady-state free precession
                   (DW-SSFP) accumulates signal from multiple echoes over
                   several TRs yielding a strong sensitivity to diffusion
                   with short gradient durations and imaging times.
                   Although the DW-SSFP signal is well characterized for
                   isotropic, Gaussian diffusion, it is unclear how the
                   DW-SSFP signal propagates in inhomogeneous media such
                   as brain tissue. This article presents a more general
                   analytical expression for the DW-SSFP signal which
                   accommodates Gaussian and non-Gaussian spin
                   displacement probability density functions. This new
                   framework for calculating the DW-SSFP signal is used to
                   investigate signal behavior for a single fiber,
                   crossing fibers, and reflective barriers. DW-SSFP
                   measurements in the corpus callosum of a fixed brain
                   are shown to be in good agreement with theoretical
                   predictions. Further measurements in fixed brain tissue
                   also demonstrate that 3D DW-SSFP out-performs 3D
                   diffusion weighted spin echo in both SNR and CNR
                   efficiency providing a compelling example of its
                   potential to be used for high resolution diffusion
                   tensor imaging.},
  owner          = {ian},
  timestamp      = {2009.03.27},
  year           = 2008
}

@Article{Corney2007,
  Author         = {Corney, David and Lotto, R Beau},
  Title          = {{What are lightness illusions and why do we see them?}},
  Journal        = {PLoS computational biology},
  Volume         = {3},
  Number         = {9},
  Pages          = {1790--800},
  abstract       = {Lightness illusions are fundamental to human
                   perception, and yet why we see them is still the focus
                   of much research. Here we address the question by
                   modelling not human physiology or perception directly
                   as is typically the case but our natural visual world
                   and the need for robust behaviour. Artificial neural
                   networks were trained to predict the reflectance of
                   surfaces in a synthetic ecology consisting of 3-D
                   "dead-leaves" scenes under non-uniform illumination.
                   The networks learned to solve this task accurately and
                   robustly given only ambiguous sense data. In
                   addition--and as a direct consequence of their
                   experience--the networks also made systematic "errors"
                   in their behaviour commensurate with human illusions,
                   which includes brightness contrast and
                   assimilation--although assimilation (specifically
                   White's illusion) only emerged when the virtual ecology
                   included 3-D, as opposed to 2-D scenes. Subtle
                   variations in these illusions, also found in human
                   perception, were observed, such as the asymmetry of
                   brightness contrast. These data suggest that
                   "illusions" arise in humans because (i) natural stimuli
                   are ambiguous, and (ii) this ambiguity is resolved
                   empirically by encoding the statistical relationship
                   between images and scenes in past visual experience.
                   Since resolving stimulus ambiguity is a challenge faced
                   by all visual systems, a corollary of these findings is
                   that human illusions must be experienced by all visual
                   animals regardless of their particular neural
                   machinery. The data also provide a more formal
                   definition of illusion: the condition in which the true
                   source of a stimulus differs from what is its most
                   likely (and thus perceived) source. As such, illusions
                   are not fundamentally different from non-illusory
                   percepts, all being direct manifestations of the
                   statistical relationship between images and scenes.},
  doi            = {10.1371/journal.pcbi.0030180},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Corney, Lotto - 2007 - What are
                   lightness illusions and why do we see them.pdf:pdf},
  issn           = {1553-7358},
  keywords       = {Artificial Intelligence,Biomimetics,Biomimetics:
                   methods,Humans,Image Interpretation,
                   Computer-Assisted,Image Interpretation,
                   Computer-Assisted: methods,Nerve Net,Nerve Net:
                   physiology,Optical Illusions,Optical Illusions:
                   physiology,Photometry,Photometry: methods,Visual
                   Perception,Visual Perception: physiology},
  pmid           = {17907795},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/17907795},
  year           = 2007
}

@Article{Commowick2008,
  Author         = {Commowick, O and Arsigny, V and Isambert, a and Costa,
                   J and Dhermain, F and Bidault, F and Bondiau, P-Y and
                   Ayache, N and Malandain, G},
  Title          = {{An efficient locally affine framework for the smooth
                   registration of anatomical structures.}},
  Journal        = {Medical image analysis},
  Volume         = {12},
  Number         = {4},
  Pages          = {427--41},
  abstract       = {Intra-subject and inter-subject nonlinear registration
                   based on dense transformations requires the setting of
                   many parameters, mainly for regularization. This task
                   is a major issue, as the global quality of the
                   registration will depend on it. Setting these
                   parameters is, however, very hard, and they may have to
                   be tuned for each patient when processing data acquired
                   by different centers or using different protocols.
                   Thus, we present in this article a method to introduce
                   more coherence in the registration by using fewer
                   degrees of freedom than with a dense registration. This
                   is done by registering the images only on user-defined
                   areas, using a set of affine transformations, which are
                   optimized together in a very efficient manner. Our
                   framework also ensures a smooth and coherent
                   transformation thanks to a new regularization of the
                   affine components. Finally, we ensure an invertible
                   transformation thanks to the Log-Euclidean polyaffine
                   framework. This allows us to get a more robust and very
                   efficient registration method, while obtaining good
                   results as explained below. We performed a qualitative
                   and quantitative evaluation of the obtained results on
                   two applications: first on atlas-based brain
                   segmentation, comparing our results with a dense
                   registration algorithm. Then the second application for
                   which our framework is particularly well suited
                   concerns bone registration in the lower-abdomen area.
                   We obtain in this case a better positioning of the
                   femoral heads than with a dense registration. For both
                   applications, we show a significant improvement in
                   computation time, which is crucial for clinical
                   applications.},
  doi            = {10.1016/j.media.2008.01.002},
  file           = {::},
  issn           = {1361-8423},
  keywords       = {Algorithms,Brain,Brain: anatomy \&
                   histology,Diagnostic Imaging,Diagnostic Imaging:
                   methods,Humans,Image Processing,
                   Computer-Assisted,Radiotherapy Planning,
                   Computer-Assisted,Radiotherapy Planning,
                   Computer-Assisted: methods,Sensitivity and Specificity},
  month          = aug,
  pmid           = {18325825},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18325825},
  year           = 2008
}

@Article{Kerkyacharian2007a,
  Author         = {Kerkyacharian, G´ Erard and Petrushev, Pencho and
                   Picard, Dominique and Willer, Thomas},
  Title          = {{Needlet algorithms for estimation in inverse
                   problems}},
  Journal        = {Electron. J. Stat},
  Volume         = {1},
  Pages          = {30--76},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kerkyacharian et al. - 2007 -
                   Needlet algorithms for estimation in inverse
                   problems.pdf:pdf},
  year           = 2007
}

@InProceedings{Leow2008ISBI,
  Author         = {Leow, Alex D. and Zhu, Siwei and McMahon, Katie L. and
                   {de Zubicaray}, Greig I. and Meredith, G. Matthew and
                   Wright, Margaret and Thompson, Paul M.},
  Title          = {The Tensor Distribution Function},
  BookTitle      = {5th IEEE International Symposium on Biomedical
                   Imaging: From Nano to Macro},
  Pages          = {FR-P2a (poster)},
  abstract       = {Diffusion weighted MR imaging is a powerful tool that
                   can be employed to study white matter microstructure by
                   examing the 3D displacement profile of water molecules
                   in brain tissue. By applying diffusion-sensitizing
                   gradients along a minimum of 6 directions, second-order
                   tensors can be computed to model dominant diffusion
                   processes. However, it has been shown that conventional
                   DTI is not sufficient to resolve crossing fiber tracts.
                   More recently, High Angular Resolution Diffusion
                   Imaging (HARDI) seeks to address this issue by
                   employing more than 6 gradient directions. In this
                   paper, we introduce the Tensor Distribution Function
                   (TDF), a probability function defined on the space of
                   symmetric and positive definite matrices. Here, fiber
                   crossing is modeled as an ensemble of Gaussian
                   diffusion processes with weights specified by the TDF.
                   Once this optimal TDF is determined, ODF can easily be
                   computed by analytic integration of the resulting
                   displacement probability function. Moreover, principal
                   fiber directions can also be directly derived from the
                   TDF.},
  file           = {attachment\:Leow2008ISBI.pdf:attachment\:Leow2008ISBI.pdf:PDF},
  year           = 2008
}

@Article{Wainwright,
  Author         = {Wainwright, Martin},
  Title          = {{Graphical models and variational methods :
                   Message-passing , convex relaxations , and all that}},
  Journal        = {Electrical Engineering},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wainwright - Unknown - Graphical
                   models and variational methods Message-passing , convex
                   relaxations , and all that.pdf:pdf}
}

@Article{Tuch2004,
  Author         = {Tuch, DS},
  Title          = {{Q-ball imaging}},
  Journal        = {change},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tuch - 2004 - Q-ball imaging.pdf:pdf},
  url            = {http://noodle.med.yale.edu/\~{}mjack/papers/tuch-2004.pdf},
  year           = 2004
}

@Article{RHW+03,
  Author         = {Reese, T. G. and Heid, O. and Weisskoff, R. M. and
                   Wedeen, V. J.},
  Title          = {Reduction of eddy-current-induced distortion in
                   diffusion {MRI} using a twice-refocused spin echo.},
  Journal        = {Magn Reson Med},
  Volume         = {49},
  Number         = {1},
  Pages          = {177-82},
  abstract       = {Image distortion due to field gradient eddy currents
                   can create image artifacts in diffusion-weighted MR
                   images. These images, acquired by measuring the
                   attenuation of NMR signal due to directionally
                   dependent diffusion, have recently been shown to be
                   useful in the diagnosis and assessment of acute stroke
                   and in mapping of tissue structure. This work presents
                   an improvement on the spin-echo (SE) diffusion sequence
                   that displays less distortion and consequently improves
                   image quality. Adding a second refocusing pulse
                   provides better image quality with less distortion at
                   no cost in scanning efficiency or effectiveness, and
                   allows more flexible diffusion gradient timing. By
                   adjusting the timing of the diffusion gradients, eddy
                   currents with a single exponential decay constant can
                   be nulled, and eddy currents with similar decay
                   constants can be greatly reduced. This new sequence is
                   demonstrated in phantom measurements and in diffusion
                   anisotropy images of normal human brain.},
  authoraddress  = {Department of Radiology, Massachusetts General
                   Hospital, Boston, Massachusetts, USA.
                   reese@nmr.MGH.harvard.edu},
  keywords       = {*Artifacts ; Brain/anatomy \& histology/pathology ;
                   Echo-Planar Imaging/methods ; Humans ; Magnetic
                   Resonance Imaging/*methods ; Phantoms, Imaging ;
                   Stroke/diagnosis},
  language       = {eng},
  medline-aid    = {10.1002/mrm.10308 [doi]},
  medline-ci     = {Copyright 2003 Wiley-Liss, Inc.},
  medline-crdt   = {2003/01/02 04:00},
  medline-da     = {20030101},
  medline-dcom   = {20030422},
  medline-edat   = {2003/01/02 04:00},
  medline-fau    = {Reese, T G ; Heid, O ; Weisskoff, R M ; Wedeen, V J},
  medline-gr     = {R01 MH64044/MH/NIMH NIH HHS/United States},
  medline-is     = {0740-3194 (Print)},
  medline-jid    = {8505245},
  medline-jt     = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  medline-lr     = {20071115},
  medline-mhda   = {2003/04/23 05:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {12509835},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, U.S. Gov't, P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Magn Reson Med. 2003 Jan;49(1):177-82.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12509835},
  year           = 2003
}

@Article{hyvarinen2000ica,
  Author         = {Hyv{\"a}rinen, A. and Oja, E.},
  Title          = {{Independent component analysis: algorithms and
                   applications}},
  Journal        = {Neural networks},
  Volume         = {13},
  Number         = {4-5},
  Pages          = {411--430},
  publisher      = {Elsevier},
  year           = 2000
}

@Article{Friedman2008,
  Author         = {Friedman, Jerome and Hastie, Trevor},
  Title          = {{Regularization Paths for Generalized Linear Models
                   via Coordinate Descent}},
  Pages          = {1--22},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Friedman, Hastie - 2008 -
                   Regularization Paths for Generalized Linear Models via
                   Coordinate Descent.pdf:pdf},
  year           = 2008
}

@conference{lee2007trajectory,
  author         = {Lee, J.G. and Han, J. and Whang, K.Y.},
  booktitle      = {Proceedings of the 2007 ACM SIGMOD international
                   conference on Management of data},
  organization   = {ACM},
  pages          = {604},
  title          = {{Trajectory clustering: a partition-and-group
                   framework}},
  year           = 2007
}

@Article{Ipython2008,
  Author         = {Ipython, The and Team, Development},
  Title          = {{IPython Documentation}},
  Journal        = {Development},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ipython, Team - 2008 - IPython
                   Documentation.pdf:pdf},
  year           = 2008
}

@Book{DiffMRIBook,
  Author         = {{Heidi Johansen-Berg}},
  Editor         = {Heidi Johansen-Berg, Oxford Centre for Functional MRI
                   of the Brain (FMRIB), Department of Clinical Neurology
                   and Timothy E.J. Behrens, Department of Experimental
                   Psychology, University of Oxford; Centre for Functional
                   MRI of the Brain (FMRIB)},
  Title          = {Diffusion {MRI}},
  Publisher      = {Academic Press},
  year           = 2009
}

@Article{Guo2005,
  Author         = {Guo, D. and Shamai, S. and Verdu, S.},
  Title          = {{Mutual Information and Minimum Mean-Square Error in
                   Gaussian Channels}},
  Journal        = {IEEE Transactions on Information Theory},
  Volume         = {51},
  Number         = {4},
  Pages          = {1261--1282},
  doi            = {10.1109/TIT.2005.844072},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Guo, Shamai, Verdu - 2005 - Mutual
                   Information and Minimum Mean-Square Error in Gaussian
                   Channels.pdf:pdf},
  issn           = {0018-9448},
  month          = apr,
  url            = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1412024},
  year           = 2005
}

@Article{Mishra2007,
  Author         = {Mishra, Arabinda and Lu, Yonggang and Choe, Ann S and
                   Aldroubi, Akram and Gore, John C and Anderson, Adam W
                   and Ding, Zhaohua},
  Title          = {{An image-processing toolset for diffusion tensor
                   tractography.}},
  Journal        = {Magnetic resonance imaging},
  Volume         = {25},
  Number         = {3},
  Pages          = {365--76},
  abstract       = {Diffusion tensor imaging (DTI)-based fiber
                   tractography holds great promise in delineating
                   neuronal fiber tracts and, hence, providing
                   connectivity maps of the neural networks in the human
                   brain. An array of image-processing techniques has to
                   be developed to turn DTI tractography into a
                   practically useful tool. To this end, we have developed
                   a suite of image-processing tools for fiber
                   tractography with improved reliability. This article
                   summarizes the main technical developments we have made
                   to date, which include anisotropic smoothing,
                   anisotropic interpolation, Bayesian fiber tracking and
                   automatic fiber bundling. A primary focus of these
                   techniques is the robustness to noise and partial
                   volume averaging, the two major hurdles to reliable
                   fiber tractography. Performance of these techniques has
                   been comprehensively examined with simulated and in
                   vivo DTI data, demonstrating improvements in the
                   robustness and reliability of DTI tractography.},
  doi            = {10.1016/j.mri.2006.10.006},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mishra et al. - 2007 - An
                   image-processing toolset for diffusion tensor
                   tractography..pdf:pdf},
  issn           = {0730-725X},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   anatomy \& histology,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Nerve
                   Net,Nerve Net: anatomy \& histology,Neural
                   Pathways,Neural Pathways: anatomy \&
                   histology,Reproducibility of Results,Sensitivity and
                   Specificity,Software},
  pmid           = {17371726},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/17371726},
  year           = 2007
}

@conference{deriche1990dcm,
  author         = {Deriche, R. and Faugeras, O.},
  booktitle      = {Pattern Recognition, 1990. Proceedings., 10th
                   International Conference on},
  title          = {{2-D curve matching using high curvature points:
                   application tostereo vision}},
  volume         = {1},
  year           = 1990
}

@Article{Vogiatzis,
  Author         = {Vogiatzis, George},
  Title          = {{Visual Estimation of Shape , Reflectance and
                   Illumination}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Vogiatzis - Unknown - Visual
                   Estimation of Shape , Reflectance and
                   Illumination.pdf:pdf}
}

@Article{vernooij2007fda,
  Author         = {Vernooij, M W and Smits, M. and Wielopolski, P A and
                   Houston, G C and Krestin, G P and van der Lugt, A.},
  Title          = {{Fiber density asymmetry of the arcuate fasciculus in
                   relation to functional hemispheric language
                   lateralization in both right-and left-handed healthy
                   subjects: A combined fMRI and DTI study}},
  Journal        = {Neuroimage},
  Volume         = {35},
  Number         = {3},
  Pages          = {1064--1076},
  file           = {attachment\:vernooij_arcuate_fasciculus_2007.pdf:attachment\:vernooij_arcuate_fasciculus_2007.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2007
}

@Article{KanaanPsych2006,
  Author         = {Kanaan, R. A. and Shergill, S. S. and Barker, G. J.
                   and Catani, M. and Ng, V. W. and Howard, R. and
                   McGuire, P. K. and Jones, D. K.},
  Title          = {Tract-specific anisotropy measurements in diffusion
                   tensor imaging.},
  Journal        = {Psychiatry Res},
  Volume         = {146},
  Number         = {1},
  Pages          = {73-82},
  abstract       = {Diffusion tensor magnetic resonance imaging (DT-MRI)
                   has been used to examine the microstructure of
                   individual white matter tracts, often in
                   neuropsychiatric conditions without identifiable focal
                   pathology. However, the voxel-based group-mapping and
                   region-of-interest (ROI) approaches used to analyse the
                   data have inherent conceptual and practical
                   difficulties. Taking the example of the genu of the
                   corpus callosum in a sample of schizophrenic patients,
                   we discuss the difficulties in attempting to replicate
                   a voxel-based finding of reduced anisotropy using two
                   ROI methods. Firstly we consider conventional ROIs;
                   secondly, we present a novel tractography-based
                   approach. The problems of both methods are explored,
                   particularly of high variance and ROI definition. The
                   potential benefits of the tractographic method for
                   neuropsychiatric conditions with subtle and diffuse
                   pathology are outlined.},
  authoraddress  = {King's College London, Institute of Psychiatry,
                   London, UK. r.kanaan@iop.kcl.ac.uk},
  keywords       = {Adult ; Anisotropy ; Brain/*pathology ; *Diffusion
                   Magnetic Resonance Imaging ; Female ; Humans ; Male ;
                   Middle Aged ; Schizophrenia/*pathology},
  language       = {eng},
  medline-aid    = {S0925-4927(05)00197-6 [pii] ;
                   10.1016/j.pscychresns.2005.11.002 [doi]},
  medline-crdt   = {2005/12/27 09:00},
  medline-da     = {20060227},
  medline-dcom   = {20060425},
  medline-dep    = {20051220},
  medline-edat   = {2005/12/27 09:00},
  medline-fau    = {Kanaan, Richard A ; Shergill, Sukhwinder S ; Barker,
                   Gareth J ; Catani, Marco ; Ng, Virginia W ; Howard,
                   Robert ; McGuire, Philip K ; Jones, Derek K},
  medline-gr     = {Wellcome Trust/United Kingdom},
  medline-is     = {0165-1781 (Print)},
  medline-jid    = {7911385},
  medline-jt     = {Psychiatry research},
  medline-lr     = {20080417},
  medline-mhda   = {2006/04/28 09:00},
  medline-own    = {NLM},
  medline-phst   = {2005/05/24 [received] ; 2005/09/13 [revised] ;
                   2005/11/03 [accepted] ; 2005/12/20 [aheadofprint]},
  medline-pl     = {Ireland},
  medline-pmid   = {16376059},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {Psychiatry Res. 2006 Jan 30;146(1):73-82. Epub 2005
                   Dec 20.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16376059},
  year           = 2006
}

@Article{Mallo,
  Author         = {Mallo, O and Peikert, R and Sigg, C and Sadlo, F},
  Title          = {{Illuminated lines revisited}},
  Journal        = {In Proceedings of IEEE Visualization},
  Volume         = {pages},
  Pages          = {19--26},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mallo et al. - Unknown - Illuminated
                   lines revisited.pdf:pdf}
}

@Article{HM96,
  Author         = {Haselgrove, J. C. and Moore, J. R.},
  Title          = {Correction for distortion of echo-planar images used
                   to calculate the apparent diffusion coefficient.},
  Journal        = {Magn Reson Med},
  Volume         = {36},
  Number         = {6},
  Pages          = {960-4},
  abstract       = {An algorithm for correcting the distortions that occur
                   in diffusion-weighted echo-planar images due to the
                   strong diffusion-sensitizing gradients is presented.
                   The dominant distortions may be considered to be only
                   changes of scale coupled with a shear and linear
                   translation in the phase-encoding direction. It is then
                   possible to correct for them by using an algorithm in
                   which each line of the image in the phase-encoding
                   direction is considered in turn, with only one
                   parameter (the scale) to be found by searching.},
  authoraddress  = {Department of Radiology, Children's Hospital of
                   Philadelphia, PA 19104, USA.},
  keywords       = {*Algorithms ; Brain/pathology ; Echo-Planar
                   Imaging/*methods ; Humans ; Image Enhancement/*methods
                   ; Sensitivity and Specificity},
  language       = {eng},
  medline-crdt   = {1996/12/01 00:00},
  medline-da     = {19970225},
  medline-dcom   = {19970225},
  medline-edat   = {1996/12/01},
  medline-fau    = {Haselgrove, J C ; Moore, J R},
  medline-is     = {0740-3194 (Print)},
  medline-jid    = {8505245},
  medline-jt     = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  medline-lr     = {20041117},
  medline-mhda   = {1996/12/01 00:01},
  medline-own    = {NLM},
  medline-pl     = {UNITED STATES},
  medline-pmid   = {8946363},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article},
  medline-sb     = {IM},
  medline-so     = {Magn Reson Med. 1996 Dec;36(6):960-4.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=8946363},
  year           = 1996
}

@Article{Kume2005,
  Author         = {Kume, a.},
  Title          = {{Saddlepoint approximations for the Bingham and
                   Fisher-Bingham normalising constants}},
  Journal        = {Biometrika},
  Volume         = {92},
  Number         = {2},
  Pages          = {465--476},
  doi            = {10.1093/biomet/92.2.465},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kume - 2005 - Saddlepoint
                   approximations for the Bingham and Fisher-Bingham
                   normalising constants.pdf:pdf},
  issn           = {0006-3444},
  month          = jun,
  url            = {http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/92.2.465},
  year           = 2005
}

@Article{Koev2006,
  Author         = {Koev, Plamen and Edelman, Alan},
  Title          = {{OF THE HYPERGEOMETRIC FUNCTION OF A MATRIX ARGUMENT}},
  Journal        = {Mathematics of Computation},
  Volume         = {75},
  Number         = {254},
  Pages          = {833--846},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Koev, Edelman - 2006 - OF THE
                   HYPERGEOMETRIC FUNCTION OF A MATRIX ARGUMENT.pdf:pdf},
  keywords       = {and phrases,c 2006 american mathematical,eigenvalues
                   of random matrices,grant dms-0314286,hypergeometric
                   function of a,in part by nsf,jack function,matrix
                   argument,polynomial,society,this work was
                   supported,zonal},
  year           = 2006
}

@conference{corouge2004towards,
  author         = {Corouge, I. and Gouttard, S. and Gerig, G.},
  booktitle      = {International Symposium on Biomedical Imaging},
  organization   = {Citeseer},
  pages          = {344--347},
  title          = {{Towards a shape model of white matter fiber bundles
                   using diffusion tensor MRI}},
  year           = 2004
}

@Article{Koles1991a,
  Author         = {Koles, Z J},
  Title          = {{The quantitative extraction and topographic mapping
                   of the abnormal components in the clinical EEG.}},
  Journal        = {Electroencephalography and clinical neurophysiology},
  Volume         = {79},
  Number         = {6},
  Pages          = {440--7},
  abstract       = {A method is described which seems to be effective for
                   extracting the abnormal components from the clinical
                   EEG. The approach involves the use of a set a spatial
                   patterns which are common to recorded and 'normal' EEGs
                   and which can account for maximally different
                   proportions of the combined variances in both EEGs.
                   These spatial factors are used to decompose the EEG
                   into orthogonal temporal wave forms which can be judged
                   by the expert electroencephalographer to be abnormal,
                   normal or of artifactual origin. The original EEG is
                   then reconstructed using only the abnormal components
                   and principal component analysis is used to present the
                   spatial topography of the abnormal components. The
                   effectiveness of the method is discussed along with its
                   value for localization of abnormal sources. It is
                   suggested, in conclusion, that the approach described
                   may be optimal for interpretation of the clinical EEG
                   since it allows what is best in terms of quantitative
                   analysis of the EEG to be combined with the best that
                   is available in terms of expert qualitative analysis.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Koles - 1991 - The quantitative
                   extraction and topographic mapping of the abnormal
                   components in the clinical EEG..pdf:pdf},
  issn           = {0013-4694},
  keywords       = {Brain,Brain Mapping,Brain:
                   physiology,Electroencephalography,Electroencephalography:
                   methods,Humans,Signal Processing, Computer-Assisted},
  month          = dec,
  pmid           = {1721571},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/1721571},
  year           = 1991
}

@Article{Kim,
  Author         = {Kim, Min-soo},
  Title          = {{A Particle-and-Density Based Evolutionary Clustering
                   Method for Dynamic Networks}},
  Number         = {1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kim - Unknown - A
                   Particle-and-Density Based Evolutionary Clustering
                   Method for Dynamic Networks.pdf:pdf}
}

@Article{Marinucci2008,
  Author         = {Marinucci, D and Pietrobon, D and Balbi, A and Baldi,
                   P and Cabella, P and Kerkyacharian, G and Natoli, P and
                   Picard, D and Vittorio, N},
  Title          = {{Spherical Needlets for CMB Data Analysis}},
  Volume         = {000},
  Number         = {February},
  arxivid        = {arXiv:0707.0844v1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Marinucci et al. - 2008 - Spherical
                   Needlets for CMB Data Analysis.pdf:pdf},
  year           = 2008
}

@Article{ODonnell_IEEETMI07,
  Author         = {O'Donnell, L. J. and Westin, C. F.},
  Title          = {Automatic tractography segmentation using a
                   high-dimensional white matter atlas.},
  Journal        = {IEEE Trans Med Imaging},
  Volume         = {26},
  Number         = {11},
  Pages          = {1562-75},
  abstract       = {We propose a new white matter atlas creation method
                   that learns a model of the common white matter
                   structures present in a group of subjects. We
                   demonstrate that our atlas creation method, which is
                   based on group spectral clustering of tractography,
                   discovers structures corresponding to expected white
                   matter anatomy such as the corpus callosum, uncinate
                   fasciculus, cingulum bundles, arcuate fasciculus, and
                   corona radiata. The white matter clusters are augmented
                   with expert anatomical labels and stored in a new type
                   of atlas that we call a high-dimensional white matter
                   atlas. We then show how to perform automatic
                   segmentation of tractography from novel subjects by
                   extending the spectral clustering solution, stored in
                   the atlas, using the Nystrom method. We present results
                   regarding the stability of our method and parameter
                   choices. Finally we give results from an atlas creation
                   and automatic segmentation experiment. We demonstrate
                   that our automatic tractography segmentation identifies
                   corresponding white matter regions across hemispheres
                   and across subjects, enabling group comparison of white
                   matter anatomy.},
  authoraddress  = {Golby Laboratory, Department of Neurosurgery, Brigham
                   and Women's Hospital, Harvard Medical School, Boston,
                   MA 02115, USA. lauren@csail.mit.edu},
  keywords       = {Algorithms ; Artificial Intelligence ; Computer
                   Simulation ; Corpus Callosum/*anatomy \& histology ;
                   Diffusion Magnetic Resonance Imaging/*methods ; Humans
                   ; Image Enhancement/*methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/*methods ; Models, Anatomic ; Models,
                   Neurological ; Nerve Fibers, Myelinated/*ultrastructure
                   ; Pattern Recognition, Automated/*methods ;
                   Reproducibility of Results ; Sensitivity and
                   Specificity ; Subtraction Technique},
  language       = {eng},
  medline-aid    = {10.1109/TMI.2007.906785 [doi]},
  medline-crdt   = {2007/11/29 09:00},
  medline-da     = {20071128},
  medline-dcom   = {20080122},
  medline-edat   = {2007/11/29 09:00},
  medline-fau    = {O'Donnell, Lauren J ; Westin, Carl-Fredrik},
  medline-gr     = {P41-RR13218/RR/NCRR NIH HHS/United States ;
                   P41-RR15241/RR/NCRR NIH HHS/United States ;
                   R01-AG20012/AG/NIA NIH HHS/United States ;
                   R01-MH074794/MH/NIMH NIH HHS/United States ;
                   U24-RR021382/RR/NCRR NIH HHS/United States ;
                   U41-RR019703/RR/NCRR NIH HHS/United States},
  medline-is     = {0278-0062 (Print)},
  medline-jid    = {8310780},
  medline-jt     = {IEEE transactions on medical imaging},
  medline-mhda   = {2008/01/23 09:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {18041271},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural},
  medline-sb     = {IM},
  medline-so     = {IEEE Trans Med Imaging. 2007 Nov;26(11):1562-75.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18041271},
  year           = 2007
}

@Article{Bihan2001,
  Author         = {Bihan, MD Denis Le and Mangin, JF and Poupon, C},
  Title          = {{Diffusion tensor imaging: concepts and applications}},
  Journal        = {Journal of Magnetic \ldots},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Bihan, Mangin, Poupon - 2001 -
                   Diffusion tensor imaging concepts and
                   applications.pdf:pdf},
  url            = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.9156\&rep=rep1\&type=pdf},
  year           = 2001
}

@Article{LWT+03,
  Author         = {Lazar, M. and Weinstein, D. M. and Tsuruda, J. S. and
                   Hasan, K. M. and Arfanakis, K. and Meyerand, M. E. and
                   Badie, B. and Rowley, H. A. and Haughton, V. and Field,
                   A. and Alexander, A. L.},
  Title          = {White matter tractography using diffusion tensor
                   deflection.},
  Journal        = {Hum Brain Mapp},
  Volume         = {18},
  Number         = {4},
  Pages          = {306-21},
  abstract       = {Diffusion tensor MRI provides unique directional
                   diffusion information that can be used to estimate the
                   patterns of white matter connectivity in the human
                   brain. In this study, the behavior of an algorithm for
                   white matter tractography is examined. The algorithm,
                   called TEND, uses the entire diffusion tensor to
                   deflect the estimated fiber trajectory. Simulations and
                   imaging experiments on in vivo human brains were
                   performed to investigate the behavior of the
                   tractography algorithm. The simulations show that the
                   deflection term is less sensitive than the major
                   eigenvector to image noise. In the human brain imaging
                   experiments, estimated tracts were generated in corpus
                   callosum, corticospinal tract, internal capsule, corona
                   radiata, superior longitudinal fasciculus, inferior
                   longitudinal fasciculus, fronto-occipital fasciculus,
                   and uncinate fasciculus. This approach is promising for
                   mapping the organizational patterns of white matter in
                   the human brain as well as mapping the relationship
                   between major fiber trajectories and the location and
                   extent of brain lesions.},
  authoraddress  = {Department of Physics, University of Utah, Salt Lake
                   City, Utah, USA.},
  keywords       = {Algorithms ; Brain Mapping/*methods ; Corpus
                   Callosum/physiology ; Humans ; Nerve Fibers,
                   Myelinated/*physiology ; Neural Pathways/physiology ;
                   Pyramidal Tracts/physiology},
  language       = {eng},
  medline-aid    = {10.1002/hbm.10102 [doi]},
  medline-ci     = {Copyright 2003 Wiley-Liss, Inc.},
  medline-crdt   = {2003/03/13 04:00},
  medline-da     = {20030312},
  medline-dcom   = {20030530},
  medline-edat   = {2003/03/13 04:00},
  medline-fau    = {Lazar, Mariana ; Weinstein, David M ; Tsuruda, Jay S ;
                   Hasan, Khader M ; Arfanakis, Konstantinos ; Meyerand, M
                   Elizabeth ; Badie, Benham ; Rowley, Howard A ;
                   Haughton, Victor ; Field, Aaron ; Alexander, Andrew L},
  medline-gr     = {MH62015/MH/NIMH NIH HHS/United States ; P30
                   CA42014/CA/NCI NIH HHS/United States},
  medline-is     = {1065-9471 (Print)},
  medline-jid    = {9419065},
  medline-jt     = {Human brain mapping},
  medline-lr     = {20071114},
  medline-mhda   = {2003/05/31 05:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {12632468},
  medline-pst    = {ppublish},
  medline-pt     = {Comparative Study ; Journal Article ; Research
                   Support, U.S. Gov't, P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Hum Brain Mapp. 2003 Apr;18(4):306-21.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12632468},
  year           = 2003
}

@Article{Heil,
  Author         = {Heil, Christopher},
  Title          = {{No Title}},
  Journal        = {Proofs},
  Number         = {1},
  Pages          = {2--5},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Heil - Unknown - No Title.pdf:pdf}
}

@Article{Rules2004,
  Author         = {Rules, Association},
  Title          = {{Outline of the Course 1 . Introduction and
                   Terminology 2 . Data Warehousing ( sketch ) Statement
                   of the Problem}},
  Pages          = {155--187},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Rules - 2004 - Outline of the Course
                   1 . Introduction and Terminology 2 . Data Warehousing (
                   sketch ) Statement of the Problem.pdf:pdf},
  year           = 2004
}

@Article{Papadakis1999,
  Author         = {Papadakis, NG and Xing, D and Houston, GC and Smith,
                   JM},
  Title          = {{A study of rotationally invariant and symmetric
                   indices of diffusion anisotropy}},
  Journal        = {Magnetic resonance \ldots},
  url            = {http://linkinghub.elsevier.com/retrieve/pii/S0730725X99000296},
  year           = 1999
}

@Article{Drepper2007,
  Author         = {Drepper, Ulrich and Hat, Red},
  Title          = {{What Every Programmer Should Know About Memory}},
  Journal        = {Changes},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Drepper, Hat - 2007 - What Every
                   Programmer Should Know About Memory.pdf:pdf},
  year           = 2007
}

@Article{WWS+08,
  Author         = {Wedeen, V. J. and Wang, R. P. and Schmahmann, J. D.
                   and Benner, T. and Tseng, W. Y. and Dai, G. and Pandya,
                   D. N. and Hagmann, P. and D'Arceuil, H. and de
                   Crespigny, A. J.},
  Title          = {Diffusion spectrum magnetic resonance imaging ({DSI})
                   tractography of crossing fibers.},
  Journal        = {Neuroimage},
  Volume         = {41},
  Number         = {4},
  Pages          = {1267-77},
  abstract       = {MRI tractography is the mapping of neural fiber
                   pathways based on diffusion MRI of tissue diffusion
                   anisotropy. Tractography based on diffusion tensor
                   imaging (DTI) cannot directly image multiple fiber
                   orientations within a single voxel. To address this
                   limitation, diffusion spectrum MRI (DSI) and related
                   methods were developed to image complex distributions
                   of intravoxel fiber orientation. Here we demonstrate
                   that tractography based on DSI has the capacity to
                   image crossing fibers in neural tissue. DSI was
                   performed in formalin-fixed brains of adult macaque and
                   in the brains of healthy human subjects. Fiber tract
                   solutions were constructed by a streamline procedure,
                   following directions of maximum diffusion at every
                   point, and analyzed in an interactive visualization
                   environment (TrackVis). We report that DSI tractography
                   accurately shows the known anatomic fiber crossings in
                   optic chiasm, centrum semiovale, and brainstem; fiber
                   intersections in gray matter, including cerebellar
                   folia and the caudate nucleus; and radial fiber
                   architecture in cerebral cortex. In contrast, none of
                   these examples of fiber crossing and complex structure
                   was identified by DTI analysis of the same data sets.
                   These findings indicate that DSI tractography is able
                   to image crossing fibers in neural tissue, an essential
                   step toward non-invasive imaging of connectional
                   neuroanatomy.},
  authoraddress  = {Department of Radiology, MGH Martinos Center for
                   Biomedical Imaging, Harvard Medical School,
                   Charlestown, MA 02129, USA. van@nmr.mgh.harvard.edu},
  keywords       = {Adult ; Algorithms ; Animals ; Brain/anatomy \&
                   histology ; Diffusion Magnetic Resonance
                   Imaging/*methods ; Female ; Humans ; Image Processing,
                   Computer-Assisted/methods ; Macaca fascicularis ; Male
                   ; Middle Aged ; Nerve Fibers/*physiology ; Neural
                   Pathways/*anatomy \& histology/*physiology},
  language       = {eng},
  medline-aid    = {S1053-8119(08)00253-X [pii] ;
                   10.1016/j.neuroimage.2008.03.036 [doi]},
  medline-crdt   = {2008/05/23 09:00},
  medline-da     = {20080616},
  medline-dcom   = {20080829},
  medline-dep    = {20080408},
  medline-edat   = {2008/05/23 09:00},
  medline-fau    = {Wedeen, V J ; Wang, R P ; Schmahmann, J D ; Benner, T
                   ; Tseng, W Y I ; Dai, G ; Pandya, D N ; Hagmann, P ;
                   D'Arceuil, H ; de Crespigny, A J},
  medline-gr     = {1R01 MH 64044/MH/NIMH NIH HHS/United States ; 1R01
                   MH67980/MH/NIMH NIH HHS/United States ;
                   1R01EB00790/EB/NIBIB NIH HHS/United States ;
                   1R01NS401285/NS/NINDS NIH HHS/United States ;
                   1S10RR016811-01/RR/NCRR NIH HHS/United States ;
                   P41RR14075/RR/NCRR NIH HHS/United States},
  medline-is     = {1053-8119 (Print)},
  medline-jid    = {9215515},
  medline-jt     = {NeuroImage},
  medline-mhda   = {2008/08/30 09:00},
  medline-own    = {NLM},
  medline-phst   = {2007/11/30 [received] ; 2008/03/14 [revised] ;
                   2008/03/17 [accepted] ; 2008/04/08 [aheadofprint]},
  medline-pl     = {United States},
  medline-pmid   = {18495497},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural
                   ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {Neuroimage. 2008 Jul 15;41(4):1267-77. Epub 2008 Apr
                   8.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18495497},
  year           = 2008
}

@Article{Indyk2003,
  Author         = {Indyk, Piotr and Venkatasubramanian, Suresh},
  Title          = {{Approximate congruence in nearly linear time}},
  Journal        = {Computational Geometry},
  Volume         = {24},
  Pages          = {115--128},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Indyk, Venkatasubramanian - 2003 -
                   Approximate congruence in nearly linear time.pdf:pdf},
  keywords       = {bottleneck distance,computational geometry,hall,metric
                   entropy,pattern matching,point set matching,s},
  year           = 2003
}

@Article{Arsigny2009,
  Author         = {Arsigny, Vincent and Commowick, Olivier and Ayache,
                   Nicholas and Pennec, Xavier},
  Title          = {{A Fast and Log-Euclidean Polyaffine Framework for
                   Locally Linear Registration}},
  Journal        = {Journal of Mathematical Imaging and Vision},
  Volume         = {33},
  Number         = {2},
  Pages          = {222--238},
  doi            = {10.1007/s10851-008-0135-9},
  file           = {::},
  issn           = {0924-9907},
  keywords       = {arsigny,ayache,commowick,diffeomorphisms,ing,locally
                   affine transformations,log-euclidean,medical
                   imag-,n,non-rigid registration,o,ode,pennec,polyaffine
                   transformations,v,x},
  month          = jan,
  url            = {http://www.springerlink.com/index/10.1007/s10851-008-0135-9},
  year           = 2009
}

@Article{Close2009,
  Author         = {Close, Thomas G and Tournier, Jacques-Donald and
                   Calamante, Fernando and Johnston, Leigh a and Mareels,
                   Iven and Connelly, Alan},
  Title          = {{A software tool to generate simulated white matter
                   structures for the assessment of fibre-tracking
                   algorithms.}},
  Journal        = {NeuroImage},
  Volume         = {47},
  Number         = {4},
  Pages          = {1288--300},
  abstract       = {The assessment of Diffusion-Weighted MRI (DW-MRI)
                   fibre-tracking algorithms has been limited by the lack
                   of an appropriate 'gold standard'. Practical
                   limitations of alternative methods and physical models
                   have meant that numerical simulations have become the
                   method of choice in practice. However, previous
                   numerical phantoms have consisted of separate fibres
                   embedded in homogeneous backgrounds, which do not
                   capture the true nature of white matter. In this paper
                   we describe a method that is able to randomly generate
                   numerical structures consisting of densely packed
                   bundles of fibres, which are much more representative
                   of human white matter, and simulate the DW-MR images
                   that would arise from them under many imaging
                   conditions. User-defined parameters may be adjusted to
                   produce structures with a range of complexities that
                   spans the levels we would expect to find in vivo. These
                   structures are shown to contain many different features
                   that occur in human white matter and which could
                   confound fibre-tracking algorithms, such as tract
                   kissing and crossing. Furthermore, combinations of such
                   features can be sampled by the random generation of
                   many different structures with consistent levels of
                   complexity. The proposed software provides means for
                   quantitative assessment via direct comparison between
                   tracking results and the exact location of the
                   generated fibres. This should greatly improve our
                   understanding of algorithm performance and therefore
                   prove an important tool for fibre tracking development.},
  doi            = {10.1016/j.neuroimage.2009.03.077},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Close et al. - 2009 - A software
                   tool to generate simulated white matter structures for
                   the assessment of fibre-tracking algorithms..pdf:pdf},
  issn           = {1095-9572},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Computer
                   Simulation,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Magnetic
                   Resonance Imaging,Magnetic Resonance Imaging:
                   methods,Models, Anatomic,Models, Neurological,Nerve
                   Fibers, Myelinated,Nerve Fibers, Myelinated:
                   ultrastructure,Pattern Recognition, Automated,Pattern
                   Recognition, Automated: methods,Reproducibility of
                   Results,Sensitivity and Specificity,Software},
  pmid           = {19361565},
  publisher      = {Elsevier Inc.},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19361565},
  year           = 2009
}

@Misc{TheMendeleySupportTeam2010,
  Author         = {{The Mendeley Support Team}},
  Title          = {{Getting Started with Mendeley}},
  abstract       = {A quick introduction to Mendeley. Learn how Mendeley
                   creates your personal digital library, how to organize
                   and annotate documents, how to collaborate and share
                   with colleagues, and how to generate citations and
                   bibliographies.},
  address        = {London},
  booktitle      = {Mendeley Desktop},
  file           = {:usr/share/doc/mendeleydesktop/FAQ.pdf:pdf},
  keywords       = {Mendeley,how-to,user manual},
  pages          = {1--14},
  publisher      = {Mendeley Ltd.},
  url            = {http://www.mendeley.com},
  year           = 2010
}

@conference{pickalov2006tra,
  author         = {Pickalov, V. and Basser, P.J.},
  booktitle      = {3rd IEEE International Symposium on Biomedical
                   Imaging: Nano to Macro, 2006},
  pages          = {710--713},
  title          = {{3d tomographic reconstruction of the average
                   propagator from mri data}},
  year           = 2006
}

@Article{PCC+08,
  Author         = {Perrin, M. and Cointepas, Y. and Cachia, A. and
                   Poupon, C. and Thirion, B. and Riviere, D. and Cathier,
                   P. and El Kouby, V. and Constantinesco, A. and Le
                   Bihan, D. and Mangin, J. F.},
  Title          = {Connectivity-{B}ased {P}arcellation of the {C}ortical
                   {M}antle {U}sing q-{B}all {D}iffusion {I}maging.},
  Journal        = {Int J Biomed Imaging},
  Volume         = {2008},
  Pages          = {368406},
  abstract       = {This paper exploits the idea that each individual
                   brain region has a specific connection profile to
                   create parcellations of the cortical mantle using MR
                   diffusion imaging. The parcellation is performed in two
                   steps. First, the cortical mantle is split at a
                   macroscopic level into 36 large gyri using a sulcus
                   recognition system. Then, for each voxel of the cortex,
                   a connection profile is computed using a probabilistic
                   tractography framework. The tractography is performed
                   from q fields using regularized particle trajectories.
                   Fiber ODF are inferred from the q-balls using a
                   sharpening process focusing the weight around the
                   q-ball local maxima. A sophisticated mask of
                   propagation computed from a T1-weighted image perfectly
                   aligned with the diffusion data prevents the particles
                   from crossing the cortical folds. During propagation,
                   the particles father child particles in order to
                   improve the sampling of the long fascicles. For each
                   voxel, intersection of the particle trajectories with
                   the gyri lead to a connectivity profile made up of only
                   36 connection strengths. These profiles are clustered
                   on a gyrus by gyrus basis using a K-means approach
                   including spatial regularization. The reproducibility
                   of the results is studied for three subjects using
                   spatial normalization.},
  authoraddress  = {NeuroSpin Institut d'Imagerie BioMedicale,
                   Commissariat l'Energie Atomique (CEA), Gif-sur-Yvette
                   91191, France.},
  language       = {eng},
  medline-aid    = {10.1155/2008/368406 [doi]},
  medline-crdt   = {2008/04/11 09:00},
  medline-da     = {20080410},
  medline-edat   = {2008/04/11 09:00},
  medline-fau    = {Perrin, Muriel ; Cointepas, Yann ; Cachia, Arnaud ;
                   Poupon, Cyril ; Thirion, Bertrand ; Riviere, Denis ;
                   Cathier, Pascal ; El Kouby, Vincent ; Constantinesco,
                   Andre ; Le Bihan, Denis ; Mangin, Jean-Francois},
  medline-is     = {1687-4188 (Print)},
  medline-jid    = {101250756},
  medline-jt     = {International journal of biomedical imaging},
  medline-mhda   = {2008/04/11 09:00},
  medline-oid    = {NLM: PMC2288697},
  medline-own    = {NLM},
  medline-phst   = {2007/09/01 [received] ; 2007/11/30 [revised] ;
                   2007/12/16 [accepted]},
  medline-pl     = {United States},
  medline-pmc    = {PMC2288697},
  medline-pmid   = {18401457},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article},
  medline-so     = {Int J Biomed Imaging. 2008;2008:368406.},
  medline-stat   = {In-Data-Review},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18401457},
  year           = 2008
}

@Article{Tsiaras2009,
  Author         = {Tsiaras, Vassilis L},
  Title          = {{Algorithms for the Analysis and Visualization of
                   Biomedical Networks}},
  Journal        = {October},
  Number         = {October},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tsiaras - 2009 - Algorithms for the
                   Analysis and Visualization of Biomedical
                   Networks.pdf:pdf},
  year           = 2009
}

@Article{Maaten2008,
  Author         = {Maaten, L and Hinton, G},
  Title          = {{Visualizing data using t-sne}},
  Journal        = {Journal of Machine Learning Research},
  url            = {http://scholar.google.co.uk/scholar?q=hinton
                   t-sne\&oe=utf-8\&rls=com.ubuntu:en-GB:official\&client=firefox-a\&um=1\&ie=UTF-8\&sa=N\&hl=en\&tab=ws\#2},
  year           = 2008
}

@Article{NedjatiGilani2008ISMRM,
  Author         = {Nedjati-Gilani, S. and Parker, G. J. and Alexander, D.
                   C.},
  Title          = {Regularized super-resolution for diffusion \{{M}{RI}\}},
  Journal        = {Proc. Intl. Soc. Mag. Reson. Med.},
  Volume         = {16},
  Pages          = {41},
  abstract       = {We present a new regularized super-resolution method,
                   which finds fibre orientations and volume fractions on
                   a sub-voxel scale and helps distinguish various fibre
                   configurations such as fanning, bending and partial
                   volume effects. We treat the task as a general inverse
                   problem, which we solve by regularization and
                   optimization, and run our method on human brain data.},
  file           = {attachment\:NedjatiGilani2008ISMRM.pdf:attachment\:NedjatiGilani2008ISMRM.pdf:PDF},
  year           = 2008
}

@Article{Jones1999,
  Author         = {Jones, D K and Horsfield, M a and Simmons, a},
  Title          = {{Optimal strategies for measuring diffusion in
                   anisotropic systems by magnetic resonance imaging.}},
  Journal        = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  Volume         = {42},
  Number         = {3},
  Pages          = {515--25},
  abstract       = {The optimization of acquisition parameters for precise
                   measurement of diffusion in anisotropic systems is
                   described. First, an algorithm is presented that
                   minimizes the bias inherent in making measurements with
                   a fixed set of gradient vector directions by spreading
                   out measurements in 3-dimensional gradient vector
                   space. Next, it is shown how the set of b-matrices and
                   echo time can be optimized for estimating the diffusion
                   tensor and its scalar invariants. The standard
                   deviation in the estimate of the tensor trace in a
                   water phantom was reduced by more than 40\% and the
                   artefactual anisotropy was reduced by more than 60\%
                   when using the optimized scheme compared with a more
                   conventional scheme for the same scan time, and marked
                   improvements are demonstrated in the human brain with
                   the optimized sequences. Use of these optimal schemes
                   results in reduced scan times, increased precision, or
                   improved resolution in diffusion tensor images. Magn
                   Reson Med 42:515-525, 1999.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Jones, Horsfield, Simmons - 1999 -
                   Optimal strategies for measuring diffusion in
                   anisotropic systems by magnetic resonance
                   imaging..pdf:pdf},
  issn           = {0740-3194},
  keywords       = {Adult,Algorithms,Anisotropy,Brain,Brain: anatomy \&
                   histology,Diffusion,Humans,Linear Models,Magnetic
                   Resonance Imaging,Magnetic Resonance Imaging:
                   methods,Models, Structural,Phantoms, Imaging,Water},
  month          = sep,
  pmid           = {10467296},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/10467296},
  year           = 1999
}

@Article{Szeliski2006,
  Author         = {Szeliski, Richard},
  Title          = {{Image Alignment and Stitching: A Tutorial}},
  Journal        = {Foundations and Trends® in Computer Graphics and
                   Vision},
  Volume         = {2},
  Number         = {1},
  Pages          = {1--104},
  doi            = {10.1561/0600000009},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Szeliski - 2006 - Image Alignment
                   and Stitching A Tutorial.pdf:pdf},
  issn           = {1572-2740},
  url            = {http://www.nowpublishers.com/product.aspx?product=CGV\&doi=0600000009},
  year           = 2006
}

@Article{Maddah_IEEEBI2008,
  Author         = {Maddah, M. and Zollei, L. and Grimson, W. E. and
                   Westin, C. F. and Wells, W. M.},
  Title          = {A {M}athematical {F}ramework for {I}ncorporating
                   {A}natomical {K}nowledge in {DT}-{MRI} {A}nalysis.},
  Journal        = {Proc IEEE Int Symp Biomed Imaging},
  Volume         = {4543943},
  Pages          = {105-108},
  abstract       = {We propose a Bayesian approach to incorporate
                   anatomical information in the clustering of fiber
                   trajectories. An expectation-maximization (EM)
                   algorithm is used to cluster the trajectories, in which
                   an atlas serves as the prior on the labels. The atlas
                   guides the clustering algorithm and makes the resulting
                   bundles anatomically meaningful. In addition, it
                   provides the seed points for the tractography and
                   initial settings of the EM algorithm. The proposed
                   approach provides a robust and automated tool for
                   tract-oriented analysis both in a single subject and
                   over a population.},
  authoraddress  = {Computer Science and Artificial Intelligence
                   Laboratory, Massachusetts Institute of Technology,
                   Cambridge, MA 02139, USA.},
  language       = {ENG},
  medline-aid    = {10.1109/ISBI.2008.4540943 [doi]},
  medline-crdt   = {2009/02/13 09:00},
  medline-da     = {20090305},
  medline-edat   = {2009/02/13 09:00},
  medline-gr     = {P41 RR013218-09/NCRR NIH HHS/United States ; R01
                   MH074794-02/NIMH NIH HHS/United States ; R01
                   NS051826-04/NINDS NIH HHS/United States ; U41
                   RR019703-03/NCRR NIH HHS/United States ; U54
                   EB005149-04/NIBIB NIH HHS/United States},
  medline-is     = {1945-7928 (Print)},
  medline-jid    = {101492570},
  medline-jt     = {Proceedings / IEEE International Symposium on
                   Biomedical Imaging: from nano to macro. IEEE
                   International Symposium on Biomedical Imaging},
  medline-mhda   = {2009/02/13 09:00},
  medline-mid    = {NIHMS88086},
  medline-own    = {NLM},
  medline-pmc    = {PMC2638065},
  medline-pmid   = {19212449},
  medline-pst    = {ppublish},
  medline-pt     = {JOURNAL ARTICLE},
  medline-so     = {Proc IEEE Int Symp Biomed Imaging.
                   2008;4543943:105-108.},
  medline-stat   = {Publisher},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=19212449},
  year           = 2008
}

@Article{Santana2010,
  Author         = {Santana, Roberto and Bielza, Concha and Larra, Pedro},
  Title          = {{Classification of MEG data using a combined machine
                   learning approach Problem definition}},
  Journal        = {Challenge},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Santana, Bielza, Larra - 2010 -
                   Classification of MEG data using a combined machine
                   learning approach Problem definition.pdf:pdf},
  year           = 2010
}

@Article{Kohn2009,
  Author         = {K\"{o}hn, Alexander and Klein, Jan and Weiler, Florian
                   and Peitgen, Heinz-Otto},
  Title          = {{A GPU-based fiber tracking framework using geometry
                   shaders}},
  Journal        = {Proceedings of SPIE},
  Pages          = {72611J--72611J--10},
  doi            = {10.1117/12.812219},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/K\"{o}hn et al. - 2009 - A GPU-based
                   fiber tracking framework using geometry shaders.pdf:pdf},
  keywords       = {diffusion tensor imaging,fiber
                   tracking,gpu,visualization},
  publisher      = {Spie},
  url            = {http://link.aip.org/link/PSISDG/v7261/i1/p72611J/s1\&Agg=doi},
  year           = 2009
}

@Misc{okada2006dtf,
  Author         = {Okada, T. and Miki, Y. and Fushimi, Y. and Hanakawa,
                   T. and Kanagaki, M. and Yamamoto, A. and Urayama, S.
                   and Fukuyama, H. and Hiraoka, M. and Togashi, K.},
  Title          = {{Diffusion-Tensor Fiber Tractography: Intraindividual
                   Comparison of 3.0-T and 1.5-T MR Imaging 1}},
  journal        = {Radiology},
  number         = {2},
  pages          = {668--678},
  publisher      = {RSNA},
  volume         = {238},
  year           = 2006
}

@Article{Mittmann2010,
  Author         = {Mittmann, Adiel and Nobrega, Tiago H C and Comunello,
                   Eros and Pinto, Juliano P O and Dellani, Paulo R and
                   Stoeter, Peter and von Wangenheim, Aldo},
  Title          = {{Performing Real-Time Interactive Fiber Tracking.}},
  Journal        = {Journal of digital imaging : the official journal of
                   the Society for Computer Applications in Radiology},
  abstract       = {Fiber tracking is a technique that, based on a
                   diffusion tensor magnetic resonance imaging dataset,
                   locates the fiber bundles in the human brain. Because
                   it is a computationally expensive process, the
                   interactivity of current fiber tracking tools is
                   limited. We propose a new approach, which we termed
                   real-time interactive fiber tracking, which aims at
                   providing a rich and intuitive environment for the
                   neuroradiologist. In this approach, fiber tracking is
                   executed automatically every time the user acts upon
                   the application. Particularly, when the volume of
                   interest from which fiber trajectories are calculated
                   is moved on the screen, fiber tracking is executed,
                   even while it is being moved. We present our fiber
                   tracking tool, which implements the real-time fiber
                   tracking concept by using the video card's graphics
                   processing units to execute the fiber tracking
                   algorithm. Results show that real-time interactive
                   fiber tracking is feasible on computers equipped with
                   common, low-cost video cards.},
  doi            = {10.1007/s10278-009-9266-9},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mittmann et al. - 2010 - Performing
                   Real-Time Interactive Fiber Tracking..pdf:pdf},
  issn           = {1618-727X},
  keywords       = {11 which finds,diffusion tensor imaging,fiber
                   tracking,fiber trajectories by following,graphics
                   processing units,of them being the,real-time
                   applications,streamline method,the main diffusion},
  month          = feb,
  pmid           = {20155382},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/20155382},
  year           = 2010
}

@Article{Kindlmann2007,
  Author         = {Kindlmann, Gordon and Tricoche, Xavier and Westin,
                   Carl-Fredrik},
  Title          = {{Delineating white matter structure in diffusion
                   tensor MRI with anisotropy creases.}},
  Journal        = {Medical image analysis},
  Volume         = {11},
  Number         = {5},
  Pages          = {492--502},
  abstract       = {Geometric models of white matter architecture play an
                   increasing role in neuroscientific applications of
                   diffusion tensor imaging, and the most popular method
                   for building them is fiber tractography. For some
                   analysis tasks, however, a compelling alternative may
                   be found in the first and second derivatives of
                   diffusion anisotropy. We extend to tensor fields the
                   notion from classical computer vision of ridges and
                   valleys, and define anisotropy creases as features of
                   locally extremal tensor anisotropy. Mathematically,
                   these are the loci where the gradient of anisotropy is
                   orthogonal to one or more eigenvectors of its Hessian.
                   We propose that anisotropy creases provide a basis for
                   extracting a skeleton of the major white matter
                   pathways, in that ridges of anisotropy coincide with
                   interiors of fiber tracts, and valleys of anisotropy
                   coincide with the interfaces between adjacent but
                   distinctly oriented tracts. The crease extraction
                   algorithm we present generates high-quality polygonal
                   models of crease surfaces, which are further simplified
                   by connected-component analysis. We demonstrate
                   anisotropy creases on measured diffusion MRI data, and
                   visualize them in combination with tractography to
                   confirm their anatomic relevance.},
  doi            = {10.1016/j.media.2007.07.005},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kindlmann, Tricoche, Westin - 2007 -
                   Delineating white matter structure in diffusion tensor
                   MRI with anisotropy creases..pdf:pdf},
  issn           = {1361-8415},
  keywords       = {Algorithms,Anisotropy,Artificial
                   Intelligence,Brain,Brain: cytology,Cluster
                   Analysis,Diffusion Magnetic Resonance Imaging,Diffusion
                   Magnetic Resonance Imaging: methods,Humans,Image
                   Enhancement,Image Enhancement: methods,Image
                   Interpretation, Computer-Assisted,Image Interpretation,
                   Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Nerve Fibers, Myelinated,Nerve Fibers,
                   Myelinated: ultrastructure,Neural Pathways,Neural
                   Pathways: cytology,Pattern Recognition,
                   Automated,Pattern Recognition, Automated:
                   methods,Reproducibility of Results,Sensitivity and
                   Specificity},
  pmid           = {17804278},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/17804278},
  year           = 2007
}

@Article{Koles1991,
  Author         = {Koles, Z J},
  Title          = {{The quantitative extraction and topographic mapping
                   of the abnormal components in the clinical EEG.}},
  Journal        = {Electroencephalography and clinical neurophysiology},
  Volume         = {79},
  Number         = {6},
  Pages          = {440--7},
  abstract       = {A method is described which seems to be effective for
                   extracting the abnormal components from the clinical
                   EEG. The approach involves the use of a set a spatial
                   patterns which are common to recorded and 'normal' EEGs
                   and which can account for maximally different
                   proportions of the combined variances in both EEGs.
                   These spatial factors are used to decompose the EEG
                   into orthogonal temporal wave forms which can be judged
                   by the expert electroencephalographer to be abnormal,
                   normal or of artifactual origin. The original EEG is
                   then reconstructed using only the abnormal components
                   and principal component analysis is used to present the
                   spatial topography of the abnormal components. The
                   effectiveness of the method is discussed along with its
                   value for localization of abnormal sources. It is
                   suggested, in conclusion, that the approach described
                   may be optimal for interpretation of the clinical EEG
                   since it allows what is best in terms of quantitative
                   analysis of the EEG to be combined with the best that
                   is available in terms of expert qualitative analysis.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Koles - 1991 - The quantitative
                   extraction and topographic mapping of the abnormal
                   components in the clinical EEG..pdf:pdf},
  issn           = {0013-4694},
  keywords       = {Brain,Brain Mapping,Brain:
                   physiology,Electroencephalography,Electroencephalography:
                   methods,Humans,Signal Processing, Computer-Assisted},
  month          = dec,
  pmid           = {1721571},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/1721571},
  year           = 1991
}

@Article{andersson2002mbm,
  Author         = {Andersson, J.L.R. and Skare, S.},
  Title          = {{A model-based method for retrospective correction of
                   geometric distortions in diffusion-weighted EPI}},
  Journal        = {Neuroimage},
  Volume         = {16},
  Number         = {1},
  Pages          = {177--199},
  publisher      = {Elsevier Inc.},
  year           = 2002
}

@Article{behrens2005rca,
  Author         = {Behrens, T E and Johansen-Berg, H.},
  Title          = {{Relating connectional architecture to grey matter
                   function using diffusion imaging.}},
  Journal        = {Philos Trans R Soc Lond B Biol Sci},
  Volume         = {360},
  Number         = {1457},
  Pages          = {903--11},
  file           = {attachment\:behrens_dti_connectivity_function_2005.pdf:attachment\:behrens_dti_connectivity_function_2005.pdf:PDF},
  year           = 2005
}

@Article{Hall2009,
  Author         = {Hall, Matt G and Alexander, Daniel C},
  Title          = {{Convergence and parameter choice for Monte-Carlo
                   simulations of diffusion MRI.}},
  Journal        = {IEEE transactions on medical imaging},
  Volume         = {28},
  Number         = {9},
  Pages          = {1354--64},
  abstract       = {This paper describes a general and flexible Monte-
                   Carlo simulation framework for diffusing spins that
                   generates realistic synthetic data for diffusion
                   magnetic resonance imaging. Similar systems in the
                   literature consider only simple substrates and their
                   authors do not consider convergence and parameter
                   optimization. We show how to run Monte-Carlo
                   simulations within complex irregular substrates. We
                   compare the results of the Monte-Carlo simulation to an
                   analytical model of restricted diffusion to assess
                   precision and accuracy of the generated results. We
                   obtain an optimal combination of spins and updates for
                   a given run time by trading off number of updates in
                   favor of number of spins such that precision and
                   accuracy of sythesized data are both optimized. Further
                   experiments demonstrate the system using a tissue
                   environment that current analytic models cannot
                   capture. This tissue model incorporates swelling,
                   abutting, and deformation. Swelling-induced restriction
                   in the extracellular space due to the effects of
                   abutting cylinders leads to large departures from the
                   predictions of the analytical model, which does not
                   capture these effects. This swelling-induced
                   restriction may be an important mechanism in explaining
                   the changes in apparent diffusion constant observed in
                   the aftermath of acute ischemic stroke.},
  doi            = {10.1109/TMI.2009.2015756},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Hall, Alexander - 2009 - Convergence
                   and parameter choice for Monte-Carlo simulations of
                   diffusion MRI..pdf:pdf},
  issn           = {1558-0062},
  keywords       = {Algorithms,Brain Edema,Brain Edema: pathology,Brain
                   Ischemia,Brain Ischemia: pathology,Computer
                   Simulation,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Monte Carlo Method,Reproducibility of
                   Results,Stroke,Stroke: pathology},
  month          = sep,
  pmid           = {19273001},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19273001},
  year           = 2009
}

@Article{Perbet,
  Author         = {Perbet, Frank},
  Title          = {{Correlated Probabilistic Trajectories for Pedestrian
                   Motion Detection}},
  Journal        = {Image (Rochester, N.Y.)},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Perbet - Unknown - Correlated
                   Probabilistic Trajectories for Pedestrian Motion
                   Detection.pdf:pdf}
}

@Article{Vazirani1994,
  Author         = {Vazirani, Vijay V},
  Title          = {{MAXIMUM MATCHING ALGORITHM}},
  Journal        = {Combinatorica},
  Volume         = {14},
  Number         = {i},
  Pages          = {71--109},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Vazirani - 1994 - MAXIMUM MATCHING
                   ALGORITHM.pdf:pdf},
  year           = 1994
}

@Article{boykov2004ecm,
  Author         = {Boykov, Y. and Kolmogorov, V.},
  Title          = {{An experimental comparison of min-cut/max-flow
                   algorithms for energy minimization in vision}},
  Journal        = {IEEE Transactions on Pattern Analysis and Machine
                   Intelligence},
  Volume         = {26},
  Number         = {9},
  Pages          = {1124--1137},
  year           = 2004
}

@Article{Fillard2009a,
  Author         = {Fillard, Pierre and Poupon, Cyril},
  Title          = {{A Novel Global Tractography Algorithm based on an
                   Adaptive Spin Glass Model}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Fillard, Poupon - 2009 - A Novel
                   Global Tractography Algorithm based on an Adaptive Spin
                   Glass Model.pdf:pdf},
  year           = 2009
}

@Article{0266-5611-19-5-303,
  Author         = {Jansons, Kalvis M and Alexander, Daniel C},
  Title          = {Persistent angular structure: new insights from
                   diffusion magnetic resonance imaging data},
  Journal        = {Inverse Problems},
  Volume         = {19},
  Number         = {5},
  Pages          = {1031-1046},
  abstract       = {We determine a statistic called the (radially)
                   persistent angular structure (PAS) from samples of the
                   Fourier transform of a three-dimensional function. The
                   method has applications in diffusion magnetic resonance
                   imaging (MRI), which samples the Fourier transform of
                   the probability density function of particle
                   displacements. The PAS is then a representation of the
                   relative mobility of particles in each direction. In
                   PAS-MRI, we compute the PAS in each voxel of an image.
                   This technique has biomedical applications, where it
                   reveals the orientations of microstructural fibres,
                   such as white-matter fibres in the brain. Scanner time
                   is a significant factor in determining the amount of
                   data available in clinical brain scans. Here, we use
                   measurements acquired for diffusion-tensor MRI, which
                   is a routine diffusion imaging technique, but extract
                   richer information. In particular, PAS-MRI can resolve
                   the orientations of crossing fibres.We test PAS-MRI on
                   human brain data and on synthetic data. The human brain
                   data set comes from a standard acquisition scheme for
                   diffusion-tensor MRI in which the samples in each voxel
                   lie on a sphere in Fourier space.},
  url            = {http://stacks.iop.org/0266-5611/19/1031},
  year           = 2003
}

@Article{ODonnell_AJNR06,
  Author         = {O'Donnell, L. J. and Kubicki, M. and Shenton, M. E.
                   and Dreusicke, M. H. and Grimson, W. E. and Westin, C.
                   F.},
  Title          = {A method for clustering white matter fiber tracts.},
  Journal        = {AJNR Am J Neuroradiol},
  Volume         = {27},
  Number         = {5},
  Pages          = {1032-6},
  abstract       = {BACKGROUND/PURPOSE: Despite its potential for
                   visualizing white matter fiber tracts in vivo,
                   diffusion tensor tractography has found only limited
                   applications in clinical research in which specific
                   anatomic connections between distant regions need to be
                   evaluated. We introduce a robust method for fiber
                   clustering that guides the separation of anatomically
                   distinct fiber tracts and enables further estimation of
                   anatomic connectivity between distant brain regions.
                   METHODS: Line scanning diffusion tensor images (LSDTI)
                   were acquired on a 1.5T magnet. Regions of interest for
                   several anatomically distinct fiber tracts were
                   manually drawn; then, white matter tractography was
                   performed by using the Runge-Kutta method to
                   interpolate paths (fiber traces) following the major
                   directions of diffusion, in which traces were seeded
                   only within the defined regions of interest. Next, a
                   fully automatic procedure was applied to fiber traces,
                   grouping them according to a pairwise similarity
                   function that takes into account the shapes of the
                   fibers and their spatial locations. RESULTS: We
                   demonstrated the ability of the clustering algorithm to
                   separate several fiber tracts which are otherwise
                   difficult to define (left and right fornix, uncinate
                   fasciculus and inferior occipitofrontal fasciculus, and
                   corpus callosum fibers). CONCLUSION: This method
                   successfully delineates fiber tracts that can be
                   further analyzed for clinical research purposes.
                   Hypotheses regarding specific fiber connections and
                   their abnormalities in various neuropsychiatric
                   disorders can now be tested.},
  authoraddress  = {MIT Computer Science and AI Lab, Cambridge, MA 02139,
                   USA.},
  keywords       = {Adolescent ; Adult ; Brain/*anatomy \& histology ;
                   *Diffusion Magnetic Resonance Imaging/methods ; Humans
                   ; Middle Aged},
  language       = {eng},
  medline-aid    = {27/5/1032 [pii]},
  medline-crdt   = {2006/05/12 09:00},
  medline-da     = {20060511},
  medline-dcom   = {20061030},
  medline-edat   = {2006/05/12 09:00},
  medline-fau    = {O'Donnell, L J ; Kubicki, M ; Shenton, M E ;
                   Dreusicke, M H ; Grimson, W E L ; Westin, C F},
  medline-gr     = {1-R01-NS051826-01/NS/NINDS NIH HHS/United States ; K02
                   MH 01110/MH/NIMH NIH HHS/United States ; P41
                   RR13218/RR/NCRR NIH HHS/United States ; R03 MH
                   068464-02/MH/NIMH NIH HHS/United States ; U54
                   EB005149/EB/NIBIB NIH HHS/United States},
  medline-is     = {0195-6108 (Print)},
  medline-jid    = {8003708},
  medline-jt     = {AJNR. American journal of neuroradiology},
  medline-lr     = {20080214},
  medline-mhda   = {2006/10/31 09:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {16687538},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural
                   ; Research Support, Non-U.S. Gov't ; Research Support,
                   U.S. Gov't, Non-P.H.S.},
  medline-sb     = {IM},
  medline-so     = {AJNR Am J Neuroradiol. 2006 May;27(5):1032-6.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16687538},
  year           = 2006
}

@Article{Perrin2008,
  Author         = {Perrin, Muriel and Cointepas, Yann and Cachia, Arnaud
                   and Poupon, Cyril and Thirion, Bertrand and
                   Rivi\`{e}re, Denis and Cathier, Pascal and {El Kouby},
                   Vincent and Constantinesco, Andr\'{e} and {Le Bihan},
                   Denis and Mangin, Jean-Fran\c{c}ois},
  Title          = {{Connectivity-Based Parcellation of the Cortical
                   Mantle Using q-Ball Diffusion Imaging.}},
  Journal        = {International journal of biomedical imaging},
  Volume         = {2008},
  Pages          = {368406},
  abstract       = {This paper exploits the idea that each individual
                   brain region has a specific connection profile to
                   create parcellations of the cortical mantle using MR
                   diffusion imaging. The parcellation is performed in two
                   steps. First, the cortical mantle is split at a
                   macroscopic level into 36 large gyri using a sulcus
                   recognition system. Then, for each voxel of the cortex,
                   a connection profile is computed using a probabilistic
                   tractography framework. The tractography is performed
                   from q fields using regularized particle trajectories.
                   Fiber ODF are inferred from the q-balls using a
                   sharpening process focusing the weight around the
                   q-ball local maxima. A sophisticated mask of
                   propagation computed from a T1-weighted image perfectly
                   aligned with the diffusion data prevents the particles
                   from crossing the cortical folds. During propagation,
                   the particles father child particles in order to
                   improve the sampling of the long fascicles. For each
                   voxel, intersection of the particle trajectories with
                   the gyri lead to a connectivity profile made up of only
                   36 connection strengths. These profiles are clustered
                   on a gyrus by gyrus basis using a K-means approach
                   including spatial regularization. The reproducibility
                   of the results is studied for three subjects using
                   spatial normalization.},
  doi            = {10.1155/2008/368406},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Perrin et al. - 2008 -
                   Connectivity-Based Parcellation of the Cortical Mantle
                   Using q-Ball Diffusion Imaging..pdf:pdf},
  issn           = {1687-4188},
  pmid           = {18401457},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18401457},
  year           = 2008
}

@PhdThesis{maddah2008quantitative,
  Author         = {Maddah, M.},
  Title          = {{Quantitative Analysis of Cerebral White Matter
                   Anatomy from Diffusion MRI}},
  School         = {Citeseer},
  year           = 2008
}

@Article{Schmahmann2007,
  Author         = {Schmahmann, Jeremy D and Pandya, Deepak N and Wang,
                   Ruopeng and Dai, Guangping and Arceuil, Helen E D and
                   Crespigny, Alex J De and Wedeen, Van J},
  Title          = {{Association fibre pathways of the brain : parallel
                   observations from diffusion spectrum imaging and
                   autoradiography}},
  Journal        = {Brain},
  Pages          = {630--653},
  doi            = {10.1093/brain/awl359},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Schmahmann et al. - 2007 -
                   Association fibre pathways of the brain parallel
                   observations from diffusion spectrum imaging and
                   autoradiography.pdf:pdf},
  keywords       = {abbreviations,af ¼ arcuate fasciculus,ass ¼ superior
                   limb,callosum,cb ¼ cingulum bundle,cc ¼ corpus,cs ¼
                   central sulcus,diffusion tensor
                   imaging,disconnection,dsi ¼ diffusion spectrum,dti ¼
                   diffusion tensor,dwi ¼ diffusion weighted,emc ¼
                   extreme capsule,epi ¼ echoplanar imaging,fibre
                   bundles,fof ¼ fronto-occipital fasciculus,ilf ¼
                   inferior longitudinal,image,imaging,isotope,of the
                   arcuate sulcus,tract tracing,tractography},
  year           = 2007
}

@Article{PPC+05,
  Author         = {Perrin, M. and Poupon, C. and Cointepas, Y. and Rieul,
                   B. and Golestani, N. and Pallier, C. and Riviere, D.
                   and Constantinesco, A. and Le Bihan, D. and Mangin, J.
                   F.},
  Title          = {Fiber tracking in q-ball fields using regularized
                   particle trajectories.},
  Journal        = {Inf Process Med Imaging},
  Volume         = {19},
  Pages          = {52-63},
  abstract       = {Most of the approaches dedicated to fiber tracking
                   from diffusion-weighted MR data rely on a tensor model.
                   However, the tensor model can only resolve a single
                   fiber orientation within each imaging voxel. New
                   emerging approaches have been proposed to obtain a
                   better representation of the diffusion process
                   occurring in fiber crossing. In this paper, we adapt a
                   tracking algorithm to the q-ball representation, which
                   results from a spherical Radon transform of high
                   angular resolution data. This algorithm is based on a
                   Monte-Carlo strategy, using regularized particle
                   trajectories to sample the white matter geometry. The
                   method is validated using a phantom of bundle crossing
                   made up of haemodialysis fibers. The method is also
                   applied to the detection of the auditory tract in three
                   human subjects.},
  authoraddress  = {Service Hospitalier Frederic Joliot, CEA, 91401 Orsay,
                   France. perrin@shfj.cea.fr},
  keywords       = {Algorithms ; *Artificial Intelligence ;
                   Brain/*cytology ; Diffusion Magnetic Resonance
                   Imaging/*methods ; Humans ; Image Enhancement/methods ;
                   Image Interpretation, Computer-Assisted/*methods ;
                   Imaging, Three-Dimensional/*methods ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Pattern Recognition,
                   Automated/*methods ; Reproducibility of Results ;
                   Sensitivity and Specificity},
  language       = {eng},
  medline-crdt   = {2007/03/16 09:00},
  medline-da     = {20070314},
  medline-dcom   = {20070406},
  medline-edat   = {2007/03/16 09:00},
  medline-fau    = {Perrin, M ; Poupon, C ; Cointepas, Y ; Rieul, B ;
                   Golestani, N ; Pallier, C ; Riviere, D ;
                   Constantinesco, A ; Le Bihan, D ; Mangin, J F},
  medline-is     = {1011-2499 (Print)},
  medline-jid    = {9216871},
  medline-jt     = {Information processing in medical imaging :
                   proceedings of the ... conference},
  medline-mhda   = {2007/04/07 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {17354684},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article},
  medline-sb     = {IM},
  medline-so     = {Inf Process Med Imaging. 2005;19:52-63.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17354684},
  year           = 2005
}

@Article{Neji2008a,
  Author         = {Neji, Radhou\`{e}ne and Gilles, Jean-fran\c{c}ois Deux
                   and Mezri, Fleury and Georg, Maatouk},
  Title          = {{A Kernel-based Approach to Diffusion Tensor and Fiber
                   Clustering in the Human Skeletal Muscle}},
  Journal        = {October},
  Number         = {October},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Neji et al. - 2008 - A Kernel-based
                   Approach to Diffusion Tensor and Fiber Clustering in
                   the Human Skeletal Muscle.pdf:pdf},
  year           = 2008
}

@Article{Tournier2007,
  Author         = {Tournier, J-Donald and Calamante, Fernando and
                   Connelly, Alan},
  Title          = {{Robust determination of the fibre orientation
                   distribution in diffusion MRI: non-negativity
                   constrained super-resolved spherical deconvolution.}},
  Journal        = {NeuroImage},
  Volume         = {35},
  Number         = {4},
  Pages          = {1459--72},
  abstract       = {Diffusion-weighted (DW) MR images contain information
                   about the orientation of brain white matter fibres that
                   potentially can be used to study human brain
                   connectivity in vivo using tractography techniques.
                   Currently, the diffusion tensor model is widely used to
                   extract fibre directions from DW-MRI data, but fails in
                   regions containing multiple fibre orientations. The
                   spherical deconvolution technique has recently been
                   proposed to address this limitation. It provides an
                   estimate of the fibre orientation distribution (FOD) by
                   assuming the DW signal measured from any fibre bundle
                   is adequately described by a single response function.
                   However, the deconvolution is ill-conditioned and
                   susceptible to noise contamination. This tends to
                   introduce artefactual negative regions in the FOD,
                   which are clearly physically impossible. In this study,
                   the introduction of a constraint on such negative
                   regions is proposed to improve the conditioning of the
                   spherical deconvolution. This approach is shown to
                   provide FOD estimates that are robust to noise whilst
                   preserving angular resolution. The approach also
                   permits the use of super-resolution, whereby more FOD
                   parameters are estimated than were actually measured,
                   improving the angular resolution of the results. The
                   method provides much better defined fibre orientation
                   estimates, and allows orientations to be resolved that
                   are separated by smaller angles than previously
                   possible. This should allow tractography algorithms to
                   be designed that are able to track reliably through
                   crossing fibre regions.},
  doi            = {10.1016/j.neuroimage.2007.02.016},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tournier, Calamante, Connelly - 2007
                   - Robust determination of the fibre orientation
                   distribution in diffusion MRI non-negativity
                   constrained super-resolved spherical
                   deconvolution..pdf:pdf},
  issn           = {1053-8119},
  keywords       = {Algorithms,Brain,Brain: cytology,Computer
                   Simulation,Data Interpretation, Statistical,Diffusion
                   Magnetic Resonance Imaging,Humans,Image Processing,
                   Computer-Assisted,Models, Statistical,Nerve
                   Fibers,Nerve Fibers: physiology,Reproducibility of
                   Results},
  pmid           = {17379540},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/17379540},
  year           = 2007
}

@Article{Descoteaux2009,
  Author         = {Descoteaux, Maxime and Deriche, Rachid and
                   Kn\"{o}sche, Thomas R and Anwander, Alfred},
  Title          = {{Deterministic and probabilistic tractography based on
                   complex fibre orientation distributions.}},
  Journal        = {IEEE transactions on medical imaging},
  Volume         = {28},
  Number         = {2},
  Pages          = {269--86},
  abstract       = {We propose an integral concept for tractography to
                   describe crossing and splitting fibre bundles based on
                   the fibre orientation distribution function (ODF)
                   estimated from high angular resolution diffusion
                   imaging (HARDI). We show that in order to perform
                   accurate probabilistic tractography, one needs to use a
                   fibre ODF estimation and not the diffusion ODF. We use
                   a new fibre ODF estimation obtained from a sharpening
                   deconvolution transform (SDT) of the diffusion ODF
                   reconstructed from q-ball imaging (QBI). This SDT
                   provides new insight into the relationship between the
                   HARDI signal, the diffusion ODF, and the fibre ODF. We
                   demonstrate that the SDT agrees with classical
                   spherical deconvolution and improves the angular
                   resolution of QBI. Another important contribution of
                   this paper is the development of new deterministic and
                   new probabilistic tractography algorithms using the
                   full multidirectional information obtained through use
                   of the fibre ODF. An extensive comparison study is
                   performed on human brain datasets comparing our new
                   deterministic and probabilistic tracking algorithms in
                   complex fibre crossing regions. Finally, as an
                   application of our new probabilistic tracking, we
                   quantify the reconstruction of transcallosal fibres
                   intersecting with the corona radiata and the superior
                   longitudinal fasciculus in a group of eight subjects.
                   Most current diffusion tensor imaging (DTI)-based
                   methods neglect these fibres, which might lead to
                   incorrect interpretations of brain functions.},
  doi            = {10.1109/TMI.2008.2004424},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Descoteaux et al. - 2009 -
                   Deterministic and probabilistic tractography based on
                   complex fibre orientation distributions..pdf:pdf},
  issn           = {1558-0062},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion
                   Magnetic Resonance Imaging,Diffusion Magnetic Resonance
                   Imaging: methods,Echo-Planar Imaging,Echo-Planar
                   Imaging: methods,Humans,Image Enhancement,Image
                   Enhancement: methods,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Models, Neurological,Models, Statistical,Nerve
                   Fibers,Nerve Fibers: ultrastructure,Normal
                   Distribution,Reproducibility of Results,Sensitivity and
                   Specificity},
  month          = feb,
  pmid           = {19188114},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19188114},
  year           = 2009
}

@Article{zhang1997birch,
  Author         = {Zhang, T. and Ramakrishnan, R. and Livny, M.},
  Title          = {{BIRCH: A new data clustering algorithm and its
                   applications}},
  Journal        = {Data Mining and Knowledge Discovery},
  Volume         = {1},
  Number         = {2},
  Pages          = {141--182},
  publisher      = {Springer},
  year           = 1997
}

@Article{Leemans2005MagResMed,
  Author         = {Leemans, A. and Sijbers, J. and Verhoye, M. and {Van
                   der Linden}, A. and {Van Dyck}, D. },
  Title          = {Mathematical framework for simulating diffusion tensor
                   \{{M}{R}\} neural fiber bundles},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {53},
  Number         = {4},
  Pages          = {944-953},
  doi            = {10.1002/mrm.20418},
  file           = {attachment\:Leemans2005MagResMed.pdf:attachment\:Leemans2005MagResMed.pdf:PDF},
  publisher      = {Wiley-Liss, Inc.},
  url            = {http://dx.doi.org/10.1002/mrm.20418},
  year           = 2005
}

@Article{Jones2002,
  Author         = {Jones, Derek K. and Basser, Peter J.},
  Title          = {{Diffusion-tensor MRI: theory, experimental design and
                   data analysis - a technical review}},
  Journal        = {NMR in Biomedicine},
  Volume         = {15},
  Number         = {7-8},
  Pages          = {456--467},
  abstract       = {This article treats the theoretical underpinnings of
                   diffusion-tensor magnetic resonance imaging (DT-MRI),
                   as well as experimental design and data analysis
                   issues. We review the mathematical model underlying
                   DT-MRI, discuss the quantitative parameters that are
                   derived from the measured effective diffusion tensor,
                   and describe artifacts thet arise in typical DT-MRI
                   acquisitions. We also discuss difficulties in
                   identifying appropriate models to describe water
                   diffusion in heterogeneous tissues, as well as in
                   interpreting experimental data obtained in such issues.
                   Finally, we describe new statistical methods that have
                   been developed to analyse DT-MRI data, and their
                   potential uses in clinical and multi-site studies.
                   Copyright � 2002 John Wiley \& Sons, Ltd.},
  doi            = {10.1002/nbm.783},
  shorttitle     = {Diffusion-tensor MRI},
  url            = {http://dx.doi.org/10.1002/nbm.783},
  year           = 2002
}

@Article{Mining1997,
  Author         = {Mining, Data and Discovery, Knowledge},
  Title          = {{BIRCH : A New Data Clustering Algorithm and Its
                   Applications}},
  Journal        = {Knowledge Creation Diffusion Utilization},
  Volume         = {182},
  Pages          = {141--182},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mining, Discovery - 1997 - BIRCH A
                   New Data Clustering Algorithm and Its
                   Applications.pdf:pdf},
  keywords       = {data classification and compression,data
                   clustering,incremental algorithm,very large databases},
  year           = 1997
}

@Article{Chan,
  Author         = {Chan, Cy and Drensky, Vesselin and Edelman, Alan and
                   Kan, Raymond and Koev, Plamen},
  Title          = {{On Computing Schur Functions and Series Thereof}},
  Journal        = {Journal of Algebraic Combinatorics},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Chan et al. - Unknown - On Computing
                   Schur Functions and Series Thereof.pdf:pdf},
  keywords       = {computing,hypergeometric function of a,matrix
                   argument,schur function}
}

@Article{MKW+08,
  Author         = {Maddah, M. and Kubicki, M. and Wells, W. M. and
                   Westin, C. F. and Shenton, M. E. and Grimson, W. E.},
  Title          = {Findings in schizophrenia by tract-oriented {DT}-{MRI}
                   analysis.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {11},
  Number         = {Pt 1},
  Pages          = {917-24},
  abstract       = {This paper presents a tract-oriented analysis of
                   diffusion tensor (DT) images of the human brain. We
                   demonstrate that unlike the commonly used ROI-based
                   methods for population studies, our technique is
                   sensitive to the local variation of diffusivity
                   parameters along the fiber tracts. We show the strength
                   of the proposed approach in identifying the differences
                   in schizophrenic data compared to controls.
                   Statistically significant drops in fractional
                   anisotropy are observed along the genu and bilaterally
                   in the splenium, as well as an increase in principal
                   eigenvalue in uncinate fasciculus. This is the first
                   tract-oriented clinical study in which an anatomical
                   atlas is used to guide the algorithm.},
  authoraddress  = {Computer Science and Artificial Intelligence
                   Laboratory, Massachusetts Institute of Technology,
                   Cambridge, MA, USA. mmaddah@mit.edu},
  keywords       = {Algorithms ; *Artificial Intelligence ; Brain
                   Diseases/*diagnosis ; Diffusion Magnetic Resonance
                   Imaging/*methods ; Female ; Humans ; Image
                   Enhancement/methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Male ; Nerve Fibers,
                   Myelinated/*pathology ; Pattern Recognition,
                   Automated/*methods ; Reproducibility of Results ;
                   Schizophrenia/*diagnosis ; Sensitivity and Specificity},
  language       = {eng},
  medline-crdt   = {2008/11/05 09:00},
  medline-da     = {20081104},
  medline-dcom   = {20081209},
  medline-edat   = {2008/11/05 09:00},
  medline-fau    = {Maddah, Mahnaz ; Kubicki, Marek ; Wells, William M ;
                   Westin, Carl-Fredrik ; Shenton, Martha E ; Grimson, W
                   Eric L},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-mhda   = {2008/12/17 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {18979833},
  medline-pst    = {ppublish},
  medline-pt     = {Evaluation Studies ; Journal Article},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2008;11(Pt
                   1):917-24.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18979833},
  year           = 2008
}

@Article{MaddahMIA2008,
  Author         = {Maddah, M. and Grimson, W. E. and Warfield, S. K. and
                   Wells, W. M.},
  Title          = {A unified framework for clustering and quantitative
                   analysis of white matter fiber tracts.},
  Journal        = {Med Image Anal},
  Volume         = {12},
  Number         = {2},
  Pages          = {191-202},
  abstract       = {We present a novel approach for joint clustering and
                   point-by-point mapping of white matter fiber pathways.
                   Knowledge of the point correspondence along the fiber
                   pathways is not only necessary for accurate clustering
                   of the trajectories into fiber bundles, but also
                   crucial for any tract-oriented quantitative analysis.
                   We employ an expectation-maximization (EM) algorithm to
                   cluster the trajectories in a gamma mixture model
                   context. The result of clustering is the probabilistic
                   assignment of the fiber trajectories to each cluster,
                   an estimate of the cluster parameters, i.e. spatial
                   mean and variance, and point correspondences. The fiber
                   bundles are modeled by the mean trajectory and its
                   spatial variation. Point-by-point correspondence of the
                   trajectories within a bundle is obtained by
                   constructing a distance map and a label map from each
                   cluster center at every iteration of the EM algorithm.
                   This offers a time-efficient alternative to pairwise
                   curve matching of all trajectories with respect to each
                   cluster center. The proposed method has the potential
                   to benefit from an anatomical atlas of fiber tracts by
                   incorporating it as prior information in the EM
                   algorithm. The algorithm is also capable of handling
                   outliers in a principled way. The presented results
                   confirm the efficiency and effectiveness of the
                   proposed framework for quantitative analysis of
                   diffusion tensor MRI.},
  authoraddress  = {Computer Science and Artificial Intelligence
                   Laboratory, Massachusetts Institute of Technology, 32
                   Vassar Street, Cambridge, USA. mmaddah@mit.edu},
  keywords       = {Algorithms ; *Artificial Intelligence ; Brain/*anatomy
                   \& histology ; *Cluster Analysis ; Diffusion Magnetic
                   Resonance Imaging/*methods ; Humans ; Image
                   Enhancement/methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/methods ; Likelihood Functions ;
                   Models, Biological ; Models, Statistical ; Nerve
                   Fibers, Myelinated/*ultrastructure ; Pattern
                   Recognition, Automated/*methods ; Reproducibility of
                   Results ; Sensitivity and Specificity},
  language       = {eng},
  medline-aid    = {S1361-8415(07)00099-0 [pii] ;
                   10.1016/j.media.2007.10.003 [doi]},
  medline-crdt   = {2008/01/09 09:00},
  medline-da     = {20080416},
  medline-dcom   = {20080520},
  medline-dep    = {20071025},
  medline-edat   = {2008/01/09 09:00},
  medline-fau    = {Maddah, Mahnaz ; Grimson, W Eric L ; Warfield, Simon K
                   ; Wells, William M},
  medline-gr     = {P30 HD018655/HD/NICHD NIH HHS/United States ; P30
                   HD018655-26/HD/NICHD NIH HHS/United States ; P41
                   RR013218/RR/NCRR NIH HHS/United States ; P41
                   RR013218-010001/RR/NCRR NIH HHS/United States ; P41
                   RR013218-010002/RR/NCRR NIH HHS/United States ; P41
                   RR013218-010010/RR/NCRR NIH HHS/United States ; R01
                   RR021885/RR/NCRR NIH HHS/United States ; R01
                   RR021885-01A1/RR/NCRR NIH HHS/United States ; R01
                   RR021885-02/RR/NCRR NIH HHS/United States ; R03
                   CA126466/CA/NCI NIH HHS/United States ; R03
                   CA126466-01A1/CA/NCI NIH HHS/United States ; R03
                   CA126466-02/CA/NCI NIH HHS/United States ; R21
                   MH067054/MH/NIMH NIH HHS/United States ; R21
                   MH067054-01A1/MH/NIMH NIH HHS/United States ; R21
                   MH067054-02/MH/NIMH NIH HHS/United States ; U41
                   RR019703/RR/NCRR NIH HHS/United States ; U54
                   EB005149/EB/NIBIB NIH HHS/United States},
  medline-is     = {1361-8423 (Electronic)},
  medline-jid    = {9713490},
  medline-jt     = {Medical image analysis},
  medline-lr     = {20090406},
  medline-mhda   = {2008/05/21 09:00},
  medline-mid    = {NIHMS49862},
  medline-oid    = {NLM: NIHMS49862 ; NLM: PMC2615202},
  medline-own    = {NLM},
  medline-phst   = {2006/11/18 [received] ; 2007/10/02 [revised] ;
                   2007/10/02 [accepted] ; 2007/10/25 [aheadofprint]},
  medline-pl     = {Netherlands},
  medline-pmc    = {PMC2615202},
  medline-pmid   = {18180197},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural
                   ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {Med Image Anal. 2008 Apr;12(2):191-202. Epub 2007 Oct
                   25.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18180197},
  year           = 2008
}

@Article{Tuch2005,
  Author         = {Tuch, David S and Wisco, Jonathan J and Khachaturian,
                   Mark H and Vanduffel, Wim and Ekstrom, Leeland B and
                   Ko, Rolf},
  Title          = {{Q-ball imaging of macaque white matter architecture}},
  Number         = {May},
  Pages          = {869--879},
  doi            = {10.1098/rstb.2005.1651},
  keywords       = {connectivity,diffusion magnetic resonance imaging,high
                   angular resolution
                   diffusion,imaging,macaque,tractography,white matter},
  year           = 2005
}

@Article{George2009,
  Author         = {George, Kyriazis and Erwan, Le Pennec and Pencho,
                   Petrushev and Dominique, Picard},
  Title          = {{Inversion of noisy Radon transform by SVD based
                   needlets arXiv : 0809 . 3332v2 [ math . ST ] 17 Aug
                   2009}},
  Pages          = {1--35},
  arxivid        = {arXiv:0809.3332v2},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/George et al. - 2009 - Inversion of
                   noisy Radon transform by SVD based needlets arXiv 0809
                   . 3332v2 math . ST 17 Aug 2009.pdf:pdf},
  year           = 2009
}

@Article{Hagmann2007PLoSONE,
  Author         = {Hagmann, Patric and Kurant, Maciej and Gigandet,
                   Xavier and Thiran, Patrick and Wedeen, Van J. and
                   Meuli, Reto and Thiran, Jean-Philippe },
  Title          = {Mapping human whole-brain structural networks with
                   diffusion {MRI}.},
  Journal        = {PLoS ONE},
  Volume         = {2},
  Number         = {7},
  Pages          = {e597},
  abstract       = {Understanding the large-scale structural network
                   formed by neurons is a major challenge in system
                   neuroscience. A detailed connectivity map covering the
                   entire brain would therefore be of great value. Based
                   on diffusion MRI, we propose an efficient methodology
                   to generate large, comprehensive and individual white
                   matter connectional datasets of the living or dead,
                   human or animal brain. This non-invasive tool enables
                   us to study the basic and potentially complex network
                   properties of the entire brain. For two human subjects
                   we find that their individual brain networks have an
                   exponential node degree distribution and that their
                   global organization is in the form of a small world.},
  doi            = {10.1371/journal.pone.0000597},
  file           = {attachment\:Hagmann2007PLoSONE.pdf:attachment\:Hagmann2007PLoSONE.pdf:PDF},
  year           = 2007
}

@Article{torrey1956bed,
  Author         = {Torrey, H.C.},
  Title          = {{Bloch equations with diffusion terms}},
  Journal        = {Physical Review},
  Volume         = {104},
  Number         = {3},
  Pages          = {563--565},
  publisher      = {APS},
  year           = 1956
}

@Article{Perrin2005PhilTransRoySoc,
  Author         = {Perrin, Muriel and Poupon, Cyril and Rieul, Bernard
                   and Leroux, Patrick and Constantinesco, Andr and
                   Mangin, Jean-Franois and LeBihan, Denis},
  Title          = {Validation of q-ball imaging with a diffusion
                   fibre-crossing phantom on a clinical scanner},
  Journal        = {Philosophical Transactions of the Royal Society B:
                   Biological Sciences},
  Volume         = {360},
  Number         = {1457},
  Pages          = {881-91},
  abstract       = {Magnetic resonance (MR) diffusion imaging provides a
                   valuable tool used for inferring structural anisotropy
                   of brain white matter connectivity from diffusion
                   tensor imaging. Recently, several high angular
                   resolution diffusion models were introduced in order to
                   overcome the inadequacy of the tensor model for
                   describing fibre crossing within a single voxel. Among
                   them, q-ball imaging (QBI), inherited from the q-space
                   method, relies on a spherical Radon transform providing
                   a direct relationship between the diffusion-weighted MR
                   signal and the orientation distribution function (ODF).
                   Experimental validation of these methods in a model
                   system is necessary to determine the accuracy of the
                   methods and to optimize them. A diffusion phantom made
                   up of two textile rayon fibre (comparable in diameter
                   to axons) bundles, crossing at $90^o$, was designed and
                   dedicated to ex vivo q-ball validation on a clinical
                   scanner. Normalized ODFs were calculated inside regions
                   of interest corresponding to monomodal and bimodal
                   configurations of underlying structures.
                   Threedimensional renderings of ODFs revealed monomodal
                   shapes for voxels containing single-fibre population
                   and bimodal patterns for voxels located within the
                   crossing area. Principal orientations were estimated
                   from ODFs and were compared with a priori structural
                   fibre directions, validating efficiency of QBI for
                   depicting fibre crossing. In the homogeneous regions,
                   QBI detected the fibre angle with an accuracy of $19^o$
                   and in the fibre-crossing region with an accuracy of
                   $30^o$.},
  doi            = {10.1098/rstb.2005.1650},
  file           = {attachment\:Perrin2005PhilTransRoySoc.pdf:attachment\:Perrin2005PhilTransRoySoc.pdf:PDF},
  url            = {http://journals.royalsociety.org/content/mldn6494e2xf23ta},
  year           = 2005
}

@Article{Frey2008,
  Author         = {Frey, S and Campbell, JSW and Pike, GB},
  Title          = {\ldots human language pathways with high angular
                   resolution diffusion fiber tractography},
  Journal        = {Journal of Neuroscience},
  url            = {http://neuro.cjb.net/cgi/content/abstract/28/45/11435},
  year           = 2008
}

@Article{Stejskal1965JChemPhys,
  Author         = {E. O. Stejskal and J. E. Tanner},
  Title          = {Spin Diffusion Measurements: Spin Echoes in the
                   Presence of a Time-Dependent Field Gradient},
  Journal        = {The Journal of Chemical Physics},
  Volume         = {42},
  Number         = {1},
  Pages          = {288-292},
  doi            = {10.1063/1.1695690},
  publisher      = {AIP},
  url            = {http://link.aip.org/link/?JCP/42/288/1},
  year           = 1965
}

@Article{Reisert,
  Author         = {Reisert, Marco and Mader, Irina and Kiselev, Valerij},
  Title          = {{Global Reconstruction of Neuronal Fibres}},
  Journal        = {Lecture Notes in Computer Science},
  Pages          = {1--12},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Reisert, Mader, Kiselev - Unknown -
                   Global Reconstruction of Neuronal Fibres.pdf:pdf}
}

@Article{Koles1998,
  Author         = {Koles, Z J and Soong, a C},
  Title          = {{EEG source localization: implementing the
                   spatio-temporal decomposition approach.}},
  Journal        = {Electroencephalography and clinical neurophysiology},
  Volume         = {107},
  Number         = {5},
  Pages          = {343--52},
  abstract       = {OBJECTIVES: The spatio-temporal decomposition (STD)
                   approach was used to localize the sources of simulated
                   electroencephalograms (EEGs) to gain experience with
                   the approach for analyzing real data. METHODS: The STD
                   approach used is similar to the multiple signal
                   classification method (MUSIC) in that it requires the
                   signal subspace containing the sources of interest to
                   be isolated in the EEG measurement space. It is
                   different from MUSIC in that it allows more general
                   methods of spatio-temporal decomposition to be used
                   that may be better suited to the background EEG.
                   RESULTS: If the EEG data matrix is not corrupted by
                   noise, the STD approach can be used to locate multiple
                   dipole sources of the EEG one at a time without a
                   priori knowledge of the number of active sources in the
                   signal space. In addition, the common-spatial-patterns
                   method of spatio-temporal decomposition is superior to
                   the eigenvector decomposition for localizing activity
                   that is ictal in nature. CONCLUSIONS: The STD approach
                   appears to be able to provide a means of localizing the
                   equivalent dipole sources of realistic brain sources
                   and that, even under difficult noise conditions and
                   only 2 or 3 s of available EEG, the precision of the
                   localization can be as low as a few mm.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Koles, Soong - 1998 - EEG source
                   localization implementing the spatio-temporal
                   decomposition approach..pdf:pdf},
  issn           = {0013-4694},
  keywords       = {Artifacts,Brain,Brain Mapping,Brain Mapping:
                   methods,Brain: physiology,Computer
                   Simulation,Electrodes,Electroencephalography,Electroencephalography:
                   instrumentation,Evaluation Studies as
                   Topic,Humans,Models, Neurological,Time Factors},
  month          = nov,
  pmid           = {9872437},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/9872437},
  year           = 1998
}

@Article{roebroeck2008hrd,
  Author         = {Roebroeck, A. and Galuske, R. and Formisano, E. and
                   Chiry, O. and Bratzke, H. and Ronen, I. and Kim, D. and
                   Goebel, R.},
  Title          = {{High-resolution diffusion tensor imaging and
                   tractography of the human optic chiasm at 9.4 T}},
  Journal        = {Neuroimage},
  Volume         = {39},
  Number         = {1},
  Pages          = {157--168},
  publisher      = {Elsevier},
  year           = 2008
}

@Article{To,
  Author         = {To, Introduction},
  Title          = {{INTRODUCTION TO PROBABILITY}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/To - Unknown - INTRODUCTION TO
                   PROBABILITY.pdf:pdf}
}

@Article{Catani2008,
  Author         = {Catani, M and Mesulam, M},
  Title          = {{The arcuate fasciculus and the disconnection theme in
                   language and aphasia: \ldots}},
  Journal        = {Cortex},
  url            = {http://linkinghub.elsevier.com/retrieve/pii/S0010945208001111},
  year           = 2008
}

@Article{Walter2010,
  Author         = {Walter, Thomas and Shattuck, David W and Baldock,
                   Richard and Bastin, Mark E and Carpenter, Anne E and
                   Duce, Suzanne and Ellenberg, Jan and Fraser, Adam and
                   Hamilton, Nicholas and Pieper, Steve and Ragan, Mark A
                   and Schneider, Jurgen E and Tomancak, Pavel and
                   H\'{e}rich\'{e}, Jean-karim},
  Title          = {{Visualization of image data from cells to organisms}},
  Journal        = {Nature Publishing Group},
  Volume         = {7},
  Number         = {3s},
  Pages          = {S26--S41},
  doi            = {10.1038/nmeth.1431},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Walter et al. - 2010 - Visualization
                   of image data from cells to organisms.pdf:pdf},
  issn           = {1548-7091},
  publisher      = {Nature Publishing Group},
  url            = {http://dx.doi.org/10.1038/nmeth.1431},
  year           = 2010
}

@Article{Jbabdi2007,
  Author         = {Jbabdi, S and Woolrich, M W and Andersson, J L and
                   Behrens, T E},
  Title          = {{A Bayesian framework for global tractography}},
  Journal        = {NeuroImage},
  Volume         = {37},
  Pages          = {116--129},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Jbabdi et al. - 2007 - A Bayesian
                   framework for global tractography.pdf:pdf},
  year           = 2007
}

@Article{Lewis2005,
  Author         = {Lewis, David a and Hashimoto, Takanori and Volk, David
                   W},
  Title          = {{Cortical inhibitory neurons and schizophrenia.}},
  Journal        = {Nature reviews. Neuroscience},
  Volume         = {6},
  Number         = {4},
  Pages          = {312--24},
  abstract       = {Impairments in certain cognitive functions, such as
                   working memory, are core features of schizophrenia.
                   Convergent findings indicate that a deficiency in
                   signalling through the TrkB neurotrophin receptor leads
                   to reduced GABA (gamma-aminobutyric acid) synthesis in
                   the parvalbumin-containing subpopulation of inhibitory
                   GABA neurons in the dorsolateral prefrontal cortex of
                   individuals with schizophrenia. Despite both pre- and
                   postsynaptic compensatory responses, the resulting
                   alteration in perisomatic inhibition of pyramidal
                   neurons contributes to a diminished capacity for the
                   gamma-frequency synchronized neuronal activity that is
                   required for working memory function. These findings
                   reveal specific targets for therapeutic interventions
                   to improve cognitive function in individuals with
                   schizophrenia.},
  doi            = {10.1038/nrn1648},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Lewis, Hashimoto, Volk - 2005 -
                   Cortical inhibitory neurons and schizophrenia..pdf:pdf},
  issn           = {1471-003X},
  keywords       = {Animals,Cerebral Cortex,Cerebral Cortex:
                   cytology,Cerebral Cortex: physiology,Cerebral Cortex:
                   physiopathology,Humans,Nerve Net,Nerve Net:
                   pathology,Nerve Net: physiopathology,Neural
                   Inhibition,Neural Inhibition:
                   physiology,Neurons,Neurons: cytology,Neurons:
                   physiology,Schizophrenia,Schizophrenia:
                   pathology,Schizophrenia: physiopathology},
  month          = apr,
  pmid           = {15803162},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/15803162},
  year           = 2005
}

@Article{Kenkre1997JMagRes,
  Author         = {V. M. Kenkre and Eiichi Fukushima and D. Sheltraw},
  Title          = {Simple Solutions of the Torrey-Bloch Equations in the
                   NMR Study of Molecular Diffusion},
  Journal        = {Journal of Magnetic Resonance},
  Volume         = {128},
  Number         = {1},
  Pages          = {62 - 69},
  abstract       = {A simple technique for solving the Torrey-Bloch
                   equations appearing in the calculation of the NMR
                   signal under gradient fields is presented. It is
                   applicable to arbitrary time dependence of the gradient
                   field to arbitrary initial distribution of spins, and
                   to spin motion on discrete lattices as well as in the
                   continuum under conditions of unrestricted diffusion.
                   Known results are recovered as particular cases and new
                   results are presented. The discrete lattice results are
                   shown to be similar to known results for restricted
                   diffusion in the continuum. Also presented is a
                   surprising equivalence between results for a simple
                   two-site hopping model and earlier expressions for the
                   NMR signal for spins undergoing restricted diffusion in
                   a continuum.},
  doi            = {DOI: 10.1006/jmre.1997.1216},
  issn           = {1090-7807},
  url            = {http://www.sciencedirect.com/science/article/B6WJX-45KN26H-6/2/817cb1d5d119831cc0ccf5284d324a37},
  year           = 1997
}

@Article{Ashburner2000NeuroImage,
  Author         = {Ashburner, John and Friston, Karl J.},
  Title          = {Voxel-Based Morphometry - The Methods},
  Journal        = {NeuroImage},
  Volume         = {11},
  Pages          = {805-821},
  abstract       = {At its simplest, voxel-based morphometry (VBM)
                   involves a voxel-wise comparison of the local
                   concentration of gray matter between two groups of
                   subjects. The procedure is relatively straightforward
                   and involves spatially normalizing high-resolution
                   images from all the subjects in the study into the same
                   stereotactic space. This is followed by segmenting the
                   gray matter from the spatially normalized images and
                   smoothing the gray-matter segments. Voxel-wise
                   parametric statistical tests which compare the smoothed
                   gray-matter images from the two groups are performed.
                   Corrections for multiple comparisons are made using the
                   theory of Gaussian random fields. This paper describes
                   the steps involved in VBM, with particular emphasis on
                   segmenting gray matter from MR images with
                   nonuniformity artifact. We provide evaluations of the
                   assumptions that underpin the method, including the
                   accuracy of the segmentation and the assumptions made
                   about the statistical distribution of the data.-},
  doi            = {10.1006/nimg.2000.0582},
  file           = {attachment\:Ashburner2000NeuroImage.pdf:attachment\:Ashburner2000NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2000
}

@Article{Ding2003a,
  Author         = {Ding, Zhaohua and Gore, John C and Anderson, Adam W},
  Title          = {{Classification and quantification of neuronal fiber
                   pathways using diffusion tensor MRI.}},
  Journal        = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  Volume         = {49},
  Number         = {4},
  Pages          = {716--21},
  abstract       = {Quantitative characterization of neuronal fiber
                   pathways in vivo is of significant neurological and
                   clinical interest. Using the capability of MR diffusion
                   tensor imaging to determine the local orientations of
                   neuronal fibers, novel algorithms were developed to
                   bundle neuronal fiber pathways reconstructed in vivo
                   with diffusion tensor images and to quantify various
                   physical and geometric properties of fiber bundles. The
                   reliability of the algorithms was examined with
                   reproducibility tests. Illustrative results show that
                   consistent physical and geometric measurements of novel
                   properties of neuronal tissue can be obtained, which
                   offer considerable potential for the quantitative study
                   of fiber pathways in vivo.},
  doi            = {10.1002/mrm.10415},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ding, Gore, Anderson - 2003 -
                   Classification and quantification of neuronal fiber
                   pathways using diffusion tensor MRI..pdf:pdf},
  issn           = {0740-3194},
  keywords       = {Algorithms,Brain Mapping,Brain Mapping:
                   methods,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Magnetic Resonance Imaging,Magnetic Resonance
                   Imaging: methods,Nerve Fibers,Nerve Fibers:
                   classification,Neural Pathways,Neural Pathways: anatomy
                   \& histology,Reproducibility of Results},
  pmid           = {12652543},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/12652543},
  year           = 2003
}

@Article{powell2005mtp,
  Author         = {Powell, HWR and Parker, GJM and Alexander, DC and
                   Symms, MR and Boulby, PA and Wheeler-Kingshott, CAM and
                   Barker, GJ and Koepp, MJ and Duncan, JS},
  Title          = {{MR tractography predicts visual field defects
                   following temporal lobe resection}},
  Journal        = {Neurology},
  Volume         = {65},
  Number         = {4},
  Pages          = {596--599},
  publisher      = {AAN Enterprises},
  year           = 2005
}

@Article{lawes2008abs,
  Author         = {Lawes, I. N. C. and Barrick, T.R. and Murugam, V. and
                   Spierings, N. and Evans, D.R. and Song, M. and Clark,
                   C. A.},
  Title          = {{Atlas-based segmentation of white matter tracts of
                   the human brain using diffusion tensor tractography and
                   comparison with classical dissection.}},
  Journal        = {Neuroimage},
  Volume         = {39},
  Pages          = {62--79},
  file           = {attachment\:lawes_dti_atlas-based_segmentation_2008.pdf:attachment\:lawes_dti_atlas-based_segmentation_2008.pdf:PDF},
  year           = 2008
}

@Article{Tanaka1999,
  Author         = {Tanaka, Hidefumi},
  Title          = {{Circular asymmetry of the paleomagnetic directions
                   observed at low latitude volcanic sites}},
  Journal        = {Simulation},
  Number         = {4},
  Pages          = {1279--1286},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tanaka - 1999 - Circular asymmetry
                   of the paleomagnetic directions observed at low
                   latitude volcanic sites.pdf:pdf},
  year           = 1999
}

@Article{MaddahIPMI2007,
  Author         = {Maddah, M. and Wells, 3rd, W. M. and Warfield, S. K.
                   and Westin, C. F. and Grimson, W. E.},
  Title          = {Probabilistic clustering and quantitative analysis of
                   white matter fiber tracts.},
  Journal        = {Inf Process Med Imaging},
  Volume         = {20},
  Pages          = {372-83},
  abstract       = {A novel framework for joint clustering and
                   point-by-point mapping of white matter fiber pathways
                   is presented. Accurate clustering of the trajectories
                   into fiber bundles requires point correspondence
                   determined along the fiber pathways. This knowledge is
                   also crucial for any tract-oriented quantitative
                   analysis. We employ an expectation-maximization (EM)
                   algorithm to cluster the trajectories in a Gamma
                   mixture model context. The result of clustering is the
                   probabilistic assignment of the fiber trajectories to
                   each cluster, an estimate of the cluster parameters,
                   and point correspondences along the trajectories.
                   Point-by-point correspondence of the trajectories
                   within a bundle is obtained by constructing a distance
                   map and a label map from each cluster center at every
                   iteration of the EM algorithm. This offers a
                   time-efficient alternative to pairwise curve matching
                   of all trajectories with respect to each cluster
                   center. Probabilistic assignment of the trajectories to
                   clusters is controlled by imposing a minimum threshold
                   on the membership probabilities, to remove outliers in
                   a principled way. The presented results confirm the
                   efficiency and effectiveness of the proposed framework
                   for quantitative analysis of diffusion tensor MRI.},
  authoraddress  = {Computer Science and Artificial Intelligence
                   Laboratory, Massachusetts Institute of Technology,
                   Cambridge, MA 02139, USA. mmaddah@mit.edu},
  keywords       = {Algorithms ; Artificial Intelligence ; Brain/*cytology
                   ; Cluster Analysis ; Diffusion Magnetic Resonance
                   Imaging/*methods ; Humans ; Image Enhancement/methods ;
                   Image Interpretation, Computer-Assisted/*methods ;
                   Imaging, Three-Dimensional/*methods ; Models,
                   Neurological ; Models, Statistical ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Neural Pathways/*cytology
                   ; Pattern Recognition, Automated/*methods ;
                   Reproducibility of Results ; Sensitivity and
                   Specificity},
  language       = {eng},
  medline-crdt   = {2007/07/19 09:00},
  medline-da     = {20070718},
  medline-dcom   = {20070831},
  medline-edat   = {2007/07/19 09:00},
  medline-fau    = {Maddah, Mahnaz ; Wells, William M 3rd ; Warfield,
                   Simon K ; Westin, Carl-Fredrik ; Grimson, W Eric L},
  medline-gr     = {P30 HD018655/HD/NICHD NIH HHS/United States ; P41
                   RR013218/RR/NCRR NIH HHS/United States ; R01
                   RR021885/RR/NCRR NIH HHS/United States ; R03
                   CA126466/CA/NCI NIH HHS/United States ; R21
                   MH067054/MH/NIMH NIH HHS/United States ; U41
                   RR019703/RR/NCRR NIH HHS/United States ; U54
                   EB005149/EB/NIBIB NIH HHS/United States},
  medline-is     = {1011-2499 (Print)},
  medline-jid    = {9216871},
  medline-jt     = {Information processing in medical imaging :
                   proceedings of the ... conference},
  medline-lr     = {20071203},
  medline-mhda   = {2007/09/01 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {17633714},
  medline-pst    = {ppublish},
  medline-pt     = {Evaluation Studies ; Journal Article ; Research
                   Support, N.I.H., Extramural ; Research Support,
                   Non-U.S. Gov't ; Research Support, U.S. Gov't,
                   Non-P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Inf Process Med Imaging. 2007;20:372-83.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17633714},
  year           = 2007
}

@Article{descoteaux2009deterministic,
  Author         = {Descoteaux, M. and Deriche, R. and Knoesche, T. and
                   Anwander, A.},
  Title          = {{Deterministic and probabilistic tractography based on
                   complex fibre orientation distributions}},
  Journal        = {IEEE Trans Med Imaging},
  Volume         = {28},
  Number         = {2},
  Pages          = {269--86},
  year           = 2009
}

@Article{Smith2006NeuroImage,
  Author         = {Smith, Stephen M. and Jenkinson, Mark and
                   Johansen-Berg, Heidi and Rueckert, Daniel and Nichols,
                   Thomas E. and Mackay, Clare E. and Watkins, Kate E. and
                   Ciccarelli, Olga and Cader, Zaheer and Matthews, Paul
                   M. and Behrens, Timothy E.J.},
  Title          = {Tract-based spatial statistics: Voxelwise analysis of
                   multi-subject diffusion data},
  Journal        = {NeuroImage},
  Volume         = {31},
  Pages          = {1487-1505},
  abstract       = {There has been much recent interest in using magnetic
                   resonance diffusion imaging to provide information
                   about anatomical connectivity in the brain, by
                   measuring the anisotropic diffusion of water in white
                   matter tracts. One of the measures most commonly
                   derived from diffusion data is fractional anisotropy
                   (FA), which quantifies how strongly directional the
                   local tract structure is. Many imaging studies are
                   starting to use FA images in voxelwise statistical
                   analyses, in order to localise brain changes related to
                   development, degeneration and disease. However, optimal
                   analysis is compromised by the use of standard
                   registration algorithms; there has not to date been a
                   satisfactory solution to the question of how to align
                   FA images from multiple subjects in a way that allows
                   for valid conclusions to be drawn from the subsequent
                   voxelwise analysis. Furthermore, the arbitrariness of
                   the choice of spatial smoothing extent has not yet been
                   resolved. In this paper, we present a new method that
                   aims to solve these issues via (a) carefully tuned
                   non-linear registration, followed by (b) projection
                   onto an alignment-invariant tract representation (the
                   mean FA skeleton). We refer to this new approach as
                   Tract-Based Spatial Statistics (TBSS). TBSS aims to
                   improve the sensitivity, objectivity and
                   interpretability of analysis of multi-subject diffusion
                   imaging studies. We describe TBSS in detail and present
                   example TBSS results from several diffusion imaging
                   studies.},
  file           = {attachment\:Smith2006NeuroImage.pdf:attachment\:Smith2006NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2006
}

@Article{SAM+05,
  Author         = {Sherbondy, A. and Akers, D. and Mackenzie, R. and
                   Dougherty, R. and Wandell, B.},
  Title          = {Exploring connectivity of the brain's white matter
                   with dynamic queries.},
  Journal        = {IEEE Trans Vis Comput Graph},
  Volume         = {11},
  Number         = {4},
  Pages          = {419-30},
  abstract       = {Diffusion Tensor Imaging (DTI) is a magnetic resonance
                   imaging method that can be used to measure local
                   information about the structure of white matter within
                   the human brain. Combining DTI data with the
                   computational methods of MR tractography,
                   neuroscientists can estimate the locations and sizes of
                   nerve bundles (white matter pathways) that course
                   through the human brain. Neuroscientists have used
                   visualization techniques to better understand
                   tractography data, but they often struggle with the
                   abundance and complexity of the pathways. In this
                   paper, we describe a novel set of interaction
                   techniques that make it easier to explore and interpret
                   such pathways. Specifically, our application allows
                   neuroscientists to place and interactively manipulate
                   box or ellipsoid-shaped regions to selectively display
                   pathways that pass through specific anatomical areas.
                   These regions can be used in coordination with a simple
                   and flexible query language which allows for arbitrary
                   combinations of these queries using Boolean logic
                   operators. A representation of the cortical surface is
                   provided for specifying queries of pathways that may be
                   relevant to gray matter structures and for displaying
                   activation information obtained from functional
                   magnetic resonance imaging. By precomputing the
                   pathways and their statistical properties, we obtain
                   the speed necessary for interactive question-and-answer
                   sessions with brain researchers. We survey some
                   questions that researchers have been asking about
                   tractography data and show how our system can be used
                   to answer these questions efficiently.},
  authoraddress  = {Department of Electrical Engineering, James H. Clark
                   Center, 318 Campus Dr., Room S324, Stanford University,
                   Stanford, CA 94305, USA. Sherbond@stanford.edu},
  keywords       = {Algorithms ; Animals ; Brain/*cytology ; *Computer
                   Graphics ; Computer Simulation ; Diffusion Magnetic
                   Resonance Imaging/*methods ; Humans ; Image
                   Enhancement/*methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/methods ; Models, Neurological ;
                   Nerve Fibers, Myelinated/*ultrastructure ; Nerve
                   Net/cytology ; Neural Pathways/*cytology ; Numerical
                   Analysis, Computer-Assisted ; Online Systems ;
                   *User-Computer Interface},
  language       = {eng},
  medline-aid    = {10.1109/TVCG.2005.59 [doi]},
  medline-crdt   = {2005/09/06 09:00},
  medline-da     = {20050905},
  medline-dcom   = {20050923},
  medline-edat   = {2005/09/06 09:00},
  medline-fau    = {Sherbondy, Anthony ; Akers, David ; Mackenzie, Rachel
                   ; Dougherty, Robert ; Wandell, Brian},
  medline-is     = {1077-2626 (Print)},
  medline-jid    = {9891704},
  medline-jt     = {IEEE transactions on visualization and computer
                   graphics},
  medline-mhda   = {2005/09/24 09:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {16138552},
  medline-pst    = {ppublish},
  medline-pt     = {Evaluation Studies ; Journal Article},
  medline-sb     = {IM},
  medline-so     = {IEEE Trans Vis Comput Graph. 2005
                   Jul-Aug;11(4):419-30.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16138552},
  year           = 2005
}

@Article{ODonnell_MICCAI09,
  Author         = {O'Donnell, L. J. and Westin, C. F. and Golby, A. J.},
  Title          = {Tract-based morphometry for white matter group
                   analysis.},
  Journal        = {Neuroimage},
  Volume         = {45},
  Number         = {3},
  Pages          = {832-44},
  abstract       = {We introduce an automatic method that we call
                   tract-based morphometry, or TBM, for measurement and
                   analysis of diffusion MRI data along white matter fiber
                   tracts. Using subject-specific tractography bundle
                   segmentations, we generate an arc length
                   parameterization of the bundle with point
                   correspondences across all fibers and all subjects,
                   allowing tract-based measurement and analysis. In this
                   paper we present a quantitative comparison of fiber
                   coordinate systems from the literature and we introduce
                   an improved optimal match method that reduces spatial
                   distortion and improves intra- and inter-subject
                   variability of FA measurements. We propose a method for
                   generating arc length correspondences across
                   hemispheres, enabling a TBM study of interhemispheric
                   diffusion asymmetries in the arcuate fasciculus (AF)
                   and cingulum bundle (CB). The results of this study
                   demonstrate that TBM can detect differences that may
                   not be found by measuring means of scalar invariants in
                   entire tracts, such as the mean diffusivity (MD)
                   differences found in AF. We report TBM results of
                   higher fractional anisotropy (FA) in the left
                   hemisphere in AF (caused primarily by lower lambda(3),
                   the smallest eigenvalue of the diffusion tensor, in the
                   left AF), and higher left hemisphere FA in CB (related
                   to higher lambda(1), the largest eigenvalue of the
                   diffusion tensor, in the left CB). By mapping the
                   significance levels onto the tractography trajectories
                   for each structure, we demonstrate the anatomical
                   locations of the interhemispheric differences. The TBM
                   approach brings analysis of DTI data into the
                   clinically and neuroanatomically relevant framework of
                   the tract anatomy.},
  authoraddress  = {Department of Neurosurgery, Brigham and Women's
                   Hospital, Harvard Medical School, Boston MA, USA.
                   odonnell@bwh.harvard.edu},
  language       = {eng},
  medline-aid    = {S1053-8119(08)01282-2 [pii] ;
                   10.1016/j.neuroimage.2008.12.023 [doi]},
  medline-crdt   = {2009/01/22 09:00},
  medline-da     = {20090309},
  medline-dep    = {20081225},
  medline-edat   = {2009/01/22 09:00},
  medline-fau    = {O'Donnell, Lauren J ; Westin, Carl-Fredrik ; Golby,
                   Alexandra J},
  medline-gr     = {K08NS048063/NS/NINDS NIH HHS/United States ;
                   P41RR13218/RR/NCRR NIH HHS/United States ;
                   P41RR15241/RR/NCRR NIH HHS/United States ;
                   R01AG20012/AG/NIA NIH HHS/United States ;
                   R01MH074794/MH/NIMH NIH HHS/United States ;
                   U41RR019703/RR/NCRR NIH HHS/United States ;
                   U54EB005149/EB/NIBIB NIH HHS/United States},
  medline-is     = {1095-9572 (Electronic)},
  medline-jid    = {9215515},
  medline-jt     = {NeuroImage},
  medline-mhda   = {2009/01/22 09:00},
  medline-own    = {NLM},
  medline-phst   = {2008/08/18 [received] ; 2008/11/13 [revised] ;
                   2008/12/08 [accepted] ; 2008/12/25 [aheadofprint]},
  medline-pl     = {United States},
  medline-pmid   = {19154790},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural},
  medline-sb     = {IM},
  medline-so     = {Neuroimage. 2009 Apr 15;45(3):832-44. Epub 2008 Dec
                   25.},
  medline-stat   = {In-Process},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=19154790},
  year           = 2009
}

@Article{Kuo,
  Author         = {Kuo, L W and Chen, J H and Wedeen, V J and Tseng, W Y},
  Title          = {{Optimization of diffusion spectrum imaging and q-ball
                   imaging on clinical MRI system}},
  Journal        = {Neuroimage},
  Volume         = {vol},
  Pages          = {41pp7--18},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kuo et al. - Unknown - Optimization
                   of diffusion spectrum imaging and q-ball imaging on
                   clinical MRI system.pdf:pdf}
}

@Article{candes2008ics,
  Author         = {Cand{\`e}s, E.J. and Wakin, M.B.},
  Title          = {{An introduction to compressive sampling}},
  Journal        = {IEEE Signal Processing Magazine},
  Volume         = {25},
  Number         = {2},
  Pages          = {21--30},
  publisher      = {New York, NY: Institute of Electrical \& Electronic
                   Engineers, c1991-},
  year           = 2008
}

@Article{Poldrack2008,
  Author         = {Poldrack, Russell a and Fletcher, Paul C and Henson,
                   Richard N and Worsley, Keith J and Brett, Matthew and
                   Nichols, Thomas E},
  Title          = {{Guidelines for reporting an fMRI study.}},
  Journal        = {NeuroImage},
  Volume         = {40},
  Number         = {2},
  Pages          = {409--14},
  abstract       = {In this editorial, we outline a set of guidelines for
                   the reporting of methods and results in functional
                   magnetic resonance imaging studies and provide a
                   checklist to assist authors in preparing manuscripts
                   that meet these guidelines.},
  doi            = {10.1016/j.neuroimage.2007.11.048},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Poldrack et al. - 2008 - Guidelines
                   for reporting an fMRI study..pdf:pdf},
  issn           = {1053-8119},
  keywords       = {Guidelines as Topic,Magnetic Resonance
                   Imaging,Publishing,Publishing: standards},
  pmid           = {18191585},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18191585},
  year           = 2008
}

@Article{Miki2007,
  Author         = {Miki, Y and Urayama, S and Fushimi, Y and Okada, T and
                   Hanakawa, T and Fukuyama, H},
  Title          = {{Diffusion Tensor Fiber Tractography of the Optic
                   Radiation : Analysis with 6- , 12- , 40- , and 81-}},
  Journal        = {Ajnr. American Journal Of Neuroradiology},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Miki et al. - 2007 - Diffusion
                   Tensor Fiber Tractography of the Optic Radiation
                   Analysis with 6- , 12- , 40- , and 81-.pdf:pdf},
  year           = 2007
}

@Article{Glasser2008,
  Author         = {Glasser, MF and Rilling, JK},
  Title          = {{DTI tractography of the human brain's language
                   pathways}},
  Journal        = {Cerebral Cortex},
  url            = {http://cercor.oxfordjournals.org/cgi/content/abstract/bhn011},
  year           = 2008
}

@Article{Wedeen,
  Author         = {Wedeen, V and Wang, R and Schmahmann, J and Benner, T
                   and Tseng, W and Dai, G and Pandya, D and Hagmann, P
                   and D\^a arceuil, H and A},
  Title          = {{de Crespigny, "Diffusion spectrum magnetic resonance
                   imaging (dsi) tractography of crossing fibers,"}},
  Journal        = {NeuroImage},
  Volume         = {vol},
  Pages          = {41no4pp1267--1277},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wedeen et al. - Unknown - de
                   Crespigny, Diffusion spectrum magnetic resonance
                   imaging (dsi) tractography of crossing fibers,.pdf:pdf}
}

@Article{Nannen2003c,
  Author         = {Nannen, Volker and Groningen, Rijksuniversiteit},
  Title          = {{The Paradox of Overfitting}},
  Journal        = {Artificial Intelligence},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Nannen, Groningen - 2003 - The
                   Paradox of Overfitting.pdf:pdf},
  year           = 2003
}

@Article{Chen2009,
  Author         = {Chen, Wei and Ding, Zi'ang and Zhang, Song and
                   MacKay-Brandt, Anna and Correia, Stephen and Qu, Huamin
                   and Crow, John Allen and Tate, David F and Yan,
                   Zhicheng and Peng, Qunsheng},
  Title          = {{A novel interface for interactive exploration of DTI
                   fibers.}},
  Journal        = {IEEE transactions on visualization and computer
                   graphics},
  Volume         = {15},
  Number         = {6},
  Pages          = {1433--40},
  abstract       = {Visual exploration is essential to the visualization
                   and analysis of densely sampled 3D DTI fibers in
                   biological specimens, due to the high geometric,
                   spatial, and anatomical complexity of fiber tracts.
                   Previous methods for DTI fiber visualization use
                   zooming, color-mapping, selection, and abstraction to
                   deliver the characteristics of the fibers. However,
                   these schemes mainly focus on the optimization of
                   visualization in the 3D space where cluttering and
                   occlusion make grasping even a few thousand fibers
                   difficult. This paper introduces a novel interaction
                   method that augments the 3D visualization with a 2D
                   representation containing a low-dimensional embedding
                   of the DTI fibers. This embedding preserves the
                   relationship between the fibers and removes the visual
                   clutter that is inherent in 3D renderings of the
                   fibers. This new interface allows the user to
                   manipulate the DTI fibers as both 3D curves and 2D
                   embedded points and easily compare or validate his or
                   her results in both domains. The implementation of the
                   framework is GPU based to achieve real-time
                   interaction. The framework was applied to several
                   tasks, and the results show that our method reduces the
                   user's workload in recognizing 3D DTI fibers and
                   permits quick and accurate DTI fiber selection.},
  doi            = {10.1109/TVCG.2009.112},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Chen et al. - 2009 - A novel
                   interface for interactive exploration of DTI
                   fibers..pdf:pdf},
  issn           = {1077-2626},
  keywords       = {Algorithms,Animals,Brain,Brain: anatomy \&
                   histology,Cluster Analysis,Computer Graphics,Diffusion
                   Magnetic Resonance Imaging,Diffusion Magnetic Resonance
                   Imaging: methods,Heart,Heart: anatomy \&
                   histology,Hindlimb,Models, Biological,Myofibrils,Nerve
                   Fibers,Swine,User-Computer Interface},
  pmid           = {19834218},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19834218},
  year           = 2009
}

@Article{ZLW+03,
  Author         = {Zhai, G. and Lin, W. and Wilber, K. P. and Gerig, G.
                   and Gilmore, J. H.},
  Title          = {Comparisons of regional white matter diffusion in
                   healthy neonates and adults performed with a 3.0-{T}
                   head-only {MR} imaging unit.},
  Journal        = {Radiology},
  Volume         = {229},
  Number         = {3},
  Pages          = {673-81},
  abstract       = {PURPOSE: To evaluate the normal brains of adults and
                   neonates for regional and age-related differences in
                   apparent diffusion coefficient (ADC) and fractional
                   anisotropy (FA). MATERIALS AND METHODS: Eight healthy
                   adults and 20 healthy neonates were examined with a
                   3.0-T head-only magnetic resonance (MR) imaging unit by
                   using a single-shot diffusion-tensor sequence. Trace
                   ADC maps, FA maps, directional maps of the putative
                   directions of white matter (WM) tracts, and
                   fiber-tracking maps were obtained. Regions of
                   interest-eight in WM and one in gray matter (GM)-were
                   predefined for the ADC and FA measurements. The Student
                   t test was used to compare FA and ADC between adults
                   and neonates, whereas the Tukey multiple-comparison
                   test was used to compare FA and ADC in different brain
                   regions in the adult and neonate groups. RESULTS: A
                   global elevation in ADC (P <.001) in both GM and WM and
                   a reduction in FA (P <.001) in WM were observed in
                   neonates as compared with these values in adults. In
                   addition, significant regional variations in FA and ADC
                   were observed in both groups. Regional variations in FA
                   and ADC were less remarkable in adults, whereas
                   neonates had consistently higher FA values and lower
                   ADC values in the central WM as compared with these
                   values in the peripheral WM. Fiber tracking revealed
                   only major WM tracts in the neonates but fibers
                   extending to the peripheral WM in the adults.
                   CONCLUSION: There were regional differences in FA and
                   ADC values in the neonates; such variations were less
                   remarkable in the adults.},
  authoraddress  = {Department of Biomedical Engineering, University of
                   North Carolina at Chapel Hill, CB \#7515, Chapel Hill,
                   NC 27599, USA.},
  keywords       = {Adult ; Age Factors ; Brain/*anatomy \& histology ;
                   Diffusion Magnetic Resonance Imaging/*instrumentation ;
                   Humans ; Infant, Newborn ; ROC Curve},
  language       = {eng},
  medline-aid    = {10.1148/radiol.2293021462 [doi] ; 229/3/673 [pii]},
  medline-crdt   = {2003/12/06 05:00},
  medline-da     = {20031205},
  medline-dcom   = {20040112},
  medline-edat   = {2003/12/06 05:00},
  medline-fau    = {Zhai, Guihua ; Lin, Weili ; Wilber, Kathy P ; Gerig,
                   Guido ; Gilmore, John H},
  medline-gr     = {HD03110/HD/NICHD NIH HHS/United States ; MH
                   33127/MH/NIMH NIH HHS/United States ; R01 NS
                   37312/NS/NINDS NIH HHS/United States},
  medline-is     = {0033-8419 (Print)},
  medline-jid    = {0401260},
  medline-jt     = {Radiology},
  medline-lr     = {20071114},
  medline-mhda   = {2004/01/13 05:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {14657305},
  medline-pst    = {ppublish},
  medline-pt     = {Comparative Study ; Journal Article ; Research
                   Support, U.S. Gov't, P.H.S.},
  medline-sb     = {AIM ; IM},
  medline-so     = {Radiology. 2003 Dec;229(3):673-81.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=14657305},
  year           = 2003
}

@Article{Voineskos_Neuroimage09,
  Author         = {Voineskos, A. N. and O'Donnell, L. J. and Lobaugh, N.
                   J. and Markant, D. and Ameis, S. H. and Niethammer, M.
                   and Mulsant, B. H. and Pollock, B. G. and Kennedy, J.
                   L. and Westin, C. F. and Shenton, M. E.},
  Title          = {Quantitative examination of a novel clustering method
                   using magnetic resonance diffusion tensor tractography.},
  Journal        = {Neuroimage},
  Volume         = {45},
  Number         = {2},
  Pages          = {370-6},
  abstract       = {MR diffusion tensor imaging (DTI) can measure and
                   visualize organization of white matter fibre tracts in
                   vivo. DTI is a relatively new imaging technique, and
                   new tools developed for quantifying fibre tracts
                   require evaluation. The purpose of this study was to
                   compare the reliability of a novel clustering approach
                   with a multiple region of interest (MROI) approach in
                   both healthy and disease (schizophrenia) populations.
                   DTI images were acquired in 20 participants (n=10
                   patients with schizophrenia: 56+/-15 years; n=10
                   controls: 51+/-20 years) (1.5 T GE system) with
                   diffusion gradients applied in 23 non-collinear
                   directions, repeated three times. Whole brain seeding
                   and creation of fibre tracts were then performed.
                   Interrater reliability of the clustering approach, and
                   the MROI approach, were each evaluated and the methods
                   compared. There was high spatial (voxel-based)
                   agreement within and between the clustering and MROI
                   methods. Fractional anisotropy, trace, and radial and
                   axial diffusivity values showed high intraclass
                   correlation (p<0.001 for all tracts) for each approach.
                   Differences in scalar indices of diffusion between the
                   clustering and MROI approach were minimal. The
                   excellent interrater reliability of the clustering
                   method and high agreement with the MROI method,
                   quantitatively and spatially, indicates that the
                   clustering method can be used with confidence. The
                   clustering method avoids biases of ROI drawing and
                   placement, and, not limited by a priori predictions,
                   may be a more robust and efficient way to identify and
                   measure white matter tracts of interest.},
  authoraddress  = {Geriatric Mental Health Program, Centre for Addiction
                   and Mental Health, Department of Psychiatry, University
                   of Toronto, Canada.},
  language       = {eng},
  medline-aid    = {S1053-8119(08)01281-0 [pii] ;
                   10.1016/j.neuroimage.2008.12.028 [doi]},
  medline-crdt   = {2009/01/23 09:00},
  medline-da     = {20090223},
  medline-dep    = {20081229},
  medline-edat   = {2009/01/23 09:00},
  medline-fau    = {Voineskos, Aristotle N ; O'Donnell, Lauren J ;
                   Lobaugh, Nancy J ; Markant, Doug ; Ameis, Stephanie H ;
                   Niethammer, Marc ; Mulsant, Benoit H ; Pollock, Bruce G
                   ; Kennedy, James L ; Westin, Carl Fredrik ; Shenton,
                   Martha E},
  medline-gr     = {1P50 MH08272/MH/NIMH NIH HHS/United States ; P41
                   RR13218/RR/NCRR NIH HHS/United States ; R01 MH
                   50740/MH/NIMH NIH HHS/United States ; R01
                   MH074794/MH/NIMH NIH HHS/United States ;
                   U41-RR019703/RR/NCRR NIH HHS/United States ;
                   U54GM072977-01/GM/NIGMS NIH HHS/United States},
  medline-is     = {1095-9572 (Electronic)},
  medline-jid    = {9215515},
  medline-jt     = {NeuroImage},
  medline-mhda   = {2009/01/23 09:00},
  medline-mid    = {NIHMS85018},
  medline-oid    = {NLM: NIHMS85018 [Available on 04/01/10] ; NLM:
                   PMC2646811 [Available on 04/01/10]},
  medline-own    = {NLM},
  medline-phst   = {2008/08/25 [received] ; 2008/11/05 [revised] ;
                   2008/12/08 [accepted] ; 2008/12/29 [aheadofprint]},
  medline-pl     = {United States},
  medline-pmc    = {PMC2646811},
  medline-pmcr   = {2010/04/01},
  medline-pmid   = {19159690},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural
                   ; Research Support, Non-U.S. Gov't ; Research Support,
                   U.S. Gov't, Non-P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Neuroimage. 2009 Apr 1;45(2):370-6. Epub 2008 Dec 29.},
  medline-stat   = {In-Process},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=19159690},
  year           = 2009
}

@Article{Hagmann2006Radiographics,
  Author         = {Hagmann, Patric and Jonasson, Lisa and Maeder,
                   Philippe and Thiran, Jean-Philippe and Wedeen, Van J.
                   and Meuli, Reto},
  Title          = {Understanding diffusion \{{M}{R}\} imaging techniques:
                   \{{F}\}rom scalar diffusion-weighted imaging to
                   diffusion tensor imaging and beyond},
  Journal        = {Radiographics},
  Volume         = {26},
  Number         = {suppl_1},
  Pages          = {S205-223},
  abstract       = {The complex structural organization of the white
                   matter of the brain can be depicted in vivo in great
                   detail with advanced diffusion magnetic resonance (MR)
                   imaging schemes. Diffusion MR imaging techniques are
                   increasingly varied, from the simplest and most
                   commonly used technique-the mapping of apparent
                   diffusion coefficient values-to the more complex, such
                   as diffusion tensor imaging, q-ball imaging, diffusion
                   spectrum imaging, and tractography. The type of
                   structural information obtained differs according to
                   the technique used. To fully understand how diffusion
                   MR imaging works, it is helpful to be familiar with the
                   physical principles of water diffusion in the brain and
                   the conceptual basis of each imaging technique.
                   Knowledge of the technique-specific requirements with
                   regard to hardware and acquisition time, as well as the
                   advantages, limitations, and potential interpretation
                   pitfalls of each technique, is especially useful.},
  doi            = {10.1148/rg.26si065510},
  eprint         = {http://radiographics.rsnajnls.org/cgi/reprint/26/suppl_1/S205.pdf},
  file           = {attachment\:Hagmann2006Radiographics.pdf:attachment\:Hagmann2006Radiographics.pdf:PDF},
  url            = {http://radiographics.rsnajnls.org/cgi/content/abstract/26/suppl_1/S205},
  year           = 2006
}

@Misc{Mendeley2009,
  Author         = {Mendeley},
  Title          = {{Getting Started with Mendeley}},
  address        = {London},
  annote         = {Double click on the entry on the left to view the PDF.},
  booktitle      = {Mendeley Desktop},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mendeley - 2009 - Getting Started
                   with Mendeley.pdf:pdf},
  keywords       = {Mendeley},
  publisher      = {Mendeley Ltd.},
  url            = {http://www.mendeley.com},
  year           = 2009
}

@Article{Schmid2010,
  Author         = {Schmid, Benjamin and Schindelin, Johannes and Cardona,
                   Albert and Longair, Mark and Heisenberg, Martin},
  Title          = {{A high-level 3D visualization API for Java and
                   ImageJ.}},
  Journal        = {BMC bioinformatics},
  Volume         = {11},
  Number         = {1},
  Pages          = {274},
  abstract       = {ABSTRACT: BACKGROUND: Current imaging methods such as
                   Magnetic Resonance Imaging (MRI), Confocal microscopy,
                   Electron Microscopy (EM) or Selective Plane
                   Illumination Microscopy (SPIM) yield three-dimensional
                   (3D) data sets in need of appropriate computational
                   methods for their analysis. The reconstruction,
                   segmentation and registration are best approached from
                   the 3D representation of the data set. RESULTS: Here we
                   present a platform-independent framework based on Java
                   and Java 3D for accelerated rendering of biological
                   images. Our framework is seamlessly integrated into
                   ImageJ, a free image processing package with a vast
                   collection of community-developed biological image
                   analysis tools. Our framework enriches the ImageJ
                   software libraries with methods that greatly reduce the
                   complexity of developing image analysis tools in an
                   interactive 3D visualization environment. In
                   particular, we provide high-level access to volume
                   rendering, volume editing, surface extraction, and
                   image annotation. The ability to rely on a library that
                   removes the low-level details enables concentrating
                   software development efforts on the algorithm
                   implementation parts. CONCLUSIONS: Our framework
                   enables biomedical image software development to be
                   built with 3D visualization capabilities with very
                   little effort. We offer the source code and convenient
                   binary packages along with extensive documentation at
                   http://3dviewer.neurofly.de.},
  doi            = {10.1186/1471-2105-11-274},
  issn           = {1471-2105},
  month          = may,
  pmid           = {20492697},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/20492697},
  year           = 2010
}

@Article{Durrleman2009,
  Author         = {Durrleman, Stanley and Fillard, Pierre and Pennec,
                   Xavier and Trouv\'{e}, Alain and Ayache, Nicholas},
  Title          = {{A statistical model of white matter fiber bundles
                   based on currents.}},
  Journal        = {Information processing in medical imaging :
                   proceedings of the ... conference},
  Volume         = {21},
  Pages          = {114--25},
  abstract       = {The purpose of this paper is to measure the
                   variability of a population of white matter fiber
                   bundles without imposing unrealistic geometrical
                   priors. In this respect, modeling fiber bundles as
                   currents seems particularly relevant, as it gives a
                   metric between bundles which relies neither on point
                   nor on fiber correspondences and which is robust to
                   fiber interruption. First, this metric is included in a
                   diffeomorphic registration scheme which consistently
                   aligns sets of fiber bundles. In particular, we show
                   that aligning directly fiber bundles may solve the
                   aperture problem which appears when fiber mappings are
                   constrained by tensors only. Second, the measure of
                   variability of a population of fiber bundles is based
                   on a statistical model which considers every bundle as
                   a random diffeomorphic deformation of a common template
                   plus a random non-diffeomorphic perturbation. Thus, the
                   variability is decomposed into a geometrical part and a
                   "texture" part. Our results on real data show that both
                   parts may contain interesting anatomical features.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Durrleman et al. - 2009 - A
                   statistical model of white matter fiber bundles based
                   on currents..pdf:pdf},
  issn           = {1011-2499},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   anatomy \& histology,Cluster Analysis,Computer
                   Simulation,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Models, Neurological,Models, Statistical,Nerve
                   Fibers, Myelinated,Nerve Fibers, Myelinated:
                   ultrastructure,Pattern Recognition, Automated,Pattern
                   Recognition, Automated: methods,Reproducibility of
                   Results,Sensitivity and Specificity},
  month          = jan,
  pmid           = {19694257},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19694257},
  year           = 2009
}

@Article{Wedeen2008,
  Author         = {Wedeen, VJ and Wang, RP and Schmahmann, JD and Benner,
                   T},
  Title          = {{\ldots spectrum magnetic resonance imaging (DSI)
                   tractography of crossing fibers}},
  Journal        = {Neuroimage},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wedeen et al. - Unknown - de
                   Crespigny, Diffusion spectrum magnetic resonance
                   imaging (dsi) tractography of crossing fibers,.pdf:pdf},
  url            = {http://linkinghub.elsevier.com/retrieve/pii/S105381190800253X},
  year           = 2008
}

@Article{Sotiras2009,
  Author         = {Sotiras, Aristeidis and Neji, Radhou\`{e}ne and Nikos,
                   Jean-fran\c{c}ois Deux and Mezri, Komodakis},
  Title          = {{Diffusion Tensor Registration Using Probability
                   Kernels and Discrete Optimization}},
  Journal        = {Computer},
  Number         = {May},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Sotiras et al. - 2009 - Diffusion
                   Tensor Registration Using Probability Kernels and
                   Discrete Optimization.pdf:pdf},
  year           = 2009
}

@Article{Basser1994,
  Author         = {Basser, PJ and Mattiello, J and LeBihan, D},
  Title          = {{MR diffusion tensor spectroscopy and imaging}},
  Journal        = {Biophysical journal},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Basser, Mattiello, LeBihan - 1994 -
                   MR diffusion tensor spectroscopy and imaging.pdf:pdf},
  url            = {http://linkinghub.elsevier.com/retrieve/pii/S0006349594807751},
  year           = 1994
}

@Article{wakana2004ftba,
  Author         = {Wakana, S. and Jiang, H. and Nagae-Poetscher, L. and
                   van Zijl, P. and Mori, S.},
  Title          = {Fiber tract-based atlas of human white matter anatomy},
  Journal        = {Radiology},
  Volume         = {230},
  Pages          = {77-87},
  file           = {attachment\:wakana_fiber_tract-based_atlas_2004.pdf:attachment\:wakana_fiber_tract-based_atlas_2004.pdf:PDF},
  publisher      = {RSNA},
  year           = 2004
}

@Article{Joy,
  Author         = {Joy, Kenneth I},
  Title          = {{Numerical Methods for Particle Tracing in Vector
                   Fields}},
  Journal        = {Science},
  Pages          = {1--7},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Joy - Unknown - Numerical Methods
                   for Particle Tracing in Vector Fields.pdf:pdf}
}

@Article{Staempfli2006NeuroImage,
  Author         = {Staempfli, P. and Jaermann, T. and Crelier, G.R. and
                   Kollias, S. and Valavanis, A. and Boesiger, P.},
  Title          = {Resolving fiber crossing using advanced fast marching
                   tractography based on diffusion tensor imaging},
  Journal        = {NeuroImage},
  Volume         = {30},
  Number         = {1},
  Pages          = {110-120},
  abstract       = {Magnetic resonance diffusion tensor tractography is a
                   powerful tool for the non-invasive depiction of the
                   white matter architecture in the human brain. However,
                   due to limitations in the underlying tensor model, the
                   technique is often unable to reconstruct correct
                   trajectories in heterogeneous fiber arrangements, such
                   as axonal crossings. A novel tractography method based
                   on fast marching (FM) is proposed which is capable of
                   resolving fiber crossings and also permits trajectories
                   to branch. It detects heterogeneous fiber arrangements
                   by incorporating information from the entire diffusion
                   tensor. The FM speed function is adapted to the local
                   tensor characteristics, allowing in particular to
                   maintain the front evolution direction in crossing
                   situations. In addition, the FM's discretization error
                   is reduced by increasing the number of considered
                   possible front evolution directions. The performance of
                   the technique is demonstrated in artificial data and in
                   the healthy human brain. Comparisons with standard FM
                   tractography and conventional line propagation
                   algorithms show that, in the presence of interfering
                   structures, the proposed method is more accurate in
                   reconstructing trajectories. The in vivo results
                   illustrate that the elucidated major white matter
                   pathways are consistent with known anatomy and that
                   multiple crossings and tract branching are handled
                   correctly.},
  file           = {attachment\:Staempfli2006NeuroImage.pdf:attachment\:Staempfli2006NeuroImage.pdf:PDF},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4HD8DK8-3/2/c67092fe40d5854eaa7e5e78808d9983},
  year           = 2006
}

@Article{Aksoy2008MRM,
  Author         = {Aksoy, Murat andi Liu, Chunle and Moseley, Michael E.
                   and Bammer, Roland},
  Title          = {Single-Step Nonlinear Diffusion Tensor Estimation in
                   the Presence of Microscopic and Macroscopic Motion},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {59},
  Pages          = {11381150},
  abstract       = {Patient motion can cause serious artifacts in
                   diffusion tensor imaging (DTI), diminishing the
                   reliability of the estimated diffusion tensor
                   information. Studies in this field have so far been
                   limited mainly to the correction of miniscule
                   physiological motion. In order to correct for gross
                   patient motion it is not sufficient to correct for
                   misregistration between successive shots; the change in
                   the diffusion-encoding direction must also be accounted
                   for. This becomes particularly important for multishot
                   sequences, wherebyin the presence of motioneach shot
                   is encoded with a different diffusion weighting. In
                   this study a general mathematical framework to correct
                   for gross patient motion present in a multishot and
                   multicoil DTI scan is presented. A signal model is
                   presented that includes the effect of rotational and
                   translational motion in the patient frame of reference.
                   This model was used to create a nonlinear leastsquares
                   formulation, from which the diffusion tensors were
                   obtained using a nonlinear conjugate gradient
                   algorithm. Applications to both phantom simulations and
                   in vivo studies showed that in the case of gross motion
                   the proposed algorithm performs superiorly compared to
                   conventional methods used for tensor estimation.},
  owner          = {ian},
  timestamp      = {2009.03.04},
  year           = 2008
}

@Article{IturriaMedina2007NeuroImage,
  Author         = {Iturria-Medina, Y. and Canales-Rodr{\'\i}guez, EJ and
                   Melie-Garc{\'\i}a, L. and Vald{\'e}s-Hern{\'a}ndez, PA
                   and Mart{\'\i}nez-Montes, E. and Alem{\'a}n-G{\'o}mez,
                   Y. and S{\'a}nchez-Bornot, J M},
  Title          = {Characterizing brain anatomical connections using
                   diffusion weighted \{{M}{RI}\} and graph theory},
  Journal        = {Neuroimage},
  Volume         = {36},
  Number         = {3},
  Pages          = {645-660},
  abstract       = {A new methodology based on Diffusion Weighted Magnetic
                   Resonance Imaging (DW-MRI) and Graph Theory is
                   presented for characterizing the anatomical connections
                   between brain gray matter areas. In a first step, brain
                   voxels are modeled as nodes of a non-directed graph in
                   which the weight of an arc linking two neighbor nodes
                   is assumed to be proportional to the probability of
                   being connected by nervous fibers. This probability is
                   estimated by means of probabilistic tissue segmentation
                   and intravoxel white matter orientational distribution
                   function, obtained from anatomical MRI and DW-MRI,
                   respectively. A new tractography algorithm for finding
                   white matter routes is also introduced. This algorithm
                   solves the most probable path problem between any two
                   nodes, leading to the assessment of probabilistic brain
                   anatomical connection maps. In a second step, for
                   assessing anatomical connectivity between K gray matter
                   structures, the previous graph is redefined as a K+1
                   partite graph by partitioning the initial nodes set in
                   K non-overlapped gray matter subsets and one subset
                   clustering the remaining nodes. Three different
                   measures are proposed for quantifying anatomical
                   connections between any pair of gray matter subsets:
                   Anatomical Connection Strength (ACS), Anatomical
                   Connection Density (ACD) and Anatomical Connection
                   Probability (ACP). This methodology was applied to both
                   artificial and actual human data. Results show that
                   nervous fiber pathways between some regions of interest
                   were reconstructed correctly. Additionally, mean
                   connectivity maps of ACS, ACD and ACP between 71 gray
                   matter structures for five healthy subjects are
                   presented.},
  file           = {attachment\:IturriaMedina2007NeuroImage.pdf:attachment\:IturriaMedina2007NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2007
}

@Misc{hyvarinen1998fim,
  Author         = {Hyvarinen, A. and Oja, E.},
  Title          = {{The Fast-ICA MATLAB package}},
  year           = 1998
}

@Article{Rosen2008,
  Author         = {Rosen, Bruce},
  Title          = {2 -' ' >7},
  Journal        = {Engineering},
  Number         = {2001},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Rosen - 2008 - 2 -' ' 7.pdf:pdf},
  year           = 2008
}

@PhdThesis{Tuch2002ThesisMIT,
  Author         = {Tuch, D.S.},
  Title          = {Diffusion \{{M}{RI}\} of complex tissue structure},
  School         = {Massachusetts Institute of Technology, Division of
                   Health Sciences and Technology},
  abstract       = {Magnetic resonance diffusion imaging provides an
                   exquisitely sensitive probe of tissue microstructure.
                   Owing to the microscopic length scale of diffusion in
                   biological tissues, diffusion imaging can reveal
                   histological architecture irresolvable by conventional
                   magnetic resonance imaging methods. However, diffusion
                   imaging methods to date have chiefly been based on
                   analytical models of the underlying diffusion process.
                   For example, diffusion tensor imaging assumes
                   homogeneous Gaussian diffusion within each voxel, an
                   assumption which is clearly invalid for the vast
                   majority of the brain at presently achievable voxel
                   resolutions. In this thesis I developed a diffusion
                   imaging method capable of measuring the microscopic
                   diffusion function within each voxel. In contrast to
                   previous approaches to diffusion imaging, the method
                   presented here does not require any assumptions on the
                   underlying diffusion function. The model-independent
                   approach can resolve complex intravoxel tissue
                   structure including fiber crossing and fiber divergence
                   within a single voxel. The method is capable of
                   resolving not only deep white matter intersections, but
                   also composite tissue structure at the cortical margin,
                   and fiber-specific degeneration in neurodegenerative
                   pathology. In sum, the approach can reveal complex
                   intravoxel tissue structure previously thought to be
                   beyond the scope of diffusion imaging methodology.},
  publisher      = {Massachusetts Institute of Technology},
  year           = 2002
}

@Article{Durrleman2009a,
  Author         = {Durrleman, Stanley and Fillard, Pierre and Pennec,
                   Xavier and Trouv\'{e}, Alain and Ayache, Nicholas},
  Title          = {{A statistical model of white matter fiber bundles
                   based on currents.}},
  Journal        = {Information processing in medical imaging :
                   proceedings of the ... conference},
  Volume         = {21},
  Pages          = {114--25},
  abstract       = {The purpose of this paper is to measure the
                   variability of a population of white matter fiber
                   bundles without imposing unrealistic geometrical
                   priors. In this respect, modeling fiber bundles as
                   currents seems particularly relevant, as it gives a
                   metric between bundles which relies neither on point
                   nor on fiber correspondences and which is robust to
                   fiber interruption. First, this metric is included in a
                   diffeomorphic registration scheme which consistently
                   aligns sets of fiber bundles. In particular, we show
                   that aligning directly fiber bundles may solve the
                   aperture problem which appears when fiber mappings are
                   constrained by tensors only. Second, the measure of
                   variability of a population of fiber bundles is based
                   on a statistical model which considers every bundle as
                   a random diffeomorphic deformation of a common template
                   plus a random non-diffeomorphic perturbation. Thus, the
                   variability is decomposed into a geometrical part and a
                   "texture" part. Our results on real data show that both
                   parts may contain interesting anatomical features.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Durrleman et al. - 2009 - A
                   statistical model of white matter fiber bundles based
                   on currents..pdf:pdf},
  issn           = {1011-2499},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   anatomy \& histology,Cluster Analysis,Computer
                   Simulation,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Models, Neurological,Models, Statistical,Nerve
                   Fibers, Myelinated,Nerve Fibers, Myelinated:
                   ultrastructure,Pattern Recognition, Automated,Pattern
                   Recognition, Automated: methods,Reproducibility of
                   Results,Sensitivity and Specificity},
  month          = jan,
  pmid           = {19694257},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19694257},
  year           = 2009
}

@Article{Hill2002,
  Author         = {Hill, Murray},
  Title          = {{McLaren’s Improved Snub Cube and Other New
                   Spherical Designs in Three Dimensions}},
  Journal        = {Sciences-New York},
  Number         = {1},
  arxivid        = {arXiv:math/0207211v1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Hill - 2002 - McLaren’s Improved
                   Snub Cube and Other New Spherical Designs in Three
                   Dimensions.pdf:pdf},
  year           = 2002
}

@Article{zhang2008identifying,
  Author         = {Zhang, S. and Correia, S. and Laidlaw, D.H.},
  Title          = {{Identifying White-Matter Fiber Bundles in DTI Data
                   Using an Automated Proximity-Based Fiber Clustering
                   Method}},
  Journal        = {IEEE transactions on visualization and computer
                   graphics},
  Volume         = {14},
  Number         = {5},
  Pages          = {1044},
  publisher      = {NIH Public Access},
  year           = 2008
}

@Book{einstein1956itb,
  Author         = {Einstein, A.},
  Title          = {Investigations on the {T}heory of the {B}rownian
                   {M}ovement},
  Publisher      = {Dover Publications},
  year           = 1956
}

@Article{Garyfallidis,
  Author         = {Garyfallidis, Eleftherios},
  Title          = {{Diffusion MRI and Tractography Tracks vs Tracts}},
  Journal        = {Sciences-New York},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Garyfallidis - Unknown - Di usion
                   MRI and Tractography Tracks vs Tracts.pdf:pdf}
}

@Article{Tegmark2008,
  Author         = {Tegmark, Max},
  Title          = {{No Title}},
  arxivid        = {arXiv:astro-ph/9610094v1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tegmark - 2008 - No Title.pdf:pdf},
  year           = 2008
}

@Article{wakana2007roq,
  Author         = {Wakana, S. and Caprihan, A. and Panzenboeck, M. M. and
                   Fallon, J.H. and Perry, M. and Gollub, R. L. and Hua,
                   K. and Zhang, J. and Jiang, H. and Dubey, P. and Blitz,
                   A. and van Zijl, P. and Mori, S.},
  Title          = {Reproducibility of quantitative tractography methods
                   applied to cerebral white matter},
  Journal        = {Neuroimage},
  Volume         = {36},
  Pages          = {630-644},
  file           = {attachment\:wakana_reproducibility_2007.pdf:attachment\:wakana_reproducibility_2007.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2007
}

@Article{Dale2009,
  Author         = {Dale, Darren and Droettboom, Michael and Firing, Eric
                   and Hunter, John},
  Title          = {{Matplotlib}},
  Journal        = {Building},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Dale et al. - 2009 -
                   Matplotlib.pdf:pdf},
  year           = 2009
}

@Article{Hasan2007MRI,
  Author         = {Hasan, Khader M.},
  Title          = {A framework for quality control and parameter
                   optimization in diffusion tensor imaging: theoretical
                   analysis and validation},
  Journal        = {Magnetic Resonance Imaging},
  Volume         = {25},
  Pages          = {11961202},
  abstract       = {In this communication, a theoretical framework for
                   quality control and parameter optimization in diffusion
                   tensor imaging (DTI) is presented and validated. The
                   approach is based on the analytical error propagation
                   of the mean diffusivity (Dav) obtained directly from
                   the diffusion-weighted data acquired using rotationally
                   invariant and uniformly distributed icosahedral
                   encoding schemes. The error propagation of a recently
                   described and validated cylindrical tensor model is
                   further extrapolated to the spherical tensor case
                   (diffusion anisotropy 0) to relate analytically the
                   precision error in fractional tensor anisotropy (FA)
                   with the mean diffusion-to-noise ratio (DNR). The
                   approach provided simple analytical and empirical
                   quality control measures for optimization of diffusion
                   parameter space in an isotropic medium that can be
                   tested using widely available water phantoms.},
  file           = {attachment\:Hasan2007MRI.pdf:attachment\:Hasan2007MRI.pdf:PDF},
  year           = 2007
}

@Article{Jian2007bNeuroImage,
  Author         = {Jian, Bing and Vemuri, Baba C. and Ozarslan, Evren and
                   Carney, Paul R. and Mareci, Thomas H.},
  Title          = {Erratum to '\{{A}\} novel tensor distribution model
                   for the diffusion-weighted \{{M}{R}\} signal'},
  Journal        = {NeuroImage},
  Volume         = {37},
  Number         = {2},
  file           = {attachment\:Jian2007bNeuroImage.pdf:attachment\:Jian2007bNeuroImage.pdf:PDF},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4S62RMR-5/2/160bb8aa9bf75adcf495557cec86868f},
  year           = 2007
}

@InProceedings{Haro2008ISBI,
  Author         = {Haro, Gloria and Lenglet, Christophe and Sapiro,
                   Guillermo and Thompson, Paul M.},
  Title          = {On the Non-Uniform Complexity of Brain Connectivity},
  BookTitle      = {5th IEEE International Symposium on Biomedical
                   Imaging: From Nano to Macro},
  Pages          = {FR-P2a (poster)},
  abstract       = {A stratification and manifold learning approach for
                   analyzing High Angular Resolution Diffusion Imaging
                   (HARDI) data is introduced in this paper. HARDI data
                   provides highdimensional signals measuring the complex
                   microstructure of biological tissues, such as the
                   cerebral white matter. We show that these
                   high-dimensional spaces may be understood as unions of
                   manifolds of varying dimensions/complexity and
                   densities. With such analysis, we use clustering to
                   characterize the structural complexity of the white
                   matter. We briefly present the underlying framework and
                   numerical experiments illustrating this original and
                   promising approach.},
  file           = {attachment\:Haro2008ISBI.pdf:attachment\:Haro2008ISBI.pdf:PDF},
  url            = {http://www.ieeexplore.ieee.org/search/freesrchabstract.jsp?arnumber=4541139&isnumber=4540908&punumber=4534844&k2dockey=4541139@ieeecnfs&query=&pos=0},
  year           = 2008
}

@Article{Buchel2004CerebralCortex,
  Author         = {Bchel, C. and Raedler, T. and Sommer, M. and Sach, M.
                   and Weiller, C. and Koch, M. A.},
  Title          = {White matter asymmetry in the human brain: a diffusion
                   tensor \{{M}{RI}\} study},
  Journal        = {Cerebral Cortex},
  Volume         = {14},
  Pages          = {945-951},
  abstract       = {Language ability and handedness are likely to be
                   associated with asymmetry of the cerebral cortex (grey
                   matter) and connectivity (white matter). Grey matter
                   asymmetry, most likely linked to language has been
                   identified with voxel-based morphometry (VBM) using
                   T1-weighted images. Differences in white matter
                   obtained with this technique are less consistent,
                   probably due to the relative insensitivity of the T1
                   contrast to the ultrastructure of white matter.
                   Furthermore, previous VBM studies failed to find
                   differences related to handedness in either grey or
                   white matter. We revisited these issues and
                   investigated two independent groups of subjects with
                   diffusion-tensor imaging (DTI) for asymmetries in white
                   matter composition. Using voxel-based statistical
                   analyses an asymmetry of the arcuate fascicle was
                   observed, with higher fractional anisotropy in the left
                   hemisphere. In addition, we show differences related to
                   handedness in the white matter underneath the
                   precentral gyrus contralateral to the dominant hand.
                   Remarkably, these findings were very robust, even when
                   investigating small groups of subjects. This highlights
                   the sensitivity of DTI for white matter tissue
                   differences, making it an ideal tool to study small
                   patient populations.},
  doi            = {10.1093/cercor/bhh055},
  file           = {attachment\:Buchel2004CerebralCortex.pdf:attachment\:Buchel2004CerebralCortex.pdf:PDF},
  year           = 2004
}

@Article{Ding2003,
  Author         = {Ding, Z and Gore, J and Anderson, A},
  Title          = {{Classification and quantification of neuronal fiber
                   pathways using diffusion tensor MRI}},
  Journal        = {Magn. Reson. Med.},
  Volume         = {49},
  Pages          = {716--721},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ding, Gore, Anderson - 2003 -
                   Classification and quantification of neuronal fiber
                   pathways using diffusion tensor MRI.pdf:pdf},
  year           = 2003
}

@Article{Canales-Rodriguez2009,
  Author         = {Canales-Rodr\'{\i}guez, Erick Jorge and
                   Melie-Garc\'{\i}a, Lester and Iturria-Medina, Yasser},
  Title          = {{Mathematical description of q-space in spherical
                   coordinates: exact q-ball imaging.}},
  Journal        = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  Volume         = {61},
  Number         = {6},
  Pages          = {1350--67},
  abstract       = {Novel methodologies have been recently developed to
                   characterize the microgeometry of neural tissues and
                   porous structures via diffusion MRI data. In line with
                   these previous works, this article provides a detailed
                   mathematical description of q-space in spherical
                   coordinates that helps to highlight the differences and
                   similarities between various related q-space
                   methodologies proposed to date such as q-ball imaging
                   (QBI), diffusion spectrum imaging (DSI), and diffusion
                   orientation transform imaging (DOT). This formulation
                   provides a direct relationship between the orientation
                   distribution function (ODF) and the diffusion data
                   without using any approximation. Under this
                   relationship, the exact ODF can be computed by means of
                   the Radon transform of the radial projection (in
                   q-space) of the diffusion MRI signal. This new
                   methodology, termed exact q-ball imaging (EQBI), was
                   put into practice using an analytical ODF estimation in
                   terms of spherical harmonics that allows obtaining
                   model-free and model-based reconstructions. This work
                   provides a new framework for combining information
                   coming from diffusion data recorded on multiple
                   spherical shells in q-space (hybrid diffusion imaging
                   encoding scheme), which is capable of mapping ODF to a
                   high accuracy. This represents a step toward a more
                   efficient development of diffusion MRI experiments for
                   obtaining better ODF estimates.},
  doi            = {10.1002/mrm.21917},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Canales-Rodr\'{\i}guez,
                   Melie-Garc\'{\i}a, Iturria-Medina - 2009 - Mathematical
                   description of q-space in spherical coordinates exact
                   q-ball imaging..pdf:pdf},
  issn           = {1522-2594},
  keywords       = {Algorithms,Computer Simulation,Diffusion Magnetic
                   Resonance Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Models, Biological,Reproducibility of
                   Results,Sensitivity and Specificity},
  pmid           = {19319889},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19319889},
  year           = 2009
}

@Article{Wakana2007NeuroImage,
  Author         = {Wakana, Setsu and Caprihan, Arvind and Panzenboeck,
                   Martina M. and Fallon, James H. and Perry, Michele and
                   Gollub, Randy L. and Hua, Kegang and Zhang, Jiangyang
                   and Jiang, Hangyi and Dubey, Prachi and Blitz, Ari and
                   {van Zijl}, Peter and Mori, Susumu},
  Title          = {Reproducibility of quantitative tractography methods
                   applied to cerebral white matter},
  Journal        = {NeuroImage},
  Volume         = {36},
  Number         = {1},
  Pages          = {630-644},
  abstract       = {Tractography based on diffusion tensor imaging (DTI)
                   allows visualization of white matter tracts. In this
                   study, protocols to reconstruct eleven major white
                   matter tracts are described. The protocols were refined
                   by several iterations of intra- and inter-rater
                   measurements and identification of sources of
                   variability. Reproducibility of the established
                   protocols was then tested by raters who did not have
                   previous experience in tractography. The protocols were
                   applied to a DTI database of adult normal subjects to
                   study size, fractional anisotropy (FA), and T2 of
                   individual white matter tracts. Distinctive features in
                   FA and T2 were found for the corticospinal tract and
                   callosal fibers. Hemispheric asymmetry was observed for
                   the size of white matter tracts projecting to the
                   temporal lobe. This protocol provides guidelines for
                   reproducible DTI-based tract-specific quantification.},
  file           = {attachment\:Wakana2007NeuroImage.pdf:attachment\:Wakana2007NeuroImage.pdf:PDF},
  publisher      = {Elevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4N9DK04-1/2/6f4d33fa634a866aa907f16091a9bb67},
  year           = 2007
}

@Article{Grady2006,
  Author         = {Grady, Leo},
  Title          = {{Random walks for image segmentation.}},
  Journal        = {IEEE transactions on pattern analysis and machine
                   intelligence},
  Volume         = {28},
  Number         = {11},
  Pages          = {1768--83},
  abstract       = {A novel method is proposed for performing multilabel,
                   interactive image segmentation. Given a small number of
                   pixels with user-defined (or predefined) labels, one
                   can analytically and quickly determine the probability
                   that a random walker starting at each unlabeled pixel
                   will first reach one of the prelabeled pixels. By
                   assigning each pixel to the label for which the
                   greatest probability is calculated, a high-quality
                   image segmentation may be obtained. Theoretical
                   properties of this algorithm are developed along with
                   the corresponding connections to discrete potential
                   theory and electrical circuits. This algorithm is
                   formulated in discrete space (i.e., on a graph) using
                   combinatorial analogues of standard operators and
                   principles from continuous potential theory, allowing
                   it to be applied in arbitrary dimension on arbitrary
                   graphs.},
  doi            = {10.1109/TPAMI.2006.233},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Grady - 2006 - Random walks for
                   image segmentation..pdf:pdf},
  issn           = {0162-8828},
  keywords       = {Algorithms,Artificial Intelligence,Image
                   Enhancement,Image Enhancement: methods,Image
                   Interpretation, Computer-Assisted,Image Interpretation,
                   Computer-Assisted: methods,Information Storage and
                   Retrieval,Information Storage and Retrieval:
                   methods,Models, Statistical,Pattern Recognition,
                   Automated,Pattern Recognition, Automated:
                   methods,Reproducibility of Results,Sensitivity and
                   Specificity},
  month          = nov,
  pmid           = {17063682},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/17063682},
  year           = 2006
}

@Article{Good2001NeuroImage,
  Author         = {Good, Catriona D. and Johnsrude, Ingrid S. and
                   Ashburner, John and Henson, Richard N. A. and Friston,
                   Karl J. and Frackowiak, Richard S. J.},
  Title          = {A Voxel-Based Morphometric Study of Ageing in 465
                   Normal Adult Human Brains},
  Journal        = {NeuroImage},
  Volume         = {14},
  Pages          = {21-36},
  doi            = {10.1006/nimg.2001.0786},
  file           = {attachment\:Good2001NeuroImage.pdf:attachment\:Good2001NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2001
}

@Article{Savadjiev2008,
  Author         = {Savadjiev, Peter and Campbell, Jennifer S W and
                   Descoteaux, Maxime and Deriche, Rachid and Pike, G
                   Bruce and Siddiqi, Kaleem},
  Title          = {{Labeling of ambiguous subvoxel fibre bundle
                   configurations in high angular resolution diffusion
                   MRI.}},
  Journal        = {NeuroImage},
  Volume         = {41},
  Number         = {1},
  Pages          = {58--68},
  abstract       = {Whereas high angular resolution reconstruction methods
                   for diffusion MRI can estimate multiple dominant fibre
                   orientations within a single imaging voxel, they are
                   fundamentally limited in certain cases of complex
                   subvoxel fibre structures, resulting in ambiguous local
                   orientation distribution functions. In this article we
                   address the important problem of disambiguating such
                   complex subvoxel fibre tract configurations, with the
                   purpose of improving the performance of fibre
                   tractography. We do so by extending a curve inference
                   method to distinguish between the cases of curving and
                   fanning fibre bundles using differential geometric
                   estimates in a local neighbourhood. The key benefit of
                   this method is the inference of curves, instead of only
                   fibre orientations, to model the underlying fibre
                   bundles. This in turn allows distinct fibre geometries
                   that contain nearly identical sets of fibre
                   orientations at a voxel, to be distinguished from one
                   another. Experimental results demonstrate the ability
                   of the method to successfully label voxels into one of
                   the above categories and improve the performance of a
                   fibre-tracking algorithm.},
  doi            = {10.1016/j.neuroimage.2008.01.028},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Savadjiev et al. - 2008 - Labeling
                   of ambiguous subvoxel fibre bundle configurations in
                   high angular resolution diffusion MRI..pdf:pdf},
  issn           = {1053-8119},
  keywords       = {Adult,Algorithms,Brain,Brain: anatomy \&
                   histology,Brain: cytology,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Diffusion Magnetic Resonance Imaging:
                   statistics \&,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Image Processing, Computer-Assisted: statistics
                   \& ,Motor Cortex,Motor Cortex: cytology,Motor Cortex:
                   physiology,Nerve Fibers,Nerve Fibers: physiology,Neural
                   Pathways,Neural Pathways: anatomy \& histology,Neural
                   Pathways: cytology,Neural Pathways: physiology},
  pmid           = {18367409},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18367409},
  year           = 2008
}

@Article{olver2010nist,
  Author         = {Olver, F.W. and Lozier, D.W. and Boisvert, R.F. and
                   Clark, C.W.},
  Title          = {{NIST handbook of mathematical functions}},
  publisher      = {Cambridge University Press New York, NY, USA},
  year           = 2010
}

@Article{Cohen-adad,
  Author         = {Cohen-adad, Julien and Mcnab, Jennifer and Gagoski,
                   Borjan and Wedeen, Van and Wald, Lawrence and Hospital,
                   Massachusetts General and States, United},
  Title          = {{OHBM
                   https://www.aievolution.com/hbm1001/index.cfm?...}},
  Pages          = {1--6},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Cohen-adad et al. - Unknown - OHBM
                   httpswww.aievolution.comhbm1001index.cfm....pdf:pdf}
}

@Article{Nannen2003a,
  Author         = {Nannen, Volker},
  Title          = {{A Short Introduction to Model Selection , Kolmogorov
                   Complexity and Minimum Description Length ( MDL )}},
  Journal        = {Complexity},
  Number         = {Mdl},
  Pages          = {1--23},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Nannen - 2003 - A Short Introduction
                   to Model Selection , Kolmogorov Complexity and Minimum
                   Description Length ( MDL ).pdf:pdf},
  year           = 2003
}

@Article{Masutani2003EorJRadiography,
  Author         = {Masutani, Yoshitaka and Aoki, Shigeki and Abe, Osamu
                   and Hayashi, Naoto and Otomo, Kuni},
  Title          = {\{{MR}\} diffusion tensor imaging: recent advance and
                   new techniques for diffusion tensor visualization},
  Journal        = {European Journal of Radiology},
  Volume         = {46},
  Number         = {1},
  Pages          = {53-66},
  abstract       = {Recently, diffusion tensor imaging is attracting the
                   biomedical researchers for its application in depiction
                   of fiber tracts based on diffusion anisotropy. In this
                   paper, we briefly describe the basic theory of
                   diffusion tensor MR imaging, the determination process
                   of diffusion tensor, and the basic concepts of
                   diffusion tensor visualization techniques. Several
                   results of clinical application in our institute are
                   also introduced. Finally, the limitations, advantages
                   and disadvantages of the techniques are discussed for
                   further application of diffusion tensor visualization.},
  file           = {Masutani2003EorJRadiography.pdf:Masutani2003EorJRadiography.pdf:PDF},
  url            = {http://www.sciencedirect.com/science/article/B6T6F-481N1XP-1/2/c1ca22568a2d933c2d6c23d493b98d1b},
  year           = 2003
}

@Article{anwander2007cbp,
  Author         = {Anwander, A. and Tittgemeyer, M. and von Cramon, D Y
                   and Friederici, A D and Knosche, T R},
  Title          = {{Connectivity-Based Parcellation of {B}roca's {A}rea}},
  Journal        = {Cerebral Cortex},
  Volume         = {17},
  Number         = {4},
  Pages          = {816},
  file           = {attachment\:anwander_dti_broca_parcellation_2007.pdf:attachment\:anwander_dti_broca_parcellation_2007.pdf:PDF},
  publisher      = {Oxford Univ Press},
  year           = 2007
}

@Article{Hermoye2006NeuroImage,
  Author         = {Hermoye, Laurent and Saint-Martin, Christine and
                   Cosnard, Guy and Lee, Seung-Koo and Kim, Jinna and
                   Nassogne, Marie-Cecile and Menten, Renaud and Clapuyt,
                   Philippe and Donohue, Pamela K. and Hua, Kegang and
                   Wakana, Setsu and Jiang, Hangyi and {van Zijl}, Peter
                   C.M. and Mori, Susumu},
  Title          = {Pediatric diffusion tensor imaging: Normal database
                   and observation of the white matter maturation in early
                   childhood},
  Journal        = {NeuroImage},
  Volume         = {29},
  Number         = {2},
  Pages          = {493-504},
  abstract       = {Recent advances in diffusion tensor imaging (DTI) have
                   made it possible to reveal white matter anatomy and to
                   detect neurological abnormalities in children. However,
                   the clinical use of this technique is hampered by the
                   lack of a normal standard of reference. The goal of
                   this study was to initiate the establishment of a
                   database of DTI images in children, which can be used
                   as a normal standard of reference for diagnosis of
                   pediatric neurological abnormalities. Seven pediatric
                   volunteers and 23 pediatric patients (age range: 0-54
                   months) referred for clinical MR examinations, but
                   whose brains were shown to be normal, underwent
                   anatomical and DTI acquisitions on a 1.5 T MR scanner.
                   The white matter maturation, as observed on DTI color
                   maps, was described and illustrated. Changes in
                   diffusion fractional anisotropy (FA), average apparent
                   diffusion constant (ADCave), and T2-weighted (T2W)
                   signal intensity were quantified in 12 locations to
                   characterize the anatomical variability of the
                   maturation process. Almost all prominent white matter
                   tracts could be identified from birth, although their
                   anisotropy was often low. The evolution of FA, shape,
                   and size of the white matter tracts comprised generally
                   three phases: rapid changes during the first 12 months;
                   slow modifications during the second year; and relative
                   stability after 24 months. The time courses of FA,
                   ADCave, and T2W signal intensity confirmed our visual
                   observations that maturation of the white matter and
                   the normality of its architecture can be assessed with
                   DTI in young children. The database is available online
                   and is expected to foster the use of this promising
                   technique in the diagnosis of pediatric pathologies.},
  file           = {attachment\:Hermoye2006NeuroImage.pdf:attachment\:Hermoye2006NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4H6GPNP-1/2/36429532df681a3d26bc67f5f3f8e9d9},
  year           = 2006
}

@Article{Lee2007,
  Author         = {Lee, Jae-gil and Han, Jiawei},
  Title          = {{Trajectory Clustering : A Partition-and-Group
                   Framework ∗}},
  Journal        = {Group},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Lee, Han - 2007 - Trajectory
                   Clustering A Partition-and-Group Framework ∗.pdf:pdf},
  keywords       = {a number of clustering,age processing,algorithms have
                   been,and im-,data analysis,density-based
                   clustering,market research,mdl
                   principle,partition-and-group framework,pattern
                   recognition,tering,trajectory clus-},
  year           = 2007
}

@Article{BPP+00,
  Author         = {Basser, P. J. and Pajevic, S. and Pierpaoli, C. and
                   Duda, J. and Aldroubi, A.},
  Title          = {In vivo fiber tractography using {DT}-{MRI} data.},
  Journal        = {Magn Reson Med},
  Volume         = {44},
  Number         = {4},
  Pages          = {625-32},
  abstract       = {Fiber tract trajectories in coherently organized brain
                   white matter pathways were computed from in vivo
                   diffusion tensor magnetic resonance imaging (DT-MRI)
                   data. First, a continuous diffusion tensor field is
                   constructed from this discrete, noisy, measured DT-MRI
                   data. Then a Frenet equation, describing the evolution
                   of a fiber tract, was solved. This approach was
                   validated using synthesized, noisy DT-MRI data. Corpus
                   callosum and pyramidal tract trajectories were
                   constructed and found to be consistent with known
                   anatomy. The method's reliability, however, degrades
                   where the distribution of fiber tract directions is
                   nonuniform. Moreover, background noise in
                   diffusion-weighted MRIs can cause a computed trajectory
                   to hop from tract to tract. Still, this method can
                   provide quantitative information with which to
                   visualize and study connectivity and continuity of
                   neural pathways in the central and peripheral nervous
                   systems in vivo, and holds promise for elucidating
                   architectural features in other fibrous tissues and
                   ordered media.},
  authoraddress  = {Section on Tissue Biophysics and Biomimetics, NICHD,
                   Bethesda, Maryland 20892-5772, USA.
                   pjbasser@helix.nih.gov},
  keywords       = {Artifacts ; Brain/*anatomy \& histology ; Humans ;
                   Image Processing, Computer-Assisted ; *Magnetic
                   Resonance Imaging/methods ; Nerve Fibers},
  language       = {eng},
  medline-aid    = {10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O
                   [pii]},
  medline-crdt   = {2000/10/12 11:00},
  medline-da     = {20001103},
  medline-dcom   = {20001103},
  medline-edat   = {2000/10/12 11:00},
  medline-fau    = {Basser, P J ; Pajevic, S ; Pierpaoli, C ; Duda, J ;
                   Aldroubi, A},
  medline-is     = {0740-3194 (Print)},
  medline-jid    = {8505245},
  medline-jt     = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  medline-lr     = {20061115},
  medline-mhda   = {2001/02/28 10:01},
  medline-own    = {NLM},
  medline-pl     = {UNITED STATES},
  medline-pmid   = {11025519},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, U.S. Gov't,
                   Non-P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Magn Reson Med. 2000 Oct;44(4):625-32.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=11025519},
  year           = 2000
}

@Article{O'Donnell2007,
  Author         = {O'Donnell, Lauren J and Westin, Carl-Fredrik and
                   Golby, Alexandra J},
  Title          = {{Tract-based morphometry.}},
  Journal        = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  Volume         = {10},
  Number         = {Pt 2},
  Pages          = {161--8},
  abstract       = {Multisubject statistical analyses of diffusion tensor
                   images in regions of specific white matter tracts have
                   commonly measured only the mean value of a scalar
                   invariant such as the fractional anisotropy (FA),
                   ignoring the spatial variation of FA along the length
                   of fiber tracts. We propose to instead perform
                   tract-based morphometry (TBM), or the statistical
                   analysis of diffusion MRI data in an anatomical
                   tract-based coordinate system. We present a method for
                   automatic generation of white matter tract arc length
                   parameterizations, based on learning a fiber bundle
                   model from tractography from multiple subjects. Our
                   tract-based coordinate system enables TBM for the
                   detection of white matter differences in groups of
                   subjects. We present example TBM results from a study
                   of interhemispheric differences in FA.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/O'Donnell, Westin, Golby - 2007 -
                   Tract-based morphometry..pdf:pdf},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   cytology,Cluster Analysis,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Nerve Fibers, Myelinated,Nerve Fibers,
                   Myelinated: ultrastructure,Neural Pathways,Neural
                   Pathways: cytology,Pattern Recognition,
                   Automated,Pattern Recognition, Automated:
                   methods,Reproducibility of Results,Sensitivity and
                   Specificity},
  month          = jan,
  pmid           = {18044565},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19154790},
  year           = 2007
}

@Article{Correia2009a,
  Author         = {Correia, Stephen and Lee, Stephanie Y and Voorn, Thom
                   and Tate, David F and Paul, Robert H and Salloway,
                   Stephen P and Malloy, Paul F and Laidlaw, David H},
  Title          = {{NIH Public Access}},
  Journal        = {Water},
  Volume         = {42},
  Number         = {2},
  Pages          = {568--581},
  doi            = {10.1016/j.neuroimage.2008.05.022.Quantitative},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Correia et al. - 2009 - NIH Public
                   Access.pdf:pdf},
  year           = 2009
}

@Article{toosy2004cfs,
  Author         = {Toosy, A. T. and Ciccarelli, O. and Parker, G.J.M. and
                   Wheeler-Kingshott, C. A. M. and Miller, D. H. and
                   Thompson, A. J.},
  Title          = {Characterizing function--structure relationships in
                   the human visual system with functional \{{M}{RI}\} and
                   diffusion tensor imaging},
  Journal        = {Neuroimage},
  Volume         = {21},
  Number         = {4},
  Pages          = {1452--1463},
  file           = {attachment\:toosy_visual_fmri_dti_2003.pdf:attachment\:toosy_visual_fmri_dti_2003.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2004
}

@Article{Descoteaux2007,
  Author         = {Descoteaux, M and Angelino, E and Fitzgibbons, S and
                   Deriche, R},
  Title          = {{Regularized, fast, and robust analytical q-ball
                   imaging}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {vol},
  Pages          = {58no3pp497--510},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Descoteaux et al. - 2007 -
                   Regularized, fast, and robust analytical q-ball
                   imaging.pdf:pdf},
  year           = 2007
}

@Article{Zvitia2010a,
  Author         = {Zvitia, Orly and Mayer, Arnaldo and Shadmi, Ran and
                   Miron, Shmuel and Greenspan, Hayit K},
  Title          = {{Co-registration of white matter tractographies by
                   adaptive-mean-shift and Gaussian mixture modeling.}},
  Journal        = {IEEE transactions on medical imaging},
  Volume         = {29},
  Number         = {1},
  Pages          = {132--45},
  abstract       = {In this paper, we present a robust approach to the
                   registration of white matter tractographies extracted
                   from diffusion tensor-magnetic resonance imaging scans.
                   The fibers are projected into a high dimensional
                   feature space based on the sequence of their 3-D
                   coordinates. Adaptive mean-shift clustering is applied
                   to extract a compact set of representative fiber-modes
                   (FM). Each FM is assigned to a multivariate Gaussian
                   distribution according to its population thereby
                   leading to a Gaussian mixture model (GMM)
                   representation for the entire set of fibers. The
                   registration between two fiber sets is treated as the
                   alignment of two GMMs and is performed by maximizing
                   their correlation ratio. A nine-parameters affine
                   transform is recovered and eventually refined to a
                   twelve-parameters affine transform using an innovative
                   mean-shift based registration refinement scheme
                   presented in this paper. The validation of the
                   algorithm on synthetic intrasubject data demonstrates
                   its robustness to interrupted and deviating fiber
                   artifacts as well as outliers. Using real intrasubject
                   data, a comparison is conducted to other intensity
                   based and fiber-based registration algorithms,
                   demonstrating competitive results. An option for
                   tracking-in-time, on specific white matter fiber
                   tracts, is also demonstrated on the real data.},
  doi            = {10.1109/TMI.2009.2029097},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Zvitia et al. - 2010 -
                   Co-registration of white matter tractographies by
                   adaptive-mean-shift and Gaussian mixture
                   modeling.(2).pdf:pdf},
  issn           = {1558-0062},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Cluster
                   Analysis,Diffusion Tensor Imaging,Diffusion Tensor
                   Imaging: methods,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Models, Neurological,Normal
                   Distribution,Reproducibility of Results},
  month          = jan,
  pmid           = {19709970},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19709970},
  year           = 2010
}

@Article{Mobbs2009,
  Author         = {Mobbs, Dean and Yu, Rongjun and Meyer, Marcel and
                   Passamonti, Luca and Seymour, Ben and Calder, Andrew J
                   and Schweizer, Susanne and Frith, Chris D and
                   Dalgleish, Tim},
  Title          = {{A key role for similarity in vicarious reward.}},
  Journal        = {Science (New York, N.Y.)},
  Volume         = {324},
  Number         = {5929},
  Pages          = {900},
  abstract       = {Humans appear to have an inherent prosocial tendency
                   toward one another in that we often take pleasure in
                   seeing others succeed. This fact is almost certainly
                   exploited by game shows, yet why watching others win
                   elicits a pleasurable vicarious rewarding feeling in
                   the absence of personal economic gain is unclear. One
                   explanation is that game shows use contestants who have
                   similarities to the viewing population, thereby
                   kindling kin-motivated responses (for example,
                   prosocial behavior). Using a game show-inspired
                   paradigm, we show that the interactions between the
                   ventral striatum and anterior cingulate cortex subserve
                   the modulation of vicarious reward by similarity,
                   respectively. Our results support studies showing that
                   similarity acts as a proximate neurobiological
                   mechanism where prosocial behavior extends to unrelated
                   strangers.},
  doi            = {10.1126/science.1170539},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mobbs et al. - 2009 - A key role for
                   similarity in vicarious reward..pdf:pdf},
  issn           = {1095-9203},
  keywords       = {Adult,Basal Ganglia,Basal Ganglia: physiology,Brain
                   Mapping,Empathy,Female,Games, Experimental,Gyrus
                   Cinguli,Gyrus Cinguli: physiology,Humans,Magnetic
                   Resonance Imaging,Male,Prefrontal Cortex,Prefrontal
                   Cortex: physiology,Reward,Self Concept,Social
                   Behavior,Social Desirability,Young Adult},
  month          = may,
  pmid           = {19443777},
  url            = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2839480\&tool=pmcentrez\&rendertype=abstract},
  year           = 2009
}

@Article{Nannen2003b,
  Author         = {Nannen, Volker},
  Title          = {{A Short Introduction to Kolmogorov Complexity}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Nannen - 2003 - A Short Introduction
                   to Kolmogorov Complexity.pdf:pdf},
  year           = 2003
}

@conference{Auerbach2004ISMRM,
  author         = {Auerbach, E. J. and Ugurbil, K.},
  journal        = {Proc. Intl. Soc. Mag. Reson. Med.},
  owner          = {ian},
  timestamp      = {2009.03.04},
  title          = {Improvement in Diffusion MRI at 3T and Beyond with the
                   Twice-Refocused Adiabatic Spin Echo (TRASE) Sequence},
  year           = 2004
}

@Article{sherbondy2006mma,
  Author         = {Sherbondy, AJ and Akers, DL and Dougherty, RF and
                   Ben-Shachar, M. and Napel, S. and Wandell, BA},
  Title          = {{MetroTrac: A metropolis algorithm for probabilistic
                   tractography}},
  Journal        = {Human Brain Mapping, Florence},
  year           = 2006
}

@InProceedings{bjornemoMICCAI02,
  Author         = {M. Bj\"ornemo and A. Brun and R. Kikinis and C.-F.
                   Westin},
  Title          = {Regularized Stochastic White Matter Tractography Using
                   Diffusion Tensor {MRI}},
  BookTitle      = {Fifth International Conference on Medical Image
                   Computing and Computer-Assisted Intervention
                   (MICCAI'02)},
  Pages          = {435--442},
  Address        = {Tokyo, Japan},
  year           = 2002
}

@PhdThesis{maddah_phdthesis2008,
  Author         = {Maddah, M.},
  Title          = {{Quantitative Analysis of Cerebral White Matter
                   Anatomy from Diffusion MRI}},
  School         = {Massachusetts Institute of Technology},
  year           = 2008
}

@Article{iturriamedina2007cba,
  Author         = {Iturria-Medina, Y. and Canales-Rodr{\'\i}guez, EJ and
                   Melie-Garc{\'\i}a, L. and Vald{\'e}s-Hern{\'a}ndez, PA
                   and Mart{\'\i}nez-Montes, E. and Alem{\'a}n-G{\'o}mez,
                   Y. and S{\'a}nchez-Bornot, JM},
  Title          = {Characterizing brain anatomical connections using
                   diffusion weighted \{{M}{RI}\} and graph theory},
  Journal        = {Neuroimage},
  Volume         = {36},
  Number         = {3},
  Pages          = {645--660},
  file           = {attachment\:iturria-medinaet_dti_graph_2007.pdf:attachment\:iturria-medinaet_dti_graph_2007.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2007
}

@Article{Harel2001,
  Author         = {Harel, David and Koren, Yehuda},
  Title          = {{On Clustering Using Random Walks}},
  Pages          = {18--41},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Harel, Koren - 2001 - On Clustering
                   Using Random Walks.pdf:pdf},
  year           = 2001
}

@Article{Kim2009,
  Author         = {Kim, M S and Han, J},
  Title          = {{Chronicle: A two-stage density-based clustering
                   algorithm for dynamic networks}},
  Journal        = {In: Discovery Science.},
  Volume         = {pp},
  Pages          = {152--167},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kim, Han - 2009 - Chronicle A
                   two-stage density-based clustering algorithm for
                   dynamic networks.pdf:pdf},
  year           = 2009
}

@Article{Tsai2007,
  Author         = {Tsai, Andy and Westin, Carl-fredrik and Hero, Alfred O
                   and Willsky, Alan S},
  Title          = {{Fiber tract clustering on manifolds with dual
                   rooted-graphs}},
  Journal        = {in CVPR},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tsai et al. - 2007 - Fiber tract
                   clustering on manifolds with dual rooted-graphs.pdf:pdf},
  year           = 2007
}

@Article{Staempfli2008NeuroImage,
  Author         = {Staempfli, P. and Reischauer, C. and Jaermann, T. and
                   Valavanis, A. and Kollias, S. and Boesiger, P.},
  Title          = {Combining {fMRI} and {DTI}: A framework for exploring
                   the limits of {fMRI}-guided {DTI} fiber tracking and
                   for verifying {DTI}-based fiber tractography results},
  Journal        = {NeuroImage},
  Volume         = {39},
  Number         = {1},
  Pages          = {119-126},
  abstract       = {A powerful, non-invasive technique for estimating and
                   visualizing white matter tracts in the human brain in
                   vivo is white matter fiber tractography that uses
                   magnetic resonance diffusion tensor imaging. The
                   success of this method depends strongly on the
                   capability of the applied tracking algorithm and the
                   quality of the underlying data set. However, DTI-based
                   fiber tractography still lacks standardized validation.
                   In the present work, a combined fMRI/DTI study was
                   performed, both to develop a setup for verifying fiber
                   tracking results using fMRI-derived functional
                   connections and to explore the limitations of fMRI
                   based DTI fiber tracking. Therefore, a minor fiber
                   bundle that features several fiber crossings and
                   intersections was examined: The striatum and its
                   connections to the primary motor cortex were examined
                   by using two approaches to derive the somatotopic
                   organization of the striatum. First, an fMRI-based
                   somatotopic map of the striatum was reconstructed,
                   based on fMRI activations that were provoked by
                   unilateral motor tasks. Second, fMRI-guided DTI fiber
                   tracking was performed to generate DTI-based
                   somatotopic maps, using a standard line propagation and
                   an advanced fast marching algorithm. The results show
                   that the fiber connections reconstructed by the
                   advanced fast marching algorithm are in good agreement
                   with known anatomy, and that the DTI-revealed
                   somatotopy is similar to the fMRI somatotopy.
                   Furthermore, the study illustrates that the combination
                   of fMRI with DTI can supply additional information in
                   order to choose reasonable seed regions for generating
                   functionally relevant networks and to validate
                   reconstructed fibers.},
  file           = {attachment\:Staempfli2008NeuroImage.pdf:attachment\:Staempfli2008NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4PHSC6C-2/2/dbb7febf8dca292f483c25d800bdf700},
  year           = 2008
}

@Article{Kubicki2006,
  Author         = {Kubicki, M and Shenton, M E},
  Title          = {{A Method for Clustering White Matter}},
  Journal        = {Ajnr. American Journal Of Neuroradiology},
  Number         = {May},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kubicki, Shenton - 2006 - A Method
                   for Clustering White Matter.pdf:pdf},
  year           = 2006
}

@Article{Reese2003MRM,
  Author         = {Reese, T.G. and Heid, O. and Weisskoff, R.M. and
                   Wedeen, V.J.},
  Title          = {Reduction of eddy-current-induced distortion in
                   diffusion MRI using a twice-refocused spin echo},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {49},
  Number         = {1},
  Pages          = {177-182},
  abstract       = {CP: Copyright  2003 Wiley-Liss, Inc. ON: 1522-2594
                   PN: 0740-3194 AD: Department of Radiology,
                   Massachusetts General Hospital, Boston, Massachusetts;
                   Medical Engineering Division, Siemens AG, Erlangen,
                   Germany; Epix Medical Inc., Cambridge, Massachusetts
                   DOI: 10.1002/mrm.10308 US:
                   http://dx.doi.org/10.1002/mrm.10308 AB: Image
                   distortion due to field gradient eddy currents can
                   create image artifacts in diffusion-weighted MR images.
                   These images, acquired by measuring the attenuation of
                   NMR signal due to directionally dependent diffusion,
                   have recently been shown to be useful in the diagnosis
                   and assessment of acute stroke and in mapping of tissue
                   structure. This work presents an improvement on the
                   spin-echo (SE) diffusion sequence that displays less
                   distortion and consequently improves image quality.
                   Adding a second refocusing pulse provides better image
                   quality with less distortion at no cost in scanning
                   efficiency or effectiveness, and allows more flexible
                   diffusion gradient timing. By adjusting the timing of
                   the diffusion gradients, eddy currents with a single
                   exponential decay constant can be nulled, and eddy
                   currents with similar decay constants can be greatly
                   reduced. This new sequence is demonstrated in phantom
                   measurements and in diffusion anisotropy images of
                   normal human brain. Magn Reson Med 49:177-182, 2003. 
                   2003 Wiley-Liss, Inc.},
  owner          = {ian},
  timestamp      = {2009.03.12},
  year           = 2003
}

@Article{Hua2008NeuroImage,
  Author         = {Hua, Kegang and Zhang, Jiangyang and Wakana, Setsu and
                   Jiang, Hangyi and Li, Xin and Reich, Daniel S. and
                   Calabresi, Peter A. and Pekar, James J. and {van Zijl},
                   Peter C.M. and Mori, Susumu},
  Title          = {Tract probability maps in stereotaxic spaces: Analyses
                   of white matter anatomy and tract-specific
                   quantification},
  Journal        = {NeuroImage},
  Volume         = {39},
  Number         = {1},
  Pages          = {336-347},
  abstract       = {Diffusion tensor imaging (DTI) is an exciting new MRI
                   modality that can reveal detailed anatomy of the white
                   matter. DTI also allows us to approximate the 3D
                   trajectories of major white matter bundles. By
                   combining the identified tract coordinates with various
                   types of MR parameter maps, such as T2 and diffusion
                   properties, we can perform tract-specific analysis of
                   these parameters. Unfortunately, 3D tract
                   reconstruction is marred by noise, partial volume
                   effects, and complicated axonal structures.
                   Furthermore, changes in diffusion anisotropy under
                   pathological conditions could alter the results of 3D
                   tract reconstruction. In this study, we created a white
                   matter parcellation atlas based on probabilistic maps
                   of 11 major white matter tracts derived from the DTI
                   data from 28 normal subjects. Using these probabilistic
                   maps, automated tract-specific quantification of
                   fractional anisotropy and mean diffusivity were
                   performed. Excellent correlation was found between the
                   automated and the individual tractography-based
                   results. This tool allows efficient initial screening
                   of the status of multiple white matter tracts. },
  file           = {attachment\:Hua2008NeuroImage.pdf:attachment\:Hua2008NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4PF1WFR-5/2/c08a39189151d2b118cf7f8805fe8e2a},
  year           = 2008
}

@Article{Tuch2002,
  Author         = {Tuch, David S. and Reese, Timothy G. and Wiegell,
                   Mette R. and Makris, Nikos and Belliveau, John W. and
                   Wedeen, Van J.},
  Title          = {{High angular resolution diffusion imaging reveals
                   intravoxel white matter fiber heterogeneity}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {48},
  Number         = {4},
  Pages          = {577--582},
  abstract       = {Magnetic resonance (MR) diffusion tensor imaging (DTI)
                   can resolve the white matter fiber orientation within a
                   voxel provided that the fibers are strongly aligned.
                   However, a given voxel may contain a distribution of
                   fiber orientations due to, for example, intravoxel
                   fiber crossing. The present study sought to test
                   whether a geodesic, high b-value diffusion gradient
                   sampling scheme could resolve multiple fiber
                   orientations within a single voxel. In regions of fiber
                   crossing the diffusion signal exhibited multiple local
                   maxima/minima as a function of diffusion gradient
                   orientation, indicating the presence of multiple
                   intravoxel fiber orientations. The multimodality of the
                   observed diffusion signal precluded the standard tensor
                   reconstruction, so instead the diffusion signal was
                   modeled as arising from a discrete mixture of Gaussian
                   diffusion processes in slow exchange, and the
                   underlying mixture of tensors was solved for using a
                   gradient descent scheme. The multitensor reconstruction
                   resolved multiple intravoxel fiber populations
                   corresponding to known fiber anatomy. Magn Reson Med
                   48:577-582, 2002. � 2002 Wiley-Liss, Inc.},
  doi            = {10.1002/mrm.10268},
  url            = {http://dx.doi.org/10.1002/mrm.10268},
  year           = 2002
}

@Article{Kerkyacharian2007,
  Author         = {Kerkyacharian, G and Petrushev, P and Picard, D and
                   Willer, T},
  Title          = {{Needlet algorithms for estimation in inverse
                   problems}},
  Journal        = {Electron. J. Stat},
  Volume         = {1},
  Pages          = {30--76},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Kerkyacharian et al. - 2007 -
                   Needlet algorithms for estimation in inverse
                   problems.pdf:pdf},
  year           = 2007
}

@Article{Edition,
  Author         = {Edition, Second},
  Title          = {{Statistical Pattern Stas-tical Pattern Recognit ion}},
  Journal        = {Pattern Recognition},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Edition - Unknown - Statistical
                   Pattern Stas-tical Pattern Recognit ion.pdf:pdf}
}

@Article{Sverre2009,
  Author         = {Sverre, Dag},
  Title          = {{Fast numerical computations with Cython}},
  Number         = {SciPy},
  Pages          = {15--22},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Sverre - 2009 - Fast numerical
                   computations with Cython.pdf:pdf},
  year           = 2009
}

@Article{Heim2007ComputationalStatisticsDataAnalysis,
  Author         = {Heim, S. and Fahrmeir, L. and Eilers, P.H.C. and Marx,
                   B.D.},
  Title          = {3D space-varying coefficient models with application
                   to diffusion tensor imaging},
  Journal        = {Computational Statistics \& Data Analysis},
  Volume         = {51},
  Number         = {12},
  Pages          = {6212-6228},
  abstract       = {The present methodological development and the primary
                   application field originate from diffusion tensor
                   imaging (DTI), a powerful nuclear magnetic resonance
                   technique which enables the quantification of
                   microscopical tissue properties. The current analysis
                   framework of separate voxelwise regressions is
                   reformulated as a 3D space-varying coefficient model
                   (SVCM) for the entire set of diffusion tensor images
                   recorded on a 3D voxel grid. The SVCM unifies the
                   three-step cascade of standard data processing
                   (voxelwise regression, smoothing, interpolation) into
                   one framework based on B-spline basis functions.
                   Thereby strength is borrowed from spatially correlated
                   voxels to gain a regularization effect right at the
                   estimation stage. Two SVCM variants are conceptualized:
                   a full tensor product approach and a sequential
                   approximation, rendering the SVCM numerically and
                   computationally feasible even for the huge dimension of
                   the joint model in a realistic setup. A simulation
                   study shows that both approaches outperform the
                   standard method of voxelwise regression with subsequent
                   regularization. Application of the fast sequential
                   method to real DTI data demonstrates the inherent
                   ability to increase the grid resolution by evaluating
                   the incorporated basis functions at intermediate
                   points. The resulting continuous regularized tensor
                   field may serve as basis for multiple applications,
                   yet, ameloriation of local adaptivity is desirable. },
  file           = {attachment\:Heim2007ComputationalStatisticsDataAnalysis.pdf:attachment\:Heim2007ComputationalStatisticsDataAnalysis.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6V8V-4MV74WR-2/2/882882c104fa98632263c151db9fda23},
  year           = 2007
}

@Article{Heller,
  Author         = {Heller, Katherine A},
  Title          = {{Bayesian Hierarchical Clustering}},
  Journal        = {Neuroscience},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Heller - Unknown - Bayesian
                   Hierarchical Clustering.pdf:pdf}
}

@Book{behrens2009diffusion,
  Author         = {Behrens, T.E.J.},
  Title          = {{Diffusion MRI: From Quantitative Measurement to
                   In-vivo Neuroanatomy}},
  Publisher      = {Academic Press},
  year           = 2009
}

@Article{Descoteaux2007a,
  Author         = {Descoteaux, M and Angelino, E and Fitzgibbons, S and
                   Deriche, R},
  Title          = {{Regularized, fast, and robust analytical q-ball
                   imaging}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {vol},
  Pages          = {58no3pp497--510},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Descoteaux et al. - 2007 -
                   Regularized, fast, and robust analytical q-ball
                   imaging.pdf:pdf},
  year           = 2007
}

@Article{PHW03,
  Author         = {Parker, G. J. and Haroon, H. A. and Wheeler-Kingshott,
                   C. A.},
  Title          = {A framework for a streamline-based probabilistic index
                   of connectivity ({PIC}o) using a structural
                   interpretation of {MRI} diffusion measurements.},
  Journal        = {J Magn Reson Imaging},
  Volume         = {18},
  Number         = {2},
  Pages          = {242-54},
  abstract       = {PURPOSE: To establish a general methodology for
                   quantifying streamline-based diffusion fiber tracking
                   methods in terms of probability of connection between
                   points and/or regions. MATERIALS AND METHODS: The
                   commonly used streamline approach is adapted to exploit
                   the uncertainty in the orientation of the principal
                   direction of diffusion defined for each image voxel.
                   Running the streamline process repeatedly using Monte
                   Carlo methods to exploit this inherent uncertainty
                   generates maps of connection probability. Uncertainty
                   is defined by interpreting the shape of the diffusion
                   orientation profile provided by the diffusion tensor in
                   terms of the underlying microstructure. RESULTS: Two
                   candidates for describing the uncertainty in the
                   diffusion tensor are proposed and maps of probability
                   of connection to chosen start points or regions are
                   generated in a number of major tracts. CONCLUSION: The
                   methods presented provide a generic framework for
                   utilizing streamline methods to generate probabilistic
                   maps of connectivity.},
  authoraddress  = {Imaging Science and Biomedical Engineering, University
                   of Manchester, Manchester, UK. geoff.parker@man.ac.uk},
  keywords       = {Anisotropy ; Brain/*anatomy \& histology ; Diffusion ;
                   Diffusion Magnetic Resonance Imaging/*methods ;
                   Echo-Planar Imaging ; Humans ; Models, Statistical ;
                   Monte Carlo Method ; *Probability ; Uncertainty},
  language       = {eng},
  medline-aid    = {10.1002/jmri.10350 [doi]},
  medline-ci     = {Copyright 2003 Wiley-Liss, Inc.},
  medline-crdt   = {2003/07/29 05:00},
  medline-da     = {20030728},
  medline-dcom   = {20040129},
  medline-edat   = {2003/07/29 05:00},
  medline-fau    = {Parker, Geoffrey J M ; Haroon, Hamied A ;
                   Wheeler-Kingshott, Claudia A M},
  medline-is     = {1053-1807 (Print)},
  medline-jid    = {9105850},
  medline-jt     = {Journal of magnetic resonance imaging : JMRI},
  medline-lr     = {20061115},
  medline-mhda   = {2004/01/30 05:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {12884338},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {J Magn Reson Imaging. 2003 Aug;18(2):242-54.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12884338},
  year           = 2003
}

@Article{Garyfallidis2009,
  Author         = {Garyfallidis, Eleftherios},
  Title          = {{Towards an accurate brain tractography using di usion
                   weighted imaging 1 Introduction}},
  Journal        = {Imaging},
  Number         = {June},
  Pages          = {1--25},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Garyfallidis - 2009 - Towards an
                   accurate brain tractography using di usion weighted
                   imaging 1 Introduction.pdf:pdf},
  year           = 2009
}

@Article{Liu2007NeuroImage,
  Author         = {Liu, Tianming and Li, Hai and Wong, Kelvin and Tarokh,
                   Ashley and Guo, Lei and Wong, Stephen T.C.},
  Title          = {Brain tissue segmentation based on \{{D}{TI}\} data},
  Journal        = {NeuroImage},
  Volume         = {15},
  Number         = {1},
  Pages          = {114-123},
  abstract       = {We present a method for automated brain tissue
                   segmentation based on the multi-channel fusion of
                   diffusion tensor imaging (DTI) data. The method is
                   motivated by the evidence that independent tissue
                   segmentation based on DTI parametric images provides
                   complementary information of tissue contrast to the
                   tissue segmentation based on structural MRI data. This
                   has important applications in defining accurate tissue
                   maps when fusing structural data with diffusion data.
                   In the absence of structural data, tissue segmentation
                   based on DTI data provides an alternative means to
                   obtain brain tissue segmentation. Our approach to the
                   tissue segmentation based on DTI data is to classify
                   the brain into two compartments by utilizing the tissue
                   contrast existing in a single channel. Specifically,
                   because the apparent diffusion coefficient (ADC) values
                   in the cerebrospinal fluid (CSF) are more than twice
                   that of gray matter (GM) and white matter (WM), we use
                   ADC images to distinguish CSF and non-CSF tissues.
                   Additionally, fractional anisotropy (FA) images are
                   used to separate WM from non-WM tissues, as highly
                   directional white matter structures have much larger
                   fractional anisotropy values. Moreover, other channels
                   to separate tissue are explored, such as eigenvalues of
                   the tensor, relative anisotropy (RA), and volume ratio
                   (VR). We developed an approach based on the
                   Simultaneous Truth and Performance Level Estimation
                   (STAPLE) algorithm that combines these two-class maps
                   to obtain a complete tissue segmentation map of CSF,
                   GM, and WM. Evaluations are provided to demonstrate the
                   performance of our approach. Experimental results of
                   applying this approach to brain tissue segmentation and
                   deformable registration of DTI data and spoiled
                   gradient-echo (SPGR) data are also provided.},
  file           = {attachment\:Liu2007NeuroImage.pdf:attachment\:Liu2007NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4P61N6N-3/2/a1e3c8c3d22d6c80fa4693813e380a76},
  year           = 2007
}

@Article{Alexander2007Neurotherapeutics,
  Author         = {Alexander, Andrew L. and Lee, Jee Eun and Lazar,
                   Mariana and Field, Aaron S.},
  Title          = {Diffusion Tensor Imaging of the Brain},
  Journal        = {Neurotherapeutics},
  Volume         = {4},
  Number         = {3},
  Pages          = {316-329},
  abstract       = {Diffusion tensor imaging (DTI) is a promising method
                   for characterizing microstructural changes or
                   differences with neuropathology and treatment. The
                   diffusion tensor may be used to characterize the
                   magnitude, the degree of anisotropy, and the
                   orientation of directional diffusion. This review
                   addresses the biological mechanisms, acquisition, and
                   analysis of DTI measurements. The relationships between
                   DTI measures and white matter pathologic features
                   (e.g., ischemia, myelination, axonal damage,
                   inflammation, and edema) are summarized. Applications
                   of DTI to tissue characterization in neurotherapeutic
                   applications are reviewed. The interpretations of
                   common DTI measures (mean diffusivity, MD; fractional
                   anisotropy, FA; radial diffusivity, $D_r$; and axial
                   diffusivity, $D_a$) are discussed. In particular, FA is
                   highly sensitive to microstructural changes, but not
                   very specific to the type of changes (e.g., radial or
                   axial). To maximize the specificity and better
                   characterize the tissue microstructure, future studies
                   should use multiple diffusion tensor measures (e.g., MD
                   and FA, or $D_a$ and $D_r$).},
  doi            = {10.1016/j.nurt.2007.05.011},
  file           = {attachment\:Alexander2007Neurotherapeutics.pdf:attachment\:Alexander2007Neurotherapeutics.pdf:PDF},
  year           = 2007
}

@Article{canalesrodriguez2009mdq,
  Author         = {Canales-Rodr{\'\i}guez, E.J. and Melie-Garc{\'\i}a, L.
                   and Iturria-Medina, Y. and Center, C.N.},
  Title          = {{Mathematical description of q-space in spherical
                   coordinates: Exact q-ball imaging.}},
  Journal        = {Magnetic resonance in medicine: official journal of
                   the Society of Magnetic Resonance in Medicine/Society
                   of Magnetic Resonance in Medicine},
  year           = 2009
}

@Article{Yen2009,
  Author         = {Yen, Luh and Fouss, Francois and Decaestecker,
                   Christine and Francq, Pascal and Saerens, Marco},
  Title          = {{Graph nodes clustering with the sigmoid commute-time
                   kernel: A comparative study}},
  Journal        = {Data \& Knowledge Engineering},
  Volume         = {68},
  Number         = {3},
  Pages          = {338--361},
  doi            = {10.1016/j.datak.2008.10.006},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Yen et al. - 2009 - Graph nodes
                   clustering with the sigmoid commute-time kernel A
                   comparative study(2).pdf:pdf},
  issn           = {0169023X},
  publisher      = {Elsevier B.V.},
  url            = {http://linkinghub.elsevier.com/retrieve/pii/S0169023X0800147X},
  year           = 2009
}

@Article{Hartley,
  Author         = {Hartley, Richard and Zisserman, Andrew},
  Title          = {{in computervision Multiple View Geometry in Computer
                   Vision}}
}

@Article{Oishi2008NeuroImage,
  Author         = {Oishi, Kenichi and Zilles, Karl and Amunts, Katrin and
                   Faria, Andreia and Jiang, Hangyi and Li, Xin and
                   Akhter, Kazi and Hua, Kegang and Woods, Roger and Toga,
                   Arthur W. and Pike, G. Bruce and Rosa-Neto, Pedro and
                   Evans, Alan and Zhang, Jiangyang and Huang, Hao and
                   Miller, Michael I. and {van Zijl}, Peter C. M. and
                   Mazziotta, John and Mori, Susumu},
  Title          = {Human brain white matter atlas: Identification and
                   assignment of common anatomical structures in
                   superficial white matter},
  Journal        = {NeuroImage},
  Volume         = {in press},
  abstract       = {Structural delineation and assignment are the
                   fundamental steps in understanding the anatomy of the
                   human brain. The white matter has been structurally
                   defined in the past only at its core regions (deep
                   white matter). However, the most peripheral white
                   matter areas, which are interleaved between the cortex
                   and the deep white matter, have lacked clear anatomical
                   definitions and parcellations. We used axonal fiber
                   alignment information from diffusion tensor imaging
                   (DTI) to delineate the peripheral white matter, and
                   investigated its relationship with the cortex and the
                   deep white matter. Using DTI data from 81 healthy
                   subjects, we identified nine common, blade-like
                   anatomical regions, which were further parcellated into
                   21 subregions based on the cortical anatomy. Four short
                   association fiber tracts connecting adjacent gyri
                   (U-fibers) were also identified reproducibly among the
                   healthy population. We anticipate that this atlas will
                   be useful resource for atlas-based white matter
                   anatomical studies.},
  file           = {attachment\:Oishi2008NeuroImage.pdf:attachment\:Oishi2008NeuroImage.pdf:PDF},
  year           = 2008
}

@Article{Jianu2009,
  Author         = {Jianu, Radu and Demiralp, Cağatay and Laidlaw, David
                   H},
  Title          = {{Exploring 3D DTI fiber tracts with linked 2D
                   representations.}},
  Journal        = {IEEE transactions on visualization and computer
                   graphics},
  Volume         = {15},
  Number         = {6},
  Pages          = {1449--56},
  abstract       = {We present a visual exploration paradigm that
                   facilitates navigation through complex fiber tracts by
                   combining traditional 3D model viewing with lower
                   dimensional representations. To this end, we create
                   standard streamtube models along with two
                   two-dimensional representations, an embedding in the
                   plane and a hierarchical clustering tree, for a given
                   set of fiber tracts. We then link these three
                   representations using both interaction and color
                   obtained by embedding fiber tracts into a perceptually
                   uniform color space. We describe an anecdotal
                   evaluation with neuroscientists to assess the
                   usefulness of our method in exploring anatomical and
                   functional structures in the brain. Expert feedback
                   indicates that, while a standalone clinical use of the
                   proposed method would require anatomical landmarks in
                   the lower dimensional representations, the approach
                   would be particularly useful in accelerating tract
                   bundle selection. Results also suggest that combining
                   traditional 3D model viewing with lower dimensional
                   representations can ease navigation through the complex
                   fiber tract models, improving exploration of the
                   connectivity in the brain.},
  doi            = {10.1109/TVCG.2009.141},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Jianu, Demiralp, Laidlaw - 2009 -
                   Exploring 3D DTI fiber tracts with linked 2D
                   representations..pdf:pdf},
  issn           = {1077-2626},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Cluster
                   Analysis,Computer Graphics,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Imaging, Three-Dimensional,Imaging,
                   Three-Dimensional: methods,Models, Biological,Nerve
                   Fibers},
  pmid           = {19834220},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19834220},
  year           = 2009
}

@Article{Oliphant2003,
  Author         = {Oliphant, Travis E},
  Title          = {{SciPy Tutorial}},
  Number         = {September},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Oliphant - 2003 - SciPy
                   Tutorial.pdf:pdf},
  year           = 2003
}

@InProceedings{Wedeen2000,
  Author         = {Wedeen, VJ and Reese, TG and Tuch, DS and Weigel, MR
                   and Dou, JG and Weiskoff, RM and Chessler, D},
  Title          = {{Mapping fiber orientation spectra in cerebral white
                   matter with Fourier-transform diffusion MRI}},
  BookTitle      = {Proc. Intl. Sot. Mag. Reson. Med},
  Volume         = {8},
  Pages          = {82},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wedeen et al. - 2000 - Mapping fiber
                   orientation spectra in cerebral white matter with
                   Fourier-transform diffusion MRI.pdf:pdf},
  url            = {http://cds.ismrm.org/ismrm-2000/PDF1/0082.pdf},
  year           = 2000
}

@conference{o2006high,
  author         = {O'Donnell, L. and Westin, CF},
  booktitle      = {International Society of Magnetic Resonance in
                   Medicine (ISMRM)},
  organization   = {Citeseer},
  title          = {{A high-dimensional fiber tract atlas}},
  year           = 2006
}

@Article{Sorensen1999,
  Author         = {Sorensen, A. Gregory and Wu, Ona and Copen, William A.
                   and Davis, Timothy L. and Gonzalez, R. Gilberto and
                   Koroshetz, Walter J. and Reese, Timothy G. and Rosen,
                   Bruce R. and Wedeen, Van J. and Weisskoff, Robert M.},
  Title          = {{Human Acute Cerebral Ischemia: Detection of Changes
                   in Water Diffusion Anisotropy by Using MR Imaging}},
  Journal        = {Radiology},
  Volume         = {212},
  Number         = {3},
  Pages          = {785--792},
  abstract       = {PURPOSE: To (a) determine the optimal choice of a
                   scalar metric of anisotropy and (b) determine by means
                   of magnetic resonance imaging if changes in diffusion
                   anisotropy occurred in acute human ischemic stroke.
                   MATERIALS AND METHODS: The full diffusion tensor over
                   the entire brain was measured. To optimize the choice
                   of a scalar anisotropy metric, the performances of
                   scalar indices in simulated models and in a healthy
                   volunteer were analyzed. The anisotropy, trace apparent
                   diffusion coefficient (ADC), and eigenvalues of the
                   diffusion tensor in lesions and contralateral normal
                   brain were compared in 50 patients with stroke.
                   RESULTS: Changes in anisotropy in patients were
                   quantified by using fractional anisotropy because it
                   provided the best performance in terms of
                   contrast-to-noise ratio as a function of
                   signal-to-noise ratio in simulations. The anisotropy of
                   ischemic white matter decreased (P = .01). Changes in
                   anisotropy in ischemic gray matter were not significant
                   (P = .63). The trace ADC decreased for ischemic gray
                   matter and white matter (P < .001). The first and
                   second eigenvalues decreased in both ischemic gray and
                   ischemic white matter (P < .001). The third eigenvalue
                   decreased in ischemic gray (P = .001) and white matter
                   (P = .03). CONCLUSION: Gray matter is mildly
                   anisotropic in normal and early ischemic states.
                   However, early white matter ischemia is associated with
                   not only changes in trace ADC values but also
                   significant changes in the anisotropy, or shape, of the
                   water self-diffusion tensor.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Sorensen et al. - 1999 - Human Acute
                   Cerebral Ischemia Detection of Changes in Water
                   Diffusion Anisotropy by Using MR Imaging.html:html},
  month          = sep,
  shorttitle     = {Human Acute Cerebral Ischemia},
  url            = {http://radiology.rsnajnls.org/cgi/content/abstract/212/3/785},
  year           = 1999
}

@Article{Ang2003,
  Author         = {Ang, Y O N G T and Yengaard, J E N S R N and
                   Akkenberg, B Ente P and Undersen, H A N S J \O rgen G G},
  Title          = {{STEREOLOGY OF NEURONAL CONNECTIONS ( MYELINATED
                   FIBERS OF WHITE MATTER AND SYNAPSES OF NEOCORTEX ) IN}},
  Journal        = {Methods},
  Pages          = {171--182},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ang et al. - 2003 - STEREOLOGY OF
                   NEURONAL CONNECTIONS ( MYELINATED FIBERS OF WHITE
                   MATTER AND SYNAPSES OF NEOCORTEX ) IN.pdf:pdf},
  keywords       = {human brain,myelinated nerve
                   fibers,neocortex,stereology,synapse,white matter},
  year           = 2003
}

@Article{Tournier2008,
  Author         = {Tournier, J-Donald and Yeh, Chun-Hung and Calamante,
                   Fernando and Cho, Kuan-Hung and Connelly, Alan and Lin,
                   Ching-Po},
  Title          = {{Resolving crossing fibres using constrained spherical
                   deconvolution: validation using diffusion-weighted
                   imaging phantom data.}},
  Journal        = {NeuroImage},
  Volume         = {42},
  Number         = {2},
  Pages          = {617--25},
  abstract       = {Diffusion-weighted imaging can potentially be used to
                   infer the connectivity of the human brain in vivo using
                   fibre-tracking techniques, and is therefore of great
                   interest to neuroscientists and clinicians. A key
                   requirement for fibre tracking is the accurate
                   estimation of white matter fibre orientations within
                   each imaging voxel. The diffusion tensor model, which
                   is widely used for this purpose, has been shown to be
                   inadequate in crossing fibre regions. A number of
                   approaches have recently been proposed to address this
                   issue, based on high angular resolution
                   diffusion-weighted imaging (HARDI) data. In this study,
                   an experimental model of crossing fibres, consisting of
                   water-filled plastic capillaries, is used to thoroughly
                   assess three such techniques: constrained spherical
                   deconvolution (CSD), super-resolved CSD (super-CSD) and
                   Q-ball imaging (QBI). HARDI data were acquired over a
                   range of crossing angles and b-values, from which fibre
                   orientations were computed using each technique. All
                   techniques were capable of resolving the two fibre
                   populations down to a crossing angle of 45 degrees ,
                   and down to 30 degrees for super-CSD. A bias was
                   observed in the fibre orientations estimated by QBI for
                   crossing angles other than 90 degrees, consistent with
                   previous simulation results. Finally, for a 45 degrees
                   crossing, the minimum b-value required to resolve the
                   fibre orientations was 4000 s/mm(2) for QBI, 2000
                   s/mm(2) for CSD, and 1000 s/mm(2) for super-CSD. The
                   quality of estimation of fibre orientations may
                   profoundly affect fibre tracking attempts, and the
                   results presented provide important additional
                   information regarding performance characteristics of
                   well-known methods.},
  doi            = {10.1016/j.neuroimage.2008.05.002},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tournier et al. - 2008 - Resolving
                   crossing fibres using constrained spherical
                   deconvolution validation using diffusion-weighted
                   imaging phantom data..pdf:pdf},
  issn           = {1095-9572},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   anatomy \& histology,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   instrumentat,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Nerve Fibers, Myelinated,Nerve Fibers,
                   Myelinated: ultrastructure,Pattern Recognition,
                   Automated,Pattern Recognition, Automated:
                   methods,Phantoms, Imaging,Reproducibility of
                   Results,Sensitivity and Specificity},
  pmid           = {18583153},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18583153},
  year           = 2008
}

@Article{Aganj2010,
  Author         = {Aganj, Iman and Lenglet, Christophe and Jahanshad,
                   Neda and Yacoub, Essa and Harel, Noam and Thompson,
                   Paul M and Series, I M A Preprint and E, Church Street
                   S},
  Title          = {{A HOUGH TRANSFORM GLOBAL PROBABILISTIC APPROACH A
                   Hough Transform Global Probabilistic Approach to
                   Multiple- Subject Diffusion MRI Tractography}},
  Pages          = {612--626},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Aganj et al. - 2010 - A HOUGH
                   TRANSFORM GLOBAL PROBABILISTIC APPROACH A Hough
                   Transform Global Probabilistic Approach to Multiple-
                   Subject Diffusion MRI Tractography.pdf:pdf},
  year           = 2010
}

@Article{Sherbondy2008JVision,
  Author         = {Sherbondy, Anthony J. and Dougherty, Robert F. and
                   Ben-Shachar, Michal and Napel, Sandy and Wandell, Brian
                   A.},
  Title          = {{ConTrack: Finding the most likely pathways between
                   brain regions using diffusion tractography}},
  Journal        = {J. Vis.},
  Volume         = {8},
  Number         = {9},
  Pages          = {1-16},
  abstract       = {Magnetic resonance diffusion-weighted imaging coupled
                   with fiber tractography (DFT) is the only non-invasive
                   method for measuring white matter pathways in the
                   living human brain. DFT is often used to discover new
                   pathways. But there are also many applications,
                   particularly in visual neuroscience, in which we are
                   confident that two brain regions are connected, and we
                   wish to find the most likely pathway forming the
                   connection. In several cases, current DFT algorithms
                   fail to find these candidate pathways. To overcome this
                   limitation, we have developed a probabilistic DFT
                   algorithm (ConTrack) that identifies the most likely
                   pathways between two regions. We introduce the
                   algorithm in three parts: a sampler to generate a large
                   set of potential pathways, a scoring algorithm that
                   measures the likelihood of a pathway, and an
                   inferential step to identify the most likely pathways
                   connecting two regions. In a series of experiments
                   using human data, we show that ConTrack estimates known
                   pathways at positions that are consistent with those
                   found using a high quality deterministic algorithm.
                   Further we show that separating sampling and scoring
                   enables ConTrack to identify valid pathways, known to
                   exist, that are missed by other deterministic and
                   probabilistic DFT algorithms.},
  file           = {attachment\:Sherbondy-2008-jov-8-9-15.pdf:attachment\:Sherbondy-2008-jov-8-9-15.pdf:PDF},
  issn           = {1534-7362},
  keywords       = {diffusion imaging, fiber tractography, MT+, corpus
                   callosum, optic radiation},
  month          = {7},
  url            = {http://journalofvision.org/8/9/15/},
  year           = 2008
}

@Article{jones1999osm,
  Author         = {Jones, DK and Horsfield, MA and Simmons, A.},
  Title          = {{Optimal strategies for measuring diffusion in
                   anisotropic systems by magnetic resonance imaging}},
  Journal        = {optimization},
  Volume         = {525},
  year           = 1999
}

@Article{Dauguet2007,
  Author         = {Dauguet, Julien and Peled, Sharon and Berezovskii,
                   Vladimir and Delzescaux, Thierry and Warfield, Simon K
                   and Born, Richard and Westin, Carl-Fredrik},
  Title          = {{Comparison of fiber tracts derived from in-vivo DTI
                   tractography with 3D histological neural tract tracer
                   reconstruction on a macaque brain.}},
  Journal        = {NeuroImage},
  Volume         = {37},
  Number         = {2},
  Pages          = {530--8},
  abstract       = {Since the introduction of diffusion weighted imaging
                   (DWI) as a method for examining neural connectivity,
                   its accuracy has not been formally evaluated. In this
                   study, we directly compared connections that were
                   visualized using injected neural tract tracers
                   (WGA-HRP) with those obtained using in-vivo diffusion
                   tensor imaging (DTI) tractography. First, we injected
                   the tracer at multiple sites in the brain of a macaque
                   monkey; second, we reconstructed the histological
                   sections of the labeled fiber tracts in 3D; third, we
                   segmented and registered the fibers (somatosensory and
                   motor tracts) with the anatomical in-vivo MRI from the
                   same animal; and last, we conducted fiber tracing along
                   the same pathways on the DTI data using a classical
                   diffusion tracing technique with the injection sites as
                   seeds. To evaluate the performance of DTI fiber
                   tracing, we compared the fibers derived from the DTI
                   tractography with those segmented from the histology.
                   We also studied the influence of the parameters
                   controlling the tractography by comparing Dice
                   superimposition coefficients between histology and DTI
                   segmentations. While there was generally good visual
                   agreement between the two methods, our quantitative
                   comparisons reveal certain limitations of DTI
                   tractography, particularly for regions at remote
                   locations from seeds. We have thus demonstrated the
                   importance of appropriate settings for realistic
                   tractography results.},
  doi            = {10.1016/j.neuroimage.2007.04.067},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Dauguet et al. - 2007 - Comparison
                   of fiber tracts derived from in-vivo DTI tractography
                   with 3D histological neural tract tracer reconstruction
                   on a macaque brain..pdf:pdf},
  issn           = {1053-8119},
  keywords       = {Animals,Anisotropy,Brain,Brain: anatomy \&
                   histology,Diffusion Magnetic Resonance Imaging,Image
                   Processing, Computer-Assisted,Imaging,
                   Three-Dimensional,Immunohistochemistry,Macaca,Nerve
                   Fibers,Nerve Fibers: ultrastructure,Neural
                   Pathways,Neural Pathways: cytology},
  pmid           = {17604650},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/17604650},
  year           = 2007
}

@InProceedings{Fillard2006ISBI,
  Author         = {Fillard, Pierre and Arsigny, Vincent and Pennec,
                   Xavier and Ayache, Nicholas},
  Title          = {The Tensor Distribution Function},
  BookTitle      = {Third IEEE International Symposium on Biomedical
                   Imaging: From Nano to Macro},
  Pages          = {(abstract)},
  Publisher      = {IEEE},
  abstract       = {Diffusion tensor MRI is an imaging modality that is
                   gaining importance in clinical applications. However,
                   in a clinical environment, data have to be acquired
                   rapidly, often at the detriment of the image quality.
                   We propose a new variational framework that
                   specifically targets low quality DT-MRI. The Rician
                   nature of the noise on the images leads us to a maximum
                   likelihood strategy to estimate the tensor field. To
                   further reduce the noise, we optimally exploit the
                   spatial correlation by adding to the estimation an
                   anisotropic regularization term. This criterion is
                   easily optimized thanks to the use of the recently
                   introduced Log-Euclidean metrics. Results on real
                   clinical data show promising improvements of fiber
                   tracking in the brain and the spinal cord.},
  year           = 2006
}

@Article{ZHK+06,
  Author         = {Zhuang, J. and Hrabe, J. and Kangarlu, A. and Xu, D.
                   and Bansal, R. and Branch, C. A. and Peterson, B. S.},
  Title          = {Correction of eddy-current distortions in diffusion
                   tensor images using the known directions and strengths
                   of diffusion gradients.},
  Journal        = {J Magn Reson Imaging},
  Volume         = {24},
  Number         = {5},
  Pages          = {1188-93},
  abstract       = {PURPOSE: To correct eddy-current artifacts in
                   diffusion tensor (DT) images without the need to obtain
                   auxiliary scans for the sole purpose of correction.
                   MATERIALS AND METHODS: DT images are susceptible to
                   distortions caused by eddy currents induced by large
                   diffusion gradients. We propose a new postacquisition
                   correction algorithm that does not require any
                   auxiliary reference scans. It also avoids the
                   problematic procedure of cross-correlating images with
                   significantly different contrasts. A linear model is
                   used to describe the dependence of distortion
                   parameters (translation, scaling, and shear) on the
                   diffusion gradients. The model is solved numerically to
                   provide an individual correction for every
                   diffusion-weighted (DW) image. RESULTS: The assumptions
                   of the linear model were successfully verified in a
                   series of experiments on a silicon oil phantom. The
                   correction obtained for this phantom was compared with
                   correction obtained by a previously published method.
                   The algorithm was then shown to markedly reduce
                   eddy-current distortions in DT images from human
                   subjects. CONCLUSION: The proposed algorithm can
                   accurately correct eddy-current artifacts in DT images.
                   Its principal advantages are that only images with
                   comparable signals and contrasts are cross-correlated,
                   and no additional scans are required.},
  authoraddress  = {Magnetic Resonance Imaging Unit, Department of
                   Psychiatry, Columbia College of Physicians and
                   Surgeons, New York, New York, USA. jc.zhuang@gmail.com},
  keywords       = {*Algorithms ; Brain/*anatomy \& histology ; Diffusion
                   Magnetic Resonance Imaging/*methods ; Echo-Planar
                   Imaging/instrumentation/*methods ; Humans ; Image
                   Enhancement/*methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Phantoms, Imaging ;
                   Reproducibility of Results ; Sensitivity and
                   Specificity},
  language       = {eng},
  medline-aid    = {10.1002/jmri.20727 [doi]},
  medline-ci     = {Copyright (c) 2006 Wiley-Liss, Inc.},
  medline-crdt   = {2006/10/07 09:00},
  medline-da     = {20061030},
  medline-dcom   = {20070130},
  medline-edat   = {2006/10/07 09:00},
  medline-fau    = {Zhuang, Jiancheng ; Hrabe, Jan ; Kangarlu, Alayar ;
                   Xu, Dongrong ; Bansal, Ravi ; Branch, Craig A ;
                   Peterson, Bradley S},
  medline-gr     = {DA017820/DA/NIDA NIH HHS/United States ; K02
                   MH074677-01/MH/NIMH NIH HHS/United States ;
                   MH068318/MH/NIMH NIH HHS/United States ;
                   MH59139/MH/NIMH NIH HHS/United States ; MH74677/MH/NIMH
                   NIH HHS/United States ; R01 DA017820-03/DA/NIDA NIH
                   HHS/United States ; R01 MH068318-03/MH/NIMH NIH
                   HHS/United States},
  medline-is     = {1053-1807 (Print)},
  medline-jid    = {9105850},
  medline-jt     = {Journal of magnetic resonance imaging : JMRI},
  medline-lr     = {20081120},
  medline-mhda   = {2007/01/31 09:00},
  medline-mid    = {NIHMS44414},
  medline-oid    = {NLM: NIHMS44414 ; NLM: PMC2364728},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmc    = {PMC2364728},
  medline-pmid   = {17024663},
  medline-pst    = {ppublish},
  medline-pt     = {Evaluation Studies ; Journal Article ; Research
                   Support, N.I.H., Extramural ; Research Support,
                   Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {J Magn Reson Imaging. 2006 Nov;24(5):1188-93.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17024663},
  year           = 2006
}

@Article{Rothwell,
  Author         = {Rothwell, John},
  Title          = {{HBM2010 Program at a Glance *}},
  Journal        = {Program},
  Pages          = {2010--2010},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Rothwell - Unknown - HBM2010 Program
                   at a Glance.pdf:pdf}
}

@Article{Mori2008NeuroImage,
  Author         = {Mori, Susumu and Oishi, Kenichi and Jiang, Hangyi and
                   Jiang, Li and Li, Xin and Akhter, Kazi and Hua, Kegang
                   and Faria, Andreia V. and Mahmood, Asif and Woods,
                   Roger and Toga, Arthur W. and Pike, G. Bruce and Neto,
                   Pedro Rosa and Evans, Alan and Zhang, Jiangyang and
                   Huang, Hao and Miller, Michael I. and {van Zijl}, Peter
                   and Mazziotta, John},
  Title          = {Stereotaxic white matter atlas based on diffusion
                   tensor imaging in an ICBM template},
  Journal        = {NeuroImage},
  Volume         = {40},
  Number         = {2},
  Pages          = {570-582},
  abstract       = {Brain registration to a stereotaxic atlas is an
                   effective way to report anatomic locations of interest
                   and to perform anatomic quantification. However,
                   existing stereotaxic atlases lack comprehensive
                   coordinate information about white matter structures.
                   In this paper, white matter-specific atlases in
                   stereotaxic coordinates are introduced. As a reference
                   template, the widely used ICBM-152 was used. The atlas
                   contains fiber orientation maps and hand-segmented
                   white matter parcellation maps based on diffusion
                   tensor imaging (DTI). Registration accuracy by linear
                   and non-linear transformation was measured, and
                   automated template-based white matter parcellation was
                   tested. The results showed a high correlation between
                   the manual ROI-based and the automated approaches for
                   normal adult populations. The atlases are freely
                   available and believed to be a useful resource as a
                   target template and for automated parcellation methods.
                   },
  file           = {attachment\:Mori2008NeuroImage.pdf:attachment\:Mori2008NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4RH37X2-1/2/24add3aed52eb682f7064260c33384e4},
  year           = 2008
}

@conference{weinstein1999tad,
  author         = {Weinstein, D. and Kindlmann, G. and Lundberg, E.},
  booktitle      = {Proceedings of the conference on Visualization'99:
                   celebrating ten years},
  organization   = {IEEE Computer Society Press Los Alamitos, CA, USA},
  pages          = {249--253},
  title          = {{Tensorlines: Advection-diffusion based propagation
                   through diffusion tensor fields}},
  year           = 1999
}

@Misc{tenenbaum2000ggf,
  Author         = {Tenenbaum, J.B. and Silva, V. and Langford, J.C.},
  Title          = {{A global geometric framework for nonlinear
                   dimensionality reduction}},
  journal        = {Science},
  number         = {5500},
  pages          = {2319--2323},
  volume         = {290},
  year           = 2000
}

@Article{Loper1990,
  Author         = {Loper, David and Annua, Benton E R Spin-up},
  Title          = {{Bingham statistics}},
  Journal        = {Statistics},
  Volume         = {2},
  Number         = {c},
  Pages          = {45--47},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Loper, Annua - 1990 - Bingham
                   statistics.pdf:pdf},
  year           = 1990
}

@Article{chamberlain2008gma,
  Author         = {Chamberlain, S.R. and Menzies, L.A. and Fineberg, N.A.
                   and del Campo, N. and Suckling, J. and Craig, K. and
                   M{\"u}ller, U. and Robbins, T.W. and Bullmore, E.T. and
                   Sahakian, B.J.},
  Title          = {{Grey matter abnormalities in trichotillomania:
                   morphometric magnetic resonance imaging study}},
  Journal        = {The British Journal of Psychiatry},
  Volume         = {193},
  Number         = {3},
  Pages          = {216--221},
  publisher      = {RCP},
  year           = 2008
}

@Article{Batchelor2006,
  Author         = {Batchelor, P G and Calamante, F and Tournier, J D and
                   Atkinson, D and Hill, D L and Connelly, A},
  Title          = {{Quantification of the shape of fiber tracts}},
  Journal        = {Magn. Reson. Med},
  Volume         = {55},
  Pages          = {894--903},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Batchelor et al. - 2006 -
                   Quantification of the shape of fiber tracts.pdf:pdf},
  year           = 2006
}

@Article{Qazi2009,
  Author         = {Qazi, Arish a and Radmanesh, Alireza and O'Donnell,
                   Lauren and Kindlmann, Gordon and Peled, Sharon and
                   Whalen, Stephen and Westin, Carl-Fredrik and Golby,
                   Alexandra J},
  Title          = {{Resolving crossings in the corticospinal tract by
                   two-tensor streamline tractography: Method and clinical
                   assessment using fMRI.}},
  Journal        = {NeuroImage},
  Volume         = {47 Suppl 2},
  Pages          = {T98--106},
  abstract       = {An inherent drawback of the traditional diffusion
                   tensor model is its limited ability to provide detailed
                   information about multidirectional fiber architecture
                   within a voxel. This leads to erroneous fiber
                   tractography results in locations where fiber bundles
                   cross each other. This may lead to the inability to
                   visualize clinically important tracts such as the
                   lateral projections of the corticospinal tract. In this
                   report, we present a deterministic two-tensor eXtended
                   Streamline Tractography (XST) technique, which
                   successfully traces through regions of crossing fibers.
                   We evaluated the method on simulated and in vivo human
                   brain data, comparing the results with the traditional
                   single-tensor and with a probabilistic tractography
                   technique. By tracing the corticospinal tract and
                   correlating with fMRI-determined motor cortex in both
                   healthy subjects and patients with brain tumors, we
                   demonstrate that two-tensor deterministic streamline
                   tractography can accurately identify fiber bundles
                   consistent with anatomy and previously not detected by
                   conventional single-tensor tractography. When compared
                   to the dense connectivity maps generated by
                   probabilistic tractography, the method is
                   computationally efficient and generates discrete
                   geometric pathways that are simple to visualize and
                   clinically useful. Detection of crossing white matter
                   pathways can improve neurosurgical visualization of
                   functionally relevant white matter areas.},
  doi            = {10.1016/j.neuroimage.2008.06.034},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Qazi et al. - 2009 - Resolving
                   crossings in the corticospinal tract by two-tensor
                   streamline tractography Method and clinical assessment
                   using fMRI..pdf:pdf},
  issn           = {1095-9572},
  keywords       = {Algorithms,Brain Neoplasms,Brain Neoplasms:
                   physiopathology,Computer
                   Simulation,Female,Humans,Magnetic Resonance
                   Imaging,Magnetic Resonance Imaging: methods,Male,Middle
                   Aged,Models, Theoretical,Motor Cortex,Motor Cortex:
                   pathology,Motor Cortex:
                   physiopathology,Probability,Pyramidal Tracts,Pyramidal
                   Tracts: pathology},
  pmid           = {18657622},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18657622},
  year           = 2009
}

@Article{Neji2008,
  Author         = {Neji, R and Fleury, G and Deux, J-f and Rahmouni, A
                   and Bassez, G and Vignaud, A and Paragios, N and Mas,
                   Laboratoire and Paris, Ecole Centrale and Galen, Equipe
                   and Saclay, Inria},
  Title          = {{SUPPORT VECTOR DRIVEN MARKOV RANDOM FIELDS TOWARDS
                   DTI SEGMENTATION OF THE HUMAN SKELETAL MUSCLE b b b b}},
  Pages          = {923--926},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Neji et al. - 2008 - SUPPORT VECTOR
                   DRIVEN MARKOV RANDOM FIELDS TOWARDS DTI SEGMENTATION OF
                   THE HUMAN SKELETAL MUSCLE b b b b.pdf:pdf},
  year           = 2008
}

@Article{Okada2006,
  Author         = {Okada, Tsutomu},
  Title          = {{Diffusion-Tensor Fiber Purpose : Methods : Results :
                   Conclusion :}},
  Volume         = {238},
  Number         = {2},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Okada - 2006 - Diffusion-Tensor
                   Fiber Purpose Methods Results Conclusion.pdf:pdf},
  year           = 2006
}

@Article{moriBook,
  Author         = {Mori, S. and Wakana, S. and Nagae-Poetscher, LM and
                   Van Zijl, PCM},
  Title          = {{MRI atlas of human white matter}},
  Journal        = {American Journal of Neuroradiology},
  publisher      = {Am Soc Neuroradiology}
}

@Article{Basser1994BiophysicalJ,
  Author         = {Basser, Peter J. and Mattiello, James and LeBihan,
                   Denis},
  Title          = {{MR} Diffusion Tensor Spectroscopy and Imaging},
  Journal        = {Biophysical Journal},
  Volume         = {66},
  Pages          = {259-267},
  abstract       = {This paper describes a new {NMR} imaging modality-{MR}
                   diffusion tensor imaging. It consists of estimating an
                   effective diffusion tensor, $D_{\textrm{eff}}$, within
                   a voxel, and then displaying useful quantities derived
                   from it. We show how the phenomenon of anisotropic
                   diffusion of water (or metabolites) in anisotropic
                   tissues, measured noninvasively by these {NMR} methods,
                   is exploited to determine fiber tract orientation and
                   mean particle displacements. Once $D_{\textrm{eff}}$ is
                   estimated from a series of {NMR} pulsed-gradient,
                   spin-echo experiments, a tissue's three orthotropic
                   axes can be determined. They coincide with the eigen-
                   vectors of $D_{\textrm{eff}}$, while the effective
                   diffusivities along these orthotropic directions are
                   the eigenvalues of $D_{\textrm{eff}}$. Diffusion
                   ellipsoids, constructed in each voxel from
                   $D_{\textrm{eff}}$, depict both these orthotropic axes
                   and the mean diffusion distances in these directions.
                   Moreover, the three scalar invariants of
                   $D_{\textrm{eff}}$, which are independent of the
                   tissue's orientation in the laboratory frame of
                   reference, reveal useful information about molecular
                   mobility reflective of local microstructure and
                   anatomy. Inherently, tensors (like $D_{\textrm{eff}}$)
                   describing transport processes in anisotropic media
                   contain new information within a macroscopic voxel that
                   scalars (such as the apparent diffusivity, proton
                   density, $T_1$, and $T_2$) do not.},
  year           = 1994
}

@Article{Avants2010,
  Author         = {Avants, Brian B and Tustison, Nick and Song, Gang},
  Title          = {{Advanced Normalization Tools ( ANTS )}},
  Journal        = {Computing},
  Pages          = {1--33},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Avants, Tustison, Song - 2010 -
                   Advanced Normalization Tools ( ANTS ).pdf:pdf},
  year           = 2010
}

@Article{Beaulieu2002NMRBiomed,
  Author         = {Christian Beaulieu},
  Title          = {The basis of anisotropic water diffusion in the
                   nervous system - a technical review},
  Journal        = {NMR in Biomedicine},
  Volume         = {15},
  Number         = {7-8},
  Pages          = {435-455},
  doi            = {10.1002/nbm.782},
  owner          = {ian},
  timestamp      = {2009.04.27},
  url            = {http://dx.doi.org/10.1002/nbm.782},
  year           = 2002
}

@Book{Callaghan1991OUP,
  Author         = {Callaghan, Paul T.},
  Title          = {Principles of Nuclear Magnetic Resonance Microscopy},
  Publisher      = {Oxford University Press},
  owner          = {ian},
  timestamp      = {2009.03.12},
  url            = {http://books.google.co.uk/books?id=yjrjT_W5hygC},
  year           = 1991
}

@Article{ODonnell_MICCAI07,
  Author         = {O'Donnell, L. J. and Westin, C. F. and Golby, A. J.},
  Title          = {Tract-based morphometry.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {10},
  Number         = {Pt 2},
  Pages          = {161-8},
  abstract       = {Multisubject statistical analyses of diffusion tensor
                   images in regions of specific white matter tracts have
                   commonly measured only the mean value of a scalar
                   invariant such as the fractional anisotropy (FA),
                   ignoring the spatial variation of FA along the length
                   of fiber tracts. We propose to instead perform
                   tract-based morphometry (TBM), or the statistical
                   analysis of diffusion MRI data in an anatomical
                   tract-based coordinate system. We present a method for
                   automatic generation of white matter tract arc length
                   parameterizations, based on learning a fiber bundle
                   model from tractography from multiple subjects. Our
                   tract-based coordinate system enables TBM for the
                   detection of white matter differences in groups of
                   subjects. We present example TBM results from a study
                   of interhemispheric differences in FA.},
  authoraddress  = {Golby Surgical Brain Mapping Laboratory, Department of
                   Neurosurgery, Brigham and Women's Hospital, Harvard
                   Medical School, Boston MA, USA.
                   odonnell@bwh.harvard.edu},
  keywords       = {Algorithms ; *Artificial Intelligence ;
                   Brain/*cytology ; Cluster Analysis ; Diffusion Magnetic
                   Resonance Imaging/*methods ; Humans ; Image
                   Enhancement/methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/*methods ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Neural Pathways/cytology ;
                   Pattern Recognition, Automated/*methods ;
                   Reproducibility of Results ; Sensitivity and
                   Specificity},
  language       = {eng},
  medline-crdt   = {2007/11/30 09:00},
  medline-da     = {20071129},
  medline-dcom   = {20080103},
  medline-edat   = {2007/11/30 09:00},
  medline-fau    = {O'Donnell, Lauren J ; Westin, Carl-Fredrik ; Golby,
                   Alexandra J},
  medline-gr     = {P41 RR15241-01A1/RR/NCRR NIH HHS/United States ;
                   P41RR13218/RR/NCRR NIH HHS/United States ; R01
                   AG20012-01/AG/NIA NIH HHS/United States ;
                   R01MH074794/MH/NIMH NIH HHS/United States ;
                   U41RR019703/RR/NCRR NIH HHS/United States ;
                   U54EB005149/EB/NIBIB NIH HHS/United States},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-mhda   = {2008/01/04 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {18044565},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2007;10(Pt 2):161-8.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18044565},
  year           = 2007
}

@Article{Intelligence2009,
  Author         = {Intelligence, Comp},
  Title          = {{Spatial Filtering and Single-Trial Classification of
                   EEG during Vowel Speech Imagery}},
  Journal        = {Science And Technology},
  Volume         = {5},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Intelligence - 2009 - Spatial
                   Filtering and Single-Trial Classification of EEG during
                   Vowel Speech Imagery.pdf:pdf},
  keywords       = {4259 nagatsuta,bci,csp,eeg,hama,imagery,japan
                   226-8503,mailing address,midori-ku,r2-15,spatial
                   filter,speech,vowel,yoko-},
  year           = 2009
}

@Article{Becher1999,
  Author         = {Becher, B and Giacomini, P S and Pelletier, D and
                   McCrea, E and Prat, a and Antel, J P},
  Title          = {{Interferon-gamma secretion by peripheral blood T-cell
                   subsets in multiple sclerosis: correlation with disease
                   phase and interferon-beta therapy.}},
  Journal        = {Annals of neurology},
  Volume         = {45},
  Number         = {2},
  Pages          = {247--50},
  abstract       = {Interferon-gamma (IFN-gamma) is implicated as a
                   participant in the immune effector and regulatory
                   mechanisms considered to mediate the pathogenesis of
                   multiple sclerosis (MS). We have used an intracellular
                   cytokine staining technique to demonstrate that the
                   proportion of ex vivo peripheral blood CD4 and CD8
                   T-cell subsets expressing IFN-gamma is increased in
                   secondary progressing (SP) MS patients, whereas the
                   values in untreated relapsing-remitting (RR) MS
                   patients are reduced compared with those of controls.
                   Patients treated with interferon-beta (IFN-beta) have
                   an even more significant reduction in the percentage of
                   IFN-gamma-secreting cells. The finding that the number
                   of IFN-gamma-expressing CD8 cells is increased in SPMS
                   patients, a group with reduced functional suppressor
                   activity, and is most significantly reduced by IFN-beta
                   therapy, which increases suppressor activity, indicates
                   that IFN-gamma secretion by CD8 T cells and functional
                   suppressor defects attributed to this cell subset in MS
                   can be dissociated.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Becher et al. - 1999 -
                   Interferon-gamma secretion by peripheral blood T-cell
                   subsets in multiple sclerosis correlation with disease
                   phase and interferon-beta therapy..pdf:pdf},
  issn           = {0364-5134},
  keywords       = {Adult,Female,Humans,Interferon-beta,Interferon-beta:
                   therapeutic use,Interferon-gamma,Interferon-gamma:
                   secretion,Male,Middle Aged,Multiple Sclerosis,Multiple
                   Sclerosis: immunology,Multiple Sclerosis:
                   therapy,T-Lymphocyte Subsets,T-Lymphocyte Subsets:
                   immunology,T-Lymphocytes,T-Lymphocytes: immunology},
  month          = feb,
  pmid           = {9989628},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/9989628},
  year           = 1999
}

@Article{Corouge2004,
  Author         = {Corouge, Isabelle and Gouttard, Sylvain and Gerig,
                   Guido},
  Title          = {{Accepted for oral presentation A Statistical Shape
                   Model of Individual Fiber Tracts Extracted from
                   Diffusion Tensor MRI}},
  Journal        = {Analysis},
  Volume         = {3217},
  Number         = {Part II},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Corouge, Gouttard, Gerig - 2004 -
                   Accepted for oral presentation A Statistical Shape
                   Model of Individual Fiber Tracts Extracted from
                   Diffusion Tensor MRI.pdf:pdf},
  keywords       = {diffusion tensor imaging,statistical shape modelling},
  year           = 2004
}

@Article{Mangin2002,
  Author         = {Mangin, J-F and Poupon, C and Cointepas, Y and
                   Rivi\`{e}re, D and Papadopoulos-Orfanos, D and Clark, C
                   a and R\'{e}gis, J and {Le Bihan}, D},
  Title          = {{A framework based on spin glass models for the
                   inference of anatomical connectivity from
                   diffusion-weighted MR data - a technical review.}},
  Journal        = {NMR in biomedicine},
  Volume         = {15},
  Number         = {7-8},
  Pages          = {481--92},
  abstract       = {A family of methods aiming at the reconstruction of a
                   putative fascicle map from any diffusion-weighted
                   dataset is proposed. This fascicle map is defined as a
                   trade-off between local information on voxel
                   microstructure provided by diffusion data and a priori
                   information on the low curvature of plausible
                   fascicles. The optimal fascicle map is the minimum
                   energy configuration of a simulated spin glass in which
                   each spin represents a fascicle piece. This spin glass
                   is embedded into a simulated magnetic external field
                   that tends to align the spins along the more probable
                   fiber orientations according to diffusion models. A
                   model of spin interactions related to the curvature of
                   the underlying fascicles introduces a low bending
                   potential constraint. Hence, the optimal configuration
                   is a trade-off between these two kind of forces acting
                   on the spins. Experimental results are presented for
                   the simplest spin glass model made up of compass
                   needles located in the center of each voxel of a tensor
                   based acquisition.},
  doi            = {10.1002/nbm.780},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mangin et al. - 2002 - A framework
                   based on spin glass models for the inference of
                   anatomical connectivity from diffusion-weighted MR data
                   - a technical review..pdf:pdf},
  issn           = {0952-3480},
  keywords       = {Algorithms,Astrocytes,Astrocytes: cytology,Brain,Brain
                   Mapping,Brain Mapping: methods,Brain:
                   cytology,Diffusion Magnetic Resonance Imaging,Diffusion
                   Magnetic Resonance Imaging: methods,Humans,Image
                   Enhancement,Image Enhancement: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Methods,Models, Biological,Nerve Fibers,
                   Myelinated,Nerve Fibers, Myelinated: pathology,Nerve
                   Net,Nerve Net: cytology,Neural Pathways,Neural
                   Pathways: cytology,Pattern Recognition,
                   Automated,Quality Control,Spin Labels},
  pmid           = {12489097},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/12489097},
  year           = 2002
}

@Article{Dryden2005,
  Author         = {Dryden, Ian L.},
  Title          = {{Statistical analysis on high-dimensional spheres and
                   shape spaces}},
  Journal        = {The Annals of Statistics},
  Volume         = {33},
  Number         = {4},
  Pages          = {1643--1665},
  arxivid        = {arXiv:math/0508279v1},
  doi            = {10.1214/009053605000000264},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Dryden - 2005 - Statistical analysis
                   on high-dimensional spheres and shape spaces.pdf:pdf},
  issn           = {0090-5364},
  keywords       = {and phrases,bingham distribution,complex
                   bingham,complex watson,di-},
  month          = aug,
  url            = {http://projecteuclid.org/Dienst/getRecord?id=euclid.aos/1123250225/},
  year           = 2005
}

@Article{Ziyan,
  Author         = {Ziyan, U and Sabuncu, M R and O’donnell, L J and C},
  Title          = {{-F. Westin. Nonlinear registration of diffusion mr
                   images based on fiber bundles}},
  Journal        = {In Medical Image Computing and Computer-Assisted
                   Intervention (MICCAI ’},
  Volume         = {07},
  Number         = {volume4791},
  Pages          = {351--358},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ziyan et al. - Unknown - -F. Westin.
                   Nonlinear registration of diffusion mr images based on
                   fiber bundles.pdf:pdf}
}

@Article{Orasis2007,
  Author         = {Orasis, Projet},
  Title          = {{Optimization of Discrete Markov Random Fields via
                   Dual Decomposition}},
  Journal        = {Computer},
  Number         = {April},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Orasis - 2007 - Optimization of
                   Discrete Markov Random Fields via Dual
                   Decomposition.pdf:pdf},
  year           = 2007
}

@Article{Delivery,
  Author         = {Delivery, Price and Cost, Total and Brimpari, Minodora},
  Title          = {{PC World UK Computer Superstore - Buy cheap c ... PC
                   World UK Computer Superstore - Buy cheap c ...}},
  Journal        = {Computer},
  Volume         = {5610000},
  Pages          = {1--9},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Delivery, Cost, Brimpari - Unknown -
                   PC World UK Computer Superstore - Buy cheap c ... PC
                   World UK Computer Superstore - Buy cheap c ....pdf:pdf}
}

@Article{Engel,
  Author         = {Engel, Klaus and Hadwiger, Markus and Kniss, Joe M and
                   Lefohn, Aaron E and Weiskopf, Daniel},
  Title          = {{Real-Time Volume Graphics Real-Time Volume Graphics}},
  Journal        = {Notes},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Engel et al. - Unknown - Real-Time
                   Volume Graphics Real-Time Volume Graphics.pdf:pdf}
}

@Article{Neji2009,
  Author         = {Neji, Radhou\`{e}ne and Ahmed, Jean-fran\c{c}ois Deux
                   and Nikos, Besbes and Georg, Komodakis and Mezri, Langs
                   and Alain, Maatouk and Guillaume, Rahmouni and Gilles,
                   Bassez and Paragios, Nikos},
  Title          = {{Manifold-driven Grouping of Skeletal Muscle Fibers}},
  Journal        = {Science},
  Number         = {February},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Neji et al. - 2009 - Manifold-driven
                   Grouping of Skeletal Muscle Fibers.pdf:pdf},
  year           = 2009
}

@Article{Ib2001,
  Author         = {Ib, Luis},
  Title          = {{TUTORIAL on QUATERNIONS Part I}},
  Journal        = {Seminar},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ib - 2001 - TUTORIAL on QUATERNIONS
                   Part I.pdf:pdf},
  year           = 2001
}

@Article{Wainwright2005,
  Author         = {Wainwright, Martin J and Jordan, Michael I},
  Title          = {{A Variational Principle for Graphical Models}},
  Journal        = {Electrical Engineering},
  Number         = {March},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wainwright, Jordan - 2005 - A
                   Variational Principle for Graphical Models.pdf:pdf},
  year           = 2005
}

@Article{Hosey2005MagResMed,
  Author         = {Hosey, T. and Williams, G. and Ansorge, R.},
  Title          = {{Inference of multiple fiber orientations in high
                   angular resolution diffusion imaging}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {54},
  Number         = {6},
  Pages          = {1480-1489},
  abstract       = {A method is presented that is capable of determining
                   more than one fiber orientation within a single voxel
                   in high angular resolution diffusion imaging (HARDI)
                   data sets. This method is an extension of the Markov
                   chain method recently introduced to diffusion tensor
                   imaging (DTI) analysis, allowing the probability
                   density function of up to 2 intra-voxel fiber
                   orientations to be inferred. The multiple fiber
                   architecture within a voxel is then assessed by
                   calculating the relative probabilities of a 1 and 2
                   fiber model. It is demonstrated that for realistic
                   signal to noise ratios, it is possible to accurately
                   characterize the directions of 2 intersecting fibers
                   using a 2 fiber model. The shortcomings of
                   under-fitting a 2 fiber model, or over-fitting a 1
                   fiber model, are explored. This new algorithm enhances
                   the tools available for fiber tracking.},
  file           = {attachment\:Hosey2005MagResMed.pdf:attachment\:Hosey2005MagResMed.pdf:PDF},
  year           = 2005
}

@Article{Yen2009a,
  Author         = {Yen, Luh and Fouss, Francois and Decaestecker,
                   Christine and Francq, Pascal and Saerens, Marco},
  Title          = {{Graph nodes clustering with the sigmoid commute-time
                   kernel: A comparative study}},
  Journal        = {Data \& Knowledge Engineering},
  Volume         = {68},
  Number         = {3},
  Pages          = {338--361},
  doi            = {10.1016/j.datak.2008.10.006},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Yen et al. - 2009 - Graph nodes
                   clustering with the sigmoid commute-time kernel A
                   comparative study(2).pdf:pdf},
  issn           = {0169023X},
  publisher      = {Elsevier B.V.},
  url            = {http://linkinghub.elsevier.com/retrieve/pii/S0169023X0800147X},
  year           = 2009
}

@Article{Ziyan2007,
  Author         = {Ziyan, Ulas and Sabuncu, Mert R. and Grimson, W. Eric.
                   L. and Westin, Carl-Fredrik},
  Title          = {{A Robust Algorithm for Fiber-Bundle Atlas
                   Construction}},
  Journal        = {2007 IEEE 11th International Conference on Computer
                   Vision},
  Pages          = {1--8},
  doi            = {10.1109/ICCV.2007.4409143},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ziyan et al. - 2007 - A Robust
                   Algorithm for Fiber-Bundle Atlas Construction.pdf:pdf},
  isbn           = {978-1-4244-1630-1},
  issn           = {1550-5499},
  month          = oct,
  publisher      = {Ieee},
  url            = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4409143},
  year           = 2007
}

@Article{Baldi2009,
  Author         = {Baldi, P. and Kerkyacharian, G. and Marinucci, D. and
                   Picard, D.},
  Title          = {{Asymptotics for spherical needlets}},
  Journal        = {The Annals of Statistics},
  Volume         = {37},
  Number         = {3},
  Pages          = {1150--1171},
  arxivid        = {arXiv:math/0606599v2},
  doi            = {10.1214/08-AOS601},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Baldi et al. - 2009 - Asymptotics
                   for spherical needlets.pdf:pdf},
  issn           = {0090-5364},
  keywords       = {High-frequency asymptotics, spherical needlets, ra},
  url            = {http://projecteuclid.org/euclid.aos/1239369018},
  year           = 2009
}

@Article{MoriNeuron2006,
  Author         = {Mori, Susumu and Zhang, Jiangyang},
  Title          = {Principles of Diffusion Tensor Imaging and Its
                   Applications to Basic Neuroscience Research},
  Journal        = {Neuron},
  Volume         = {51},
  Pages          = {527-39},
  abstract       = {The brain contains more than 100 billion neurons that
                   communicate with each other via axons for the formation
                   of complex neural networks. The structural mapping of
                   such networks during health and disease states is
                   essential for understanding brain function. However,
                   our understanding of brain structural connectivity is
                   surprisingly limited, due in part to the lack of
                   noninvasive methodologies to study axonal anatomy.
                   Diffusion tensor imaging (DTI) is a recently developed
                   MRI technique that can measure macroscopic axonal
                   organization in nervous system tissues. In this
                   article, the principles of DTI methodologies are
                   explained, and several applications introduced,
                   including visualization of axonal tracts in myelin and
                   axonal injuries as well as human brain and mouse
                   embryonic development. The strengths and limitations of
                   DTI and key areas for future research and development
                   are also discussed.},
  file           = {attachment\:Mori_Neuron_2006.pdf:attachment\:Mori_Neuron_2006.pdf:PDF},
  year           = 2006
}

@Article{Heid2000ISMRM,
  Author         = {Heid, O.},
  Title          = {Eddy current-nulled diffusion weighting.},
  Journal        = {In: Proceedings of the 8th Annual Meeting of ISMRM,
                   Denver},
  Pages          = {799},
  owner          = {ian},
  timestamp      = {2009.03.12},
  year           = 2000
}

@Article{merboldt1992diffusion,
  Author         = {Merboldt, K.D. and H{\\"a}nicke, W. and Bruhn, H. and
                   Gyngell, M.L. and Frahm, J.},
  Title          = {{Diffusion imaging of the human brain in vivo using
                   high-speed STEAM MRI}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {23},
  Number         = {1},
  Pages          = {179--192},
  issn           = {1522-2594},
  publisher      = {John Wiley \& Sons},
  year           = 1992
}

@Article{Descoteaux2009a,
  Author         = {Descoteaux, Maxime and Deriche, Rachid and
                   Kn\"{o}sche, Thomas R and Anwander, Alfred},
  Title          = {{Deterministic and probabilistic tractography based on
                   complex fibre orientation distributions.}},
  Journal        = {IEEE transactions on medical imaging},
  Volume         = {28},
  Number         = {2},
  Pages          = {269--86},
  abstract       = {We propose an integral concept for tractography to
                   describe crossing and splitting fibre bundles based on
                   the fibre orientation distribution function (ODF)
                   estimated from high angular resolution diffusion
                   imaging (HARDI). We show that in order to perform
                   accurate probabilistic tractography, one needs to use a
                   fibre ODF estimation and not the diffusion ODF. We use
                   a new fibre ODF estimation obtained from a sharpening
                   deconvolution transform (SDT) of the diffusion ODF
                   reconstructed from q-ball imaging (QBI). This SDT
                   provides new insight into the relationship between the
                   HARDI signal, the diffusion ODF, and the fibre ODF. We
                   demonstrate that the SDT agrees with classical
                   spherical deconvolution and improves the angular
                   resolution of QBI. Another important contribution of
                   this paper is the development of new deterministic and
                   new probabilistic tractography algorithms using the
                   full multidirectional information obtained through use
                   of the fibre ODF. An extensive comparison study is
                   performed on human brain datasets comparing our new
                   deterministic and probabilistic tracking algorithms in
                   complex fibre crossing regions. Finally, as an
                   application of our new probabilistic tracking, we
                   quantify the reconstruction of transcallosal fibres
                   intersecting with the corona radiata and the superior
                   longitudinal fasciculus in a group of eight subjects.
                   Most current diffusion tensor imaging (DTI)-based
                   methods neglect these fibres, which might lead to
                   incorrect interpretations of brain functions.},
  doi            = {10.1109/TMI.2008.2004424},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Descoteaux et al. - 2009 -
                   Deterministic and probabilistic tractography based on
                   complex fibre orientation distributions..pdf:pdf},
  issn           = {1558-0062},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion
                   Magnetic Resonance Imaging,Diffusion Magnetic Resonance
                   Imaging: methods,Echo-Planar Imaging,Echo-Planar
                   Imaging: methods,Humans,Image Enhancement,Image
                   Enhancement: methods,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Models, Neurological,Models, Statistical,Nerve
                   Fibers,Nerve Fibers: ultrastructure,Normal
                   Distribution,Reproducibility of Results,Sensitivity and
                   Specificity},
  month          = feb,
  pmid           = {19188114},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19188114},
  year           = 2009
}

@InProceedings{Deriche2007ISBI,
  Author         = {DERICHE, r. AND DESCOTEAUX, M.},
  Title          = {Splitting Tracking Through Crossing Fibers:
                   Multidirectional Q-Ball Tracking-},
  BookTitle      = {4th IEEE International Symposium on Biomedical
                   Imaging: From Nano to Macro (ISBI07)},
  Pages          = {756759},
  abstract       = {We present a new tracking algorithm based on the full
                   multidirectional information of the diffusion
                   orientation distribution function (ODF) estimated from
                   Q-Ball Imaging (QBI). From the ODF, we extract all
                   available maxima and then extend streamline (STR)
                   tracking to allow for splitting in multiple directions
                   (SPLIT-STR). Our new algorithm SPLIT-STR overcomes
                   important limitations of classical diffusion tensor
                   streamline tracking in regions of low anisotropy and
                   regions of fiber crossings. Not only can the tracking
                   propagate through fiber crossings but it can also deal
                   with fibers fanning and branching. SPLIT-STR algorithm
                   is efficient and validated on synthetic data, on a
                   biological phantom and compared against probabilistic
                   tensor tracking on a human brain dataset with known
                   crossing fibers},
  owner          = {ian},
  timestamp      = {2009.03.10},
  year           = 2007
}

@Article{Garyfallidis2009a,
  Author         = {Garyfallidis, Eleftherios and Brett, Matthew and
                   Nimmo-smith, Ian},
  Title          = {{Fast Dimensionality Reduction for Brain Tractography
                   Clustering}},
  Journal        = {Sciences-New York},
  Pages          = {7--10},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Garyfallidis, Brett, Nimmo-smith -
                   2009 - Fast Dimensionality Reduction for Brain
                   Tractography Clustering.pdf:pdf},
  year           = 2009
}

@Article{JohansenBerg2004ProcNatAcadSci,
  Author         = {Johansen-Berg, H and Behrens, T E and Robson, M D and
                   Drobnjak, I and Rushworth, M F and Brady, JM and Smith,
                   S M and Higham, D J and Matthews, P M},
  Title          = {Changes in connectivity profiles define functionally
                   distinct regions in human medial frontal cortex.},
  Journal        = {Proc. Natl. Acad. Sci. USA},
  Volume         = {101},
  Number         = {36},
  Pages          = {13335-13340},
  abstract       = {A fundamental issue in neuroscience is the relation
                   between structure and function. However, gross
                   landmarks do not correspond well to microstructural
                   borders and cytoarchitecture cannot be visualized in a
                   living brain used for functional studies. Here, we used
                   diffusion-weighted and functional MRI to test
                   structure-function relations directly. Distinct
                   neocortical regions were defined as volumes having
                   similar connectivity profiles and borders identified
                   where connectivity changed. Without using prior
                   information, we found an abrupt profile change where
                   the border between supplementary motor area (SMA) and
                   pre-SMA is expected. Consistent with this anatomical
                   assignment, putative SMA and pre-SMA connected to motor
                   and prefrontal regions, respectively. Excellent spatial
                   correlations were found between volumes defined by
                   using connectivity alone and volumes activated during
                   tasks designed to involve SMA or pre-SMA selectively.
                   This finding demonstrates a strong relationship between
                   structure and function in medial frontal cortex and
                   offers a strategy for testing such correspondences
                   elsewhere in the brain.},
  file           = {attachment\:JohansenBerg2004ProcNatAcadSci.pdf:attachment\:JohansenBerg2004ProcNatAcadSci.pdf:PDF},
  year           = 2004
}

@Article{Maddah_MICCA2005,
  Author         = {Maddah, M. and Mewes, A. U. and Haker, S. and Grimson,
                   W. E. and Warfield, S. K.},
  Title          = {Automated atlas-based clustering of white matter fiber
                   tracts from {DTMRI}.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {8},
  Number         = {Pt 1},
  Pages          = {188-95},
  abstract       = {A new framework is presented for clustering fiber
                   tracts into anatomically known bundles. This work is
                   motivated by medical applications in which variation
                   analysis of known bundles of fiber tracts in the human
                   brain is desired. To include the anatomical knowledge
                   in the clustering, we invoke an atlas of fiber tracts,
                   labeled by the number of bundles of interest. In this
                   work, we construct such an atlas and use it to cluster
                   all fiber tracts in the white matter. To build the
                   atlas, we start with a set of labeled ROIs specified by
                   an expert and extract the fiber tracts initiating from
                   each ROI. Affine registration is used to project the
                   extracted fiber tracts of each subject to the atlas,
                   whereas their B-spline representation is used to
                   efficiently compare them to the fiber tracts in the
                   atlas and assign cluster labels. Expert visual
                   inspection of the result confirms that the proposed
                   method is very promising and efficient in clustering of
                   the known bundles of fiber tracts.},
  authoraddress  = {Computer Science and Artificial Intelligence
                   Laboratory, Massachussets Institute of Technology,
                   Cambridge, MA 02139, USA. mmaddah@bwh.harvard.edu},
  keywords       = {Algorithms ; Anatomy, Artistic ; *Artificial
                   Intelligence ; Brain/*cytology ; Computer Simulation ;
                   Diffusion Magnetic Resonance Imaging/*methods ; Humans
                   ; Image Enhancement/*methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/methods ; Medical Illustration ;
                   Models, Anatomic ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Pattern Recognition,
                   Automated/*methods ; Reproducibility of Results ;
                   Sensitivity and Specificity},
  language       = {eng},
  medline-crdt   = {2006/05/12 09:00},
  medline-da     = {20060511},
  medline-dcom   = {20060609},
  medline-edat   = {2006/05/12 09:00},
  medline-fau    = {Maddah, Mahnaz ; Mewes, Andrea U J ; Haker, Steven ;
                   Grimson, W Eric L ; Warfield, Simon K},
  medline-gr     = {1U54 EB005149/EB/NIBIB NIH HHS/United States ; P01
                   CA67165/CA/NCI NIH HHS/United States ; P41
                   RR13218/RR/NCRR NIH HHS/United States ; R01
                   CA109246/CA/NCI NIH HHS/United States ; R01
                   LM007861/LM/NLM NIH HHS/United States ; R21
                   MH67054/MH/NIMH NIH HHS/United States},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-lr     = {20071114},
  medline-mhda   = {2006/06/10 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {16685845},
  medline-pst    = {ppublish},
  medline-pt     = {Evaluation Studies ; Journal Article ; Research
                   Support, N.I.H., Extramural ; Research Support,
                   Non-U.S. Gov't ; Research Support, U.S. Gov't,
                   Non-P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2005;8(Pt 1):188-95.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16685845},
  year           = 2005
}

@Article{Lenglet2010a,
  Author         = {Lenglet, Christophe and Series, I M A Preprint and
                   Hall, Lind and E, Church Street S and Aganj, Iman and
                   Sapiro, Guillermo},
  Title          = {{ODF MAXIMA EXTRACTION IN INSTITUTE FOR MATHEMATICS
                   AND ITS APPLICATIONS ODF Maxima Extraction in Spherical
                   Harmonic Representation via Analytical Search Space
                   Reduction}},
  Journal        = {Methods},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Lenglet et al. - 2010 - ODF MAXIMA
                   EXTRACTION IN INSTITUTE FOR MATHEMATICS AND ITS
                   APPLICATIONS ODF Maxima Extraction in Spherical
                   Harmonic Representation via Analytical Search Space
                   Reduction.pdf:pdf},
  year           = 2010
}

@Article{Com,
  Author         = {Com, Bookboon},
  Title          = {{RANDOM VARIABLES I}},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Com - Unknown - RANDOM VARIABLES
                   I.pdf:pdf}
}

@Article{Mai,
  Author         = {Mai, Thanh and Ngoc, Pham and Picard, Dominique},
  Title          = {{Localized deconvolution on the sphere}},
  Pages          = {1--33},
  arxivid        = {arXiv:0908.1952v1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Mai, Ngoc, Picard - Unknown -
                   Localized deconvolution on the sphere.pdf:pdf},
  keywords       = {62g05 62g08 62g20 62c10,and phrases,minimax
                   estima-,msc 2000 subject classification,second-
                   generation wavelets,statistical inverse problems,tion}
}

@Article{Alexander2005NeuroImage,
  Author         = {Alexander, Daniel C. and Barker, Gareth J.},
  Title          = {Optimal imaging parameters for fiber-orientation
                   estimation in diffusion MRI},
  Journal        = {NeuroImage},
  Volume         = {27},
  Pages          = {357  367},
  abstract       = {This study uses Monte Carlo simulations to investigate
                   the optimal value of the diffusion weighting factor b
                   for estimating white-matter fiber orientations using
                   diffusion MRI with a standard spherical sampling
                   scheme. We devise an algorithm for determining the
                   optimal echo time, pulse width, and pulse separation in
                   the pulsed-gradient spinecho sequence for a specific
                   value of b. The Monte Carlo simulations provide an
                   estimate of the optimal value of b for recovering one
                   and two fiber orientations. We show that the optimum is
                   largely independent of the noise level in the
                   measurements and the number of gradient directions and
                   that the optimum depends only weakly on the diffusion
                   anisotropy, the maximum gradient strength, and the spin
                    spin relaxation time. The optimum depends strongly on
                   the mean diffusivity. In brain tissue, the optima we
                   estimate are in the ranges [0.7, 1.0] \times 10^9 s
                   m^{-2} and [2.2, 2.8] \times 10^9 s m^{-2} for the one-
                   and two-fiber cases, respectively. The best b for
                   estimating the fractional anisotropy is slightly higher
                   than for estimating fiber directions in the one-fiber
                   case and slightly lower in the two-fiber case. To
                   estimate Tr(D) in the onefiber case, the optimal
                   setting is higher still. Simulations suggest that a
                   ratio of high to low b measurements of 5 to 1 is a good
                   compromise for measuring fiber directions and size and
                   shape indices.},
  owner          = {ian},
  timestamp      = {2009.03.04},
  year           = 2005
}

@Article{Yeh2010,
  Author         = {Yeh, F and Wedeen, V and Tseng, W},
  Title          = {{Generalized Q-Sampling Imaging.}},
  Journal        = {IEEE transactions on medical imaging},
  Number         = {c},
  abstract       = {Based on the Fourier transform relation between
                   diffusion MR signals and the underlying diffusion
                   displacement, a new relation is derived to estimate the
                   spin distribution function (SDF) directly from
                   diffusion MR signals. This relation leads to an imaging
                   method called generalized q-sampling imaging (GQI),
                   which can obtain the SDF from the shell sampling scheme
                   used in q-ball imaging (QBI) or the grid sampling
                   scheme used in diffusion spectrum imaging (DSI). The
                   accuracy of GQI was evaluated by a simulation study and
                   an in vivo experiment in comparison with QBI and DSI.
                   The simulation results showed that the accuracy of GQI
                   was comparable to that of QBI and DSI. The simulation
                   study of GQI also showed that an anisotropy index,
                   named quantitative anisotropy, was correlated with the
                   volume fraction of the resolved fiber component. The in
                   vivo images of GQI demonstrated that SDF patterns were
                   similar to the ODFs reconstructed by QBI or DSI. The
                   tractography generated from GQI was also similar to
                   those generated from QBI and DSI. In conclusion, the
                   proposed GQI method can be applied to grid or shell
                   sampling schemes and can provide directional and
                   quantitative information about the crossing fibers.},
  doi            = {10.1109/TMI.2010.2045126},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Yeh, Wedeen, Tseng - 2010 -
                   Generalized Q-Sampling Imaging..pdf:pdf},
  issn           = {1558-0062},
  month          = mar,
  pmid           = {20304721},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/20304721},
  year           = 2010
}

@Article{Cook2007,
  Author         = {Cook, P. A. and Symms, M. and Boulby, P. A. and
                   Alexander, D. C.},
  Title          = {Optimal acquisition orders of diffusion-weighted {MRI}
                   measurements.},
  Journal        = {J Magn Reson Imaging},
  Volume         = {25},
  Number         = {5},
  Pages          = {1051-8},
  abstract       = {PURPOSE: To propose a new method to optimize the
                   ordering of gradient directions in diffusion-weighted
                   MRI so that partial scans have the best spherical
                   coverage. MATERIALS AND METHODS: Diffusion-weighted MRI
                   often uses a spherical sampling scheme, which acquires
                   images sequentially with diffusion-weighting gradients
                   in unique directions distributed isotropically on the
                   hemisphere. If not all of the measurements can be
                   completed, the quality of diffusion tensors fitted to
                   the partial scan is sensitive to the order of the
                   gradient directions in the scanner protocol. If the
                   directions are in a random order, then a partial scan
                   may cover some parts of the hemisphere densely but
                   other parts sparsely and thus provide poor spherical
                   coverage. We compare the results of ordering with
                   previously published methods for optimizing the
                   acquisition in simulation. RESULTS: Results show that
                   all methods produce similar results and all improve the
                   accuracy of the estimated diffusion tensors
                   significantly over unordered acquisitions. CONCLUSION:
                   The new ordering method improves the spherical coverage
                   of partial scans and has the advantage of maintaining
                   the optimal coverage of the complete scan.},
  authoraddress  = {Centre for Medical Image Computing, Department of
                   Computer Science University College London, London, UK.
                   p.cook@cs.ucl.ac.uk},
  keywords       = {Algorithms ; Anisotropy ; Brain Mapping/*methods ;
                   Diffusion Magnetic Resonance Imaging/*methods ; Humans
                   ; Image Enhancement/*methods ; Image Processing,
                   Computer-Assisted},
  language       = {eng},
  medline-aid    = {10.1002/jmri.20905 [doi]},
  medline-ci     = {(c) 2007 Wiley-Liss, Inc.},
  medline-crdt   = {2007/04/26 09:00},
  medline-da     = {20070430},
  medline-dcom   = {20070628},
  medline-edat   = {2007/04/26 09:00},
  medline-fau    = {Cook, Philip A ; Symms, Mark ; Boulby, Philip A ;
                   Alexander, Daniel C},
  medline-is     = {1053-1807 (Print)},
  medline-jid    = {9105850},
  medline-jt     = {Journal of magnetic resonance imaging : JMRI},
  medline-mhda   = {2007/06/29 09:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {17457801},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {J Magn Reson Imaging. 2007 May;25(5):1051-8.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17457801},
  year           = 2007
}

@Article{O'Donnell2009,
  Author         = {O'Donnell, Lauren J and Westin, Carl-Fredrik and
                   Golby, Alexandra J},
  Title          = {{Tract-based morphometry for white matter group
                   analysis.}},
  Journal        = {NeuroImage},
  Volume         = {45},
  Number         = {3},
  Pages          = {832--44},
  abstract       = {We introduce an automatic method that we call
                   tract-based morphometry, or TBM, for measurement and
                   analysis of diffusion MRI data along white matter fiber
                   tracts. Using subject-specific tractography bundle
                   segmentations, we generate an arc length
                   parameterization of the bundle with point
                   correspondences across all fibers and all subjects,
                   allowing tract-based measurement and analysis. In this
                   paper we present a quantitative comparison of fiber
                   coordinate systems from the literature and we introduce
                   an improved optimal match method that reduces spatial
                   distortion and improves intra- and inter-subject
                   variability of FA measurements. We propose a method for
                   generating arc length correspondences across
                   hemispheres, enabling a TBM study of interhemispheric
                   diffusion asymmetries in the arcuate fasciculus (AF)
                   and cingulum bundle (CB). The results of this study
                   demonstrate that TBM can detect differences that may
                   not be found by measuring means of scalar invariants in
                   entire tracts, such as the mean diffusivity (MD)
                   differences found in AF. We report TBM results of
                   higher fractional anisotropy (FA) in the left
                   hemisphere in AF (caused primarily by lower lambda(3),
                   the smallest eigenvalue of the diffusion tensor, in the
                   left AF), and higher left hemisphere FA in CB (related
                   to higher lambda(1), the largest eigenvalue of the
                   diffusion tensor, in the left CB). By mapping the
                   significance levels onto the tractography trajectories
                   for each structure, we demonstrate the anatomical
                   locations of the interhemispheric differences. The TBM
                   approach brings analysis of DTI data into the
                   clinically and neuroanatomically relevant framework of
                   the tract anatomy.},
  doi            = {10.1016/j.neuroimage.2008.12.023},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/O'Donnell, Westin, Golby - 2009 -
                   Tract-based morphometry for white matter group
                   analysis..pdf:pdf},
  issn           = {1095-9572},
  keywords       = {Brain,Brain Mapping,Brain Mapping: methods,Brain:
                   anatomy \& histology,Diffusion Magnetic Resonance
                   Imaging,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods},
  pmid           = {19154790},
  publisher      = {Elsevier Inc.},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19154790},
  year           = 2009
}

@Article{Descoteaux2007MagResMed,
  Author         = {Descoteaux, Maxime and Angelino, Elaine and
                   Fitzgibbons, Shaun and Deriche, Rachid},
  Title          = {Regularized, fast, and robust analytical Q-ball
                   imaging},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {58},
  Number         = {3},
  Pages          = {497-510},
  abstract       = {We propose a regularized, fast, and robust analytical
                   solution for the Q-ball imaging (QBI) reconstruction of
                   the orientation distribution function (ODF) together
                   with its detailed validation and a discussion on its
                   benefits over the state-of-the-art. Our analytical
                   solution is achieved by modeling the raw high angular
                   resolution diffusion imaging signal with a spherical
                   harmonic basis that incorporates a regularization term
                   based on the Laplace Beltrami operator defined on the
                   unit sphere. This leads to an elegant mathematical
                   simplification of the FunkRadon transform which
                   approximates the ODF. We prove a new corollary of the
                   FunkHecke theorem to obtain this simplification. Then,
                   we show that the LaplaceBeltrami regularization is
                   theoretically and practically better than Tikhonov
                   regularization. At the cost of slightly reducing
                   angular resolution, the LaplaceBeltrami regularization
                   reduces ODF estimation errors and improves fiber
                   detection while reducing angular error in the ODF
                   maxima detected. Finally, a careful quantitative
                   validation is performed against ground truth from
                   synthetic data and against real data from a biological
                   phantom and a human brain dataset. We show that our
                   technique is also able to recover known fiber crossings
                   in the human brain and provides the practical advantage
                   of being up to 15 times faster than original numerical
                   QBI method.},
  doi            = {10.1002/mrm.21277},
  file           = {attachment\:Descoteaux2007MagResMed.pdf:attachment\:Descoteaux2007MagResMed.pdf:PDF},
  publisher      = {Wiley-Liss, Inc.},
  url            = {http://dx.doi.org/10.1002/mrm.21277},
  year           = 2007
}

@TechReport{Zhuang2008Kentucky,
  Author         = {Zhuang, Qi and Gold, Brian T. and Huang, Ruiwang and
                   Liang, Xuwei and Cao, Ning and Zhang, Jun},
  Title          = {Generalized Diffusion Simulation-Based Tractography},
  Institution    = {Technical Report CMIDA-HiPSCCS 009-08, Department of
                   Computer Science, University of Kentucky, KY},
  abstract       = {Diffusion weighted imaging ({DWI}) techniques have
                   been used to study human brain white matter fiber
                   structures in vivo. Commonly used standard diffusion
                   tensor magnetic resonance imaging ({DTI}) tractography
                   derived from the second order diffusion tensor model
                   has limitations in its ability to resolve complex fiber
                   tracts. We propose a new fiber tracking method based on
                   the generalized diffusion tensor ({GDT}) model. This
                   new method better models the anisotropic diffusion
                   process in human brain by using the generalized
                   diffusion simulation-based fiber tractography ({GDST}).
                   Due to the additional information provided by {GDT},
                   the {GDST} method simulates the underlying physical
                   diffusion process of the human brain more accurately
                   than does the standard {DTI} method. The effectiveness
                   of the new fiber tracking algorithm was demonstrated
                   via analyses on real and synthetic {DWI} datasets. In
                   addition, the general analytic expression of high order
                   b matrix is derived in the case of twice refocused
                   spin-echo ({TRSE}) pulse sequence which is used in the
                   {DWI} data acquisition. Based on our results, we
                   discuss the benefits of {GDT} and the second order
                   diffusion tensor on fiber tracking.},
  owner          = {ian},
  timestamp      = {2008.10.01},
  year           = 2008
}

@Article{Bar-Shir2008JMR,
  Author         = {Bar-Shir, Amnon and Avram, Liat and zarslan, Evren
                   and Basser, Peter J. and Cohen, Yoram},
  Title          = {The effect of the diffusion time and pulse gradient
                   duration ratio on the diffraction pattern and the
                   structural information estimated from q-space diffusion
                   MR: Experiments and simulations},
  Journal        = {Journal of Magnetic Resonance},
  Volume         = {194},
  Pages          = {230236},
  owner          = {ian},
  timestamp      = {2009.03.05},
  year           = 2008
}

@Article{king1994q,
  Author         = {King, M.D. and Houseman, J. and Roussel, S.A. and Van
                   Bruggen, N. and Williams, S.R. and Gadian, D.G.},
  Title          = {{q-Space imaging of the brain}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {32},
  Number         = {6},
  Pages          = {707--713},
  issn           = {1522-2594},
  publisher      = {John Wiley \& Sons},
  year           = 1994
}

@Book{MAB04,
  Author         = {{Matt A. Bernstein} and {Kevin F. King} and {Xiaohong
                   Joe Zhou}},
  Title          = {Handbook of {MRI} {P}ulse {S}equences},
  Publisher      = {Elsevier Academic Press},
  year           = 2004
}

@Article{Reese2003,
  Author         = {Reese, T G and Heid, O and Weisskoff, R M and Wedeen,
                   V J},
  Title          = {{Reduction of eddy-current-induced distortion in
                   diffusion MRI using a twice-refocused spin echo.}},
  Journal        = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  Volume         = {49},
  Number         = {1},
  Pages          = {177--82},
  abstract       = {Image distortion due to field gradient eddy currents
                   can create image artifacts in diffusion-weighted MR
                   images. These images, acquired by measuring the
                   attenuation of NMR signal due to directionally
                   dependent diffusion, have recently been shown to be
                   useful in the diagnosis and assessment of acute stroke
                   and in mapping of tissue structure. This work presents
                   an improvement on the spin-echo (SE) diffusion sequence
                   that displays less distortion and consequently improves
                   image quality. Adding a second refocusing pulse
                   provides better image quality with less distortion at
                   no cost in scanning efficiency or effectiveness, and
                   allows more flexible diffusion gradient timing. By
                   adjusting the timing of the diffusion gradients, eddy
                   currents with a single exponential decay constant can
                   be nulled, and eddy currents with similar decay
                   constants can be greatly reduced. This new sequence is
                   demonstrated in phantom measurements and in diffusion
                   anisotropy images of normal human brain.},
  doi            = {10.1002/mrm.10308},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Reese et al. - 2003 - Reduction of
                   eddy-current-induced distortion in diffusion MRI using
                   a twice-refocused spin echo..pdf:pdf},
  issn           = {0740-3194},
  keywords       = {Artifacts,Brain,Brain: anatomy \& histology,Brain:
                   pathology,Echo-Planar Imaging,Echo-Planar Imaging:
                   methods,Humans,Magnetic Resonance Imaging,Magnetic
                   Resonance Imaging: methods,Phantoms,
                   Imaging,Stroke,Stroke: diagnosis},
  pmid           = {12509835},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/12509835},
  year           = 2003
}

@Article{Yu,
  Author         = {Yu, Hwanjo and Yang, Jiong},
  Title          = {{Classifying Large Data Sets Using SVMs with
                   Hierarchical Clusters}},
  Journal        = {Science},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Yu, Yang - Unknown - Classifying
                   Large Data Sets Using SVMs with Hierarchical
                   Clusters.pdf:pdf},
  keywords       = {hierarchical cluster,support vector machines}
}

@Article{Zanche2008,
  Author         = {Zanche, N De and Pruessmann, K P and Boesiger, P},
  Title          = {{Preliminary Experience with Visualization of
                   Intracortical Fibers by Focused High-Resolution}},
  Journal        = {Ajnr. American Journal Of Neuroradiology},
  doi            = {10.3174/ajnr.A0742},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Zanche, Pruessmann, Boesiger - 2008
                   - Preliminary Experience with Visualization of
                   Intracortical Fibers by Focused High-Resolution.pdf:pdf},
  year           = 2008
}

@Article{DavisTMI02,
  Author         = {Davies, R. H. and Twining, C. J. and Cootes, T. F. and
                   Waterton, J. C. and Taylor, C. J.},
  Title          = {A minimum description length approach to statistical
                   shape modeling.},
  Journal        = {IEEE Trans Med Imaging},
  Volume         = {21},
  Number         = {5},
  Pages          = {525-37},
  abstract       = {We describe a method for automatically building
                   statistical shape models from a training set of example
                   boundaries/surfaces. These models show considerable
                   promise as a basis for segmenting and interpreting
                   images. One of the drawbacks of the approach is,
                   however, the need to establish a set of dense
                   correspondences between all members of a set of
                   training shapes. Often this is achieved by locating a
                   set of "landmarks" manually on each training image,
                   which is time consuming and subjective in two
                   dimensions and almost impossible in three dimensions.
                   We describe how shape models can be built automatically
                   by posing the correspondence problem as one of finding
                   the parameterization for each shape in the training
                   set. We select the set of parameterizations that build
                   the "best" model. We define "best" as that which
                   minimizes the description length of the training set,
                   arguing that this leads to models with good
                   compactness, specificity and generalization ability. We
                   show how a set of shape parameterizations can be
                   represented and manipulated in order to build a minimum
                   description length model. Results are given for several
                   different training sets of two-dimensional boundaries,
                   showing that the proposed method constructs better
                   models than other approaches including manual
                   landmarking-the current gold standard. We also show
                   that the method can be extended straightforwardly to
                   three dimensions.},
  authoraddress  = {Division of Imaging Science and Biomedical
                   Engineering, University of Manchester, UK.
                   rhodri.h.davies@stud.man.ac.uk},
  keywords       = {*Algorithms ; Animals ; *Artificial Intelligence ;
                   Brain/anatomy \& histology ; Brain Ischemia/diagnosis ;
                   Cartilage, Articular/anatomy \& histology ;
                   Hand/anatomy \& histology ; Heart Ventricles ;
                   Hip/radiography/ultrasonography ; Hip Prosthesis ;
                   Humans ; Image Enhancement/*methods ; Image
                   Interpretation, Computer-Assisted/*methods ;
                   Information Theory ; Kidney/anatomy \& histology ; Knee
                   ; Magnetic Resonance Imaging ; *Models, Statistical ;
                   Multivariate Analysis ; Normal Distribution ; Pattern
                   Recognition, Automated ; Quality Control ; Rats ; Rats,
                   Inbred F344 ; Rats, Sprague-Dawley ; Sensitivity and
                   Specificity ; Stochastic Processes},
  language       = {eng},
  medline-aid    = {10.1109/TMI.2002.1009388 [doi]},
  medline-crdt   = {2002/06/20 10:00},
  medline-da     = {20020619},
  medline-dcom   = {20021227},
  medline-edat   = {2002/06/20 10:00},
  medline-fau    = {Davies, Rhodri H ; Twining, Carole J ; Cootes, Tim F ;
                   Waterton, John C ; Taylor, Chris J},
  medline-is     = {0278-0062 (Print)},
  medline-jid    = {8310780},
  medline-jt     = {IEEE transactions on medical imaging},
  medline-lr     = {20061115},
  medline-mhda   = {2002/12/28 04:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {12071623},
  medline-pst    = {ppublish},
  medline-pt     = {Comparative Study ; Journal Article ; Research
                   Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {IEEE Trans Med Imaging. 2002 May;21(5):525-37.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12071623},
  year           = 2002
}

@Article{Papadakis2000,
  Author         = {Papadakis, N G and Murrills, C D and Hall, L D and
                   Huang, C L and {Adrian Carpenter}, T},
  Title          = {{Minimal gradient encoding for robust estimation of
                   diffusion anisotropy.}},
  Journal        = {Magnetic resonance imaging},
  Volume         = {18},
  Number         = {6},
  Pages          = {671--9},
  abstract       = {This study has investigated the relationship between
                   the noise sensitivity of measurement by magnetic
                   resonance imaging (MRI) of the diffusion tensor (D) of
                   water and the number N of diffusion-weighting (DW)
                   gradient directions, using computer simulations of
                   strongly anisotropic fibers with variable orientation.
                   The DW directions uniformly sampled the diffusion
                   ellipsoid surface. It is shown that the variation of
                   the signal-to-noise ratio (SNR) of three ideally
                   rotationally invariant scalars of D due to variable
                   fiber orientation provides an objective quantitative
                   measure for the diffusion ellipsoid sampling
                   efficiency, which is independent of the SNR value of
                   the baseline signal obtained without DW; the SNR
                   variation decreased asymptotically with increasing N.
                   The minimum number N(0) of DW directions, which
                   minimized the SNR variation of the three scalars of D
                   was determined, thereby achieving the most efficient
                   ellipsoid sampling. The resulting time efficient
                   diffusion tensor imaging (DTI) protocols provide robust
                   estimation of diffusion anisotropy in the presence of
                   noise and can improve the repeatability/reliability of
                   DTI experiments when there is high variability in the
                   orientation of similar anisotropic structures, as for
                   example, in studies which require repeated measurement
                   of one individual, intersubject comparisons or
                   multicenter studies.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Papadakis et al. - 2000 - Minimal
                   gradient encoding for robust estimation of diffusion
                   anisotropy..pdf:pdf},
  issn           = {0730-725X},
  keywords       = {Anisotropy,Computer Simulation,Humans,Magnetic
                   Resonance Imaging,Magnetic Resonance Imaging:
                   methods,Models, Theoretical,Statistics as Topic},
  month          = jul,
  pmid           = {10930776},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/10930776},
  year           = 2000
}

@Article{roberts2005fdi,
  Author         = {Roberts, T. P. L. and Liu, F. and Kassner, A. and
                   Mori, S. and Guha, A.},
  Title          = {{Fiber Density Index Correlates with Reduced
                   Fractional Anisotropy in White Matter of Patients with
                   Glioblastoma}},
  Journal        = {American Journal of Neuroradiology},
  Volume         = {26},
  Number         = {9},
  Pages          = {2183--2186},
  file           = {attachment\:roberts_FA_glioblastoma_2005.pdf:attachment\:roberts_FA_glioblastoma_2005.pdf:PDF},
  publisher      = {Am Soc Neuroradiology},
  year           = 2005
}

@Article{Baas2008,
  Author         = {Baas, Matthias},
  Title          = {{Python Computer Graphics Kit}},
  Journal        = {Interface},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Baas - 2008 - Python Computer
                   Graphics Kit.pdf:pdf},
  year           = 2008
}

@Article{Odonnell_MICCAI05,
  Author         = {O'Donnell, L. and Westin, C. F.},
  Title          = {White matter tract clustering and correspondence in
                   populations.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {8},
  Number         = {Pt 1},
  Pages          = {140-7},
  abstract       = {We present a novel method for finding white matter
                   fiber correspondences and clusters across a population
                   of brains. Our input is a collection of paths from
                   tractography in every brain. Using spectral methods we
                   embed each path as a vector in a high dimensional
                   space. We create the embedding space so that it is
                   common across all brains, consequently similar paths in
                   all brains will map to points near each other in the
                   space. By performing clustering in this space we are
                   able to find matching fiber tract clusters in all
                   brains. In addition, we automatically obtain
                   correspondence of tractographic paths across brains: by
                   selecting one or several paths of interest in one
                   brain, the most similar paths in all brains are
                   obtained as the nearest points in the high-dimensional
                   space.},
  authoraddress  = {MIT Computer Science and Artificial Intelligence Lab,
                   Cambridge MA, USA. lauren@csail.mit.edu},
  keywords       = {Algorithms ; *Artificial Intelligence ; Brain/*anatomy
                   \& histology ; Cluster Analysis ; Diffusion Magnetic
                   Resonance Imaging/*methods ; Humans ; Image
                   Enhancement/methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/*methods ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Pattern Recognition,
                   Automated/*methods ; Reproducibility of Results ;
                   Sensitivity and Specificity},
  language       = {eng},
  medline-crdt   = {2006/05/12 09:00},
  medline-da     = {20060511},
  medline-dcom   = {20060609},
  medline-edat   = {2006/05/12 09:00},
  medline-fau    = {O'Donnell, Lauren ; Westin, Carl-Fredrik},
  medline-gr     = {1-R01-NS051826-01/NS/NINDS NIH HHS/United States ;
                   P41-RR13218/RR/NCRR NIH HHS/United States ; U24
                   RR021382/RR/NCRR NIH HHS/United States ; U54
                   EB005149/EB/NIBIB NIH HHS/United States},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-lr     = {20071114},
  medline-mhda   = {2006/06/10 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {16685839},
  medline-pst    = {ppublish},
  medline-pt     = {Comparative Study ; Evaluation Studies ; Journal
                   Article ; Research Support, N.I.H., Extramural},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2005;8(Pt 1):140-7.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16685839},
  year           = 2005
}

@Article{Sorland2002MagResChem,
  Author         = {Srland, Geir Humborstad and Aksnes, Dagfinn},
  Title          = {Artefacts and pitfalls in diffusion measurements by
                   NMR},
  Journal        = {Magnetic Resonance in Chemistry},
  Volume         = {40},
  Number         = {13},
  Pages          = {S139-S146},
  abstract       = {When applying pulsed field gradient (PFG) NMR
                   experiments to determine the molecular mobility
                   characterized by the diffusion coefficient, it is
                   crucial to have control over all experimental
                   parameters that may affect the performance of the
                   diffusion experiment. This could be diffusion
                   measurement in the presence of magnetic field
                   transients, internal magnetic field gradients, either
                   constant or spatially varying, convection, mechanical
                   vibrations, or in the presence of physical restrictions
                   affecting the diffusion propagator. The effect of these
                   parameters on the diffusion experiment is discussed and
                   visualized. It is also outlined how to minimize their
                   influence on the measured diffusivity that is extracted
                   from the PFG-NMR experiment. For an expanded and more
                   general treatment we refer to the excellent reviews by
                   Dr William S. Price (Concepts Magn. Reson. 1997; 9:
                   299; 1998; 10: 197) and the references therein.},
  doi            = {10.1002/mrc.1112},
  owner          = {ian},
  timestamp      = {2009.03.12},
  url            = {http://dx.doi.org/10.1002/mrc.1112},
  year           = 2002
}

@Article{ZiyanMICCAI07,
  Author         = {Ziyan, U. and Sabuncu, M. R. and O'Donnell, L. J. and
                   Westin, C. F.},
  Title          = {Nonlinear registration of diffusion {MR} images based
                   on fiber bundles.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {10},
  Number         = {Pt 1},
  Pages          = {351-8},
  abstract       = {In this paper, we explore the use of fiber bundles
                   extracted from diffusion MR images for a nonlinear
                   registration algorithm. We employ a white matter atlas
                   to automatically label major fiber bundles and to
                   establish correspondence between subjects. We propose a
                   polyaffine framework to calculate a smooth and
                   invertible nonlinear warp field based on these
                   correspondences, and derive an analytical solution for
                   the reorientation of the tensor fields under the
                   polyaffine transformation. We demonstrate our algorithm
                   on a group of subjects and show that it performs
                   comparable to a higher dimensional nonrigid
                   registration algorithm.},
  authoraddress  = {MIT Computer Science and Artificial Intelligence Lab,
                   Cambridge MA, USA. ulas@mit.edu},
  keywords       = {*Algorithms ; *Artificial Intelligence ;
                   Brain/*anatomy \& histology ; Diffusion Magnetic
                   Resonance Imaging/*methods ; Image Enhancement/*methods
                   ; Image Interpretation, Computer-Assisted/*methods ;
                   Imaging, Three-Dimensional/*methods ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Nonlinear Dynamics ;
                   Pattern Recognition, Automated/*methods ;
                   Reproducibility of Results ; Sensitivity and
                   Specificity},
  language       = {eng},
  medline-crdt   = {2007/12/07 09:00},
  medline-da     = {20071204},
  medline-dcom   = {20080103},
  medline-edat   = {2007/12/07 09:00},
  medline-fau    = {Ziyan, Ulas ; Sabuncu, Mert R ; O'Donnell, Lauren J ;
                   Westin, Carl-Fredrik},
  medline-gr     = {P41-RR13218/RR/NCRR NIH HHS/United States ;
                   P41-RR15241/RR/NCRR NIH HHS/United States ;
                   R01-AG20012/AG/NIA NIH HHS/United States ;
                   R01-MH074794/MH/NIMH NIH HHS/United States ;
                   U54-EB005149/EB/NIBIB NIH HHS/United States},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-mhda   = {2008/01/04 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {18051078},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, N.I.H., Extramural
                   ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2007;10(Pt 1):351-8.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18051078},
  year           = 2007
}

@Article{Aganj,
  Author         = {Aganj, I and Lenglet, C and Keriven, R and Sapiro, G
                   and Harel, N and Thompson, P},
  Title          = {{A Hough Transform Global Approach to Diffusion MRI
                   Tractography}},
  Journal        = {Methods},
  Pages          = {4--4},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Aganj et al. - Unknown - A Hough
                   Transform Global Approach to Diffusion MRI
                   Tractography.pdf:pdf}
}

@Article{Pedersen2008,
  Author         = {Pedersen, Michael Syskind and Baxter, Bill and Rish\o
                   j, Christian and Theobald, Douglas L and Larsen, Jan
                   and Strimmer, Korbinian and Christiansen, Lars and
                   Hansen, Kai and Wilkinson, Leland and He, Liguo and
                   Thibaut, Loic and Bar, Miguel},
  Title          = {{The Matrix Cookbook [}},
  Journal        = {Matrix},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Pedersen et al. - 2008 - The Matrix
                   Cookbook.pdf:pdf},
  keywords       = {acknowledgements,and suggestions,bill baxter,christian
                   rish\o j,contributions,derivative of,derivative of
                   inverse matrix,determinant,differentiate a
                   matrix,douglas l,esben,matrix algebra,matrix
                   identities,matrix relations,thank the following
                   for,theobald,we would like to},
  year           = 2008
}

@Article{denislebihan2006aap,
  Author         = {Denis Le Bihan, MD and Poupon, C. and Amadon, A. and
                   Lethimonnier, F.},
  Title          = {{Artifacts and pitfalls in diffusion MRI}},
  Journal        = {Journal of Magnetic Resonance Imaging},
  Volume         = {24},
  Pages          = {478--488},
  year           = 2006
}

@Article{bernstein2005handbook,
  Author         = {Bernstein, M.A. and King, K.E. and Zhou, X.J. and
                   Fong, W.},
  Title          = {{Handbook of MRI pulse sequences}},
  Journal        = {Medical Physics},
  Volume         = {32},
  Pages          = {1452},
  year           = 2005
}

@Article{BJ02,
  Author         = {Basser, P. J. and Jones, D. K.},
  Title          = {Diffusion-tensor {MRI}: theory, experimental design
                   and data analysis - a technical review.},
  Journal        = {NMR Biomed},
  Volume         = {15},
  Number         = {7-8},
  Pages          = {456-67},
  abstract       = {This article treats the theoretical underpinnings of
                   diffusion-tensor magnetic resonance imaging (DT-MRI),
                   as well as experimental design and data analysis
                   issues. We review the mathematical model underlying
                   DT-MRI, discuss the quantitative parameters that are
                   derived from the measured effective diffusion tensor,
                   and describe artifacts that arise in typical DT-MRI
                   acquisitions. We also discuss difficulties in
                   identifying appropriate models to describe water
                   diffusion in heterogeneous tissues, as well as in
                   interpreting experimental data obtained in such issues.
                   Finally, we describe new statistical methods that have
                   been developed to analyse DT-MRI data, and their
                   potential uses in clinical and multi-site studies.},
  authoraddress  = {Section on Tissue Biophysics and Biomimetics, NICHD,
                   National Institutes of Health, Bethesda, MD 20892, USA.},
  keywords       = {Anisotropy ; Artifacts ; Brain/cytology/metabolism ;
                   Diffusion ; Diffusion Magnetic Resonance
                   Imaging/instrumentation/*methods ; Image
                   Enhancement/*methods ; *Models, Biological ; Models,
                   Chemical ; Nerve
                   Fibers/chemistry/*metabolism/*pathology ; Neural
                   Pathways/chemistry/cytology/metabolism ; Research
                   Design ; Water/chemistry},
  language       = {eng},
  medline-aid    = {10.1002/nbm.783 [doi]},
  medline-ci     = {Copyright 2002 John Wiley & Sons, Ltd.},
  medline-da     = {20021218},
  medline-dcom   = {20030701},
  medline-edat   = {2002/12/19 04:00},
  medline-fau    = {Basser, Peter J ; Jones, Derek K},
  medline-is     = {0952-3480 (Print)},
  medline-jid    = {8915233},
  medline-jt     = {NMR in biomedicine},
  medline-lr     = {20061115},
  medline-mhda   = {2003/07/02 05:00},
  medline-own    = {NLM},
  medline-pl     = {England},
  medline-pmid   = {12489095},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't ;
                   Review},
  medline-pubm   = {Print},
  medline-rf     = {107},
  medline-rn     = {7732-18-5 (Water)},
  medline-sb     = {IM},
  medline-so     = {NMR Biomed. 2002 Nov-Dec;15(7-8):456-67.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks\&dbfrom=pubmed\&retmode=ref\&id=12489095},
  year           = 2002
}

@Article{Maaten2008a,
  Author         = {Maaten, Laurens Van Der and Hinton, Geoffrey},
  Title          = {{Visualizing Data using t-SNE}},
  Journal        = {Journal of Machine Learning Research},
  Volume         = {9},
  Pages          = {2579--2605},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Maaten, Hinton - 2008 - Visualizing
                   Data using t-SNE.pdf:pdf},
  keywords       = {dimensionality reduction,embedding algorithms,manifold
                   learning,multidimensional scaling,visualization},
  year           = 2008
}

@Article{Parker2005PhilTransRoySoc,
  Author         = {Parker, G J and Alexander, D C},
  Title          = {Probabilistic anatomical connectivity derived from the
                   microscopic persistent angular structure of cerebral
                   tissue},
  Journal        = {Philos Trans R Soc Lond B Biol Sci.},
  Volume         = {360},
  Number         = {1457},
  Pages          = {893-902},
  abstract       = {Recently developed methods to extract the persistent
                   angular structure (PAS) of axonal fibre bundles from
                   diffusion-weighted magnetic resonance imaging (MRI)
                   data are applied to drive probabilistic fibre tracking,
                   designed to provide estimates of anatomical cerebral
                   connectivity. The behaviour of the PAS function in the
                   presence of realistic data noise is modelled for a
                   range of single and multiple fibre configurations. This
                   allows probability density functions (PDFs) to be
                   generated that are parametrized according to the
                   anisotropy of individual fibre populations. The PDFs
                   are incorporated in a probabilistic fibre-tracking
                   method to allow the estimation of whole-brain maps of
                   anatomical connection probability. These methods are
                   applied in two exemplar experiments in the
                   corticospinal tract to show that it is possible to
                   connect the entire primary motor cortex (M1) when
                   tracing from the cerebral peduncles, and that the
                   reverse experiment of tracking from M1 successfully
                   identifies high probability connection via the
                   pyramidal tracts. Using the extracted PAS in
                   probabilistic fibre tracking allows higher specificity
                   and sensitivity than previously reported fibre tracking
                   using diffusion-weighted MRI in the corticospinal
                   tract.},
  file           = {attachment\:Parker2005PhilTransRoySoc.pdf:attachment\:Parker2005PhilTransRoySoc.pdf:PDF},
  year           = 2005
}

@Article{Kreher2008ISMRM,
  Author         = {Kreher, B. W. and Mader, I. and Kiselev, V. G.},
  Title          = {Gibbs Tracking: A Novel Approach for the
                   Reconstruction of Neuronal Pathways},
  Journal        = {Proc. Intl. Soc. Mag. Reson. Med.},
  Volume         = {16},
  Pages          = {425},
  abstract       = {Fibre tractography based on diffusion weighted MRI is
                   a powerful method to extract the anatomical
                   connectivity in white matter in vivo. The main idea of
                   the currently available methods of fibre tracking is
                   the reconstruction of long neuronal pathways in small
                   successive steps by following the local, voxel-defined
                   fibre direction. Starting from local information on the
                   diffusivity, long-distance connections are determined.
                   This method is inherently prone to instability, since a
                   mistake at a single crossing affects radically the
                   final result. In this paper we present a method based
                   on a new principle. Instead of walking successively
                   through the volume all neuronal pathways and the
                   totality of the signal is taken into account at the
                   same time. This novel approach is capable to
                   reconstruct crossing and spreading fibre configuration.},
  file           = {attachment\:Kreher2008ISMRM.pdf:attachment\:Kreher2008ISMRM.pdf:PDF},
  year           = 2008
}

@Article{Bea02,
  Author         = {Beaulieu, C.},
  Title          = {The basis of anisotropic water diffusion in the
                   nervous system - a technical review.},
  Journal        = {NMR Biomed},
  Volume         = {15},
  Number         = {7-8},
  Pages          = {435-55},
  abstract       = {Anisotropic water diffusion in neural fibres such as
                   nerve, white matter in spinal cord, or white matter in
                   brain forms the basis for the utilization of diffusion
                   tensor imaging (DTI) to track fibre pathways. The fact
                   that water diffusion is sensitive to the underlying
                   tissue microstructure provides a unique method of
                   assessing the orientation and integrity of these neural
                   fibres, which may be useful in assessing a number of
                   neurological disorders. The purpose of this review is
                   to characterize the relationship of nuclear magnetic
                   resonance measurements of water diffusion and its
                   anisotropy (i.e. directional dependence) with the
                   underlying microstructure of neural fibres. The
                   emphasis of the review will be on model neurological
                   systems both in vitro and in vivo. A systematic
                   discussion of the possible sources of anisotropy and
                   their evaluation will be presented followed by an
                   overview of various studies of restricted diffusion and
                   compartmentation as they relate to anisotropy.
                   Pertinent pathological models, developmental studies
                   and theoretical analyses provide further insight into
                   the basis of anisotropic diffusion and its potential
                   utility in the nervous system.},
  authoraddress  = {Department of Biomedical Engineering, Faculty of
                   Medicine, University of Alberta, Edmonton, Canada.
                   christian.beaulieu@ualberta.ca},
  keywords       = {*Anisotropy ; Brain/metabolism/pathology ; Brain
                   Chemistry ; Diffusion ; Diffusion Magnetic Resonance
                   Imaging/*methods ; Models, Biological ; Nerve
                   Fibers/chemistry/metabolism/pathology ; Nervous
                   System/chemistry/*metabolism/*pathology ; Nervous
                   System Diseases/metabolism/pathology ; Spinal
                   Cord/chemistry/cytology/metabolism ; Water/*chemistry},
  language       = {eng},
  medline-aid    = {10.1002/nbm.782 [doi]},
  medline-ci     = {Copyright 2002 John Wiley & Sons, Ltd.},
  medline-da     = {20021218},
  medline-dcom   = {20030701},
  medline-edat   = {2002/12/19 04:00},
  medline-fau    = {Beaulieu, Christian},
  medline-is     = {0952-3480 (Print)},
  medline-jid    = {8915233},
  medline-jt     = {NMR in biomedicine},
  medline-lr     = {20061115},
  medline-mhda   = {2003/07/02 05:00},
  medline-own    = {NLM},
  medline-pl     = {England},
  medline-pmid   = {12489094},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't ;
                   Review},
  medline-pubm   = {Print},
  medline-rf     = {131},
  medline-rn     = {7732-18-5 (Water)},
  medline-sb     = {IM},
  medline-so     = {NMR Biomed. 2002 Nov-Dec;15(7-8):435-55.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks\&dbfrom=pubmed\&retmode=ref\&id=12489094},
  year           = 2002
}

@Article{Sciences2009,
  Author         = {Sciences, Cognition Brain},
  Title          = {{Michaelmas Term 2008}},
  Journal        = {Sciences-New York},
  Pages          = {9469--9469},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Sciences - 2009 - Michaelmas Term
                   2008.pdf:pdf},
  year           = 2009
}

@Article{Zvitia2010,
  Author         = {Zvitia, Orly and Mayer, Arnaldo and Shadmi, Ran and
                   Miron, Shmuel and Greenspan, Hayit K},
  Title          = {{Co-registration of white matter tractographies by
                   adaptive-mean-shift and Gaussian mixture modeling.}},
  Journal        = {IEEE transactions on medical imaging},
  Volume         = {29},
  Number         = {1},
  Pages          = {132--45},
  abstract       = {In this paper, we present a robust approach to the
                   registration of white matter tractographies extracted
                   from diffusion tensor-magnetic resonance imaging scans.
                   The fibers are projected into a high dimensional
                   feature space based on the sequence of their 3-D
                   coordinates. Adaptive mean-shift clustering is applied
                   to extract a compact set of representative fiber-modes
                   (FM). Each FM is assigned to a multivariate Gaussian
                   distribution according to its population thereby
                   leading to a Gaussian mixture model (GMM)
                   representation for the entire set of fibers. The
                   registration between two fiber sets is treated as the
                   alignment of two GMMs and is performed by maximizing
                   their correlation ratio. A nine-parameters affine
                   transform is recovered and eventually refined to a
                   twelve-parameters affine transform using an innovative
                   mean-shift based registration refinement scheme
                   presented in this paper. The validation of the
                   algorithm on synthetic intrasubject data demonstrates
                   its robustness to interrupted and deviating fiber
                   artifacts as well as outliers. Using real intrasubject
                   data, a comparison is conducted to other intensity
                   based and fiber-based registration algorithms,
                   demonstrating competitive results. An option for
                   tracking-in-time, on specific white matter fiber
                   tracts, is also demonstrated on the real data.},
  doi            = {10.1109/TMI.2009.2029097},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Zvitia et al. - 2010 -
                   Co-registration of white matter tractographies by
                   adaptive-mean-shift and Gaussian mixture
                   modeling..pdf:pdf},
  issn           = {1558-0062},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Cluster
                   Analysis,Diffusion Tensor Imaging,Diffusion Tensor
                   Imaging: methods,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Models, Neurological,Normal
                   Distribution,Reproducibility of Results},
  month          = jan,
  pmid           = {19709970},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/19709970},
  year           = 2010
}

@Article{Martinez2007,
  Author         = {Martinez, Aleix M},
  Title          = {{Spherical-Homoscedastic Distributions : The
                   Equivalency of Spherical and Normal Distributions in
                   Classification}},
  Journal        = {Journal of Machine Learning Research},
  Volume         = {8},
  Pages          = {1583--1623},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Martinez - 2007 -
                   Spherical-Homoscedastic Distributions The Equivalency
                   of Spherical and Normal Distributions in
                   Classification.pdf:pdf},
  keywords       = {computer vision,directional data,linear and non-linear
                   classifiers,norm normalization,normal
                   distributions,spherical distributions},
  year           = 2007
}

@Article{Tu2007TransMedIm,
  Author         = {Tu, Zhuowen and Narr, Katherine L. and Dollar, Piotr
                   and Dinov, Ivo and Thompson, Paul M. and Toga, Arthur
                   W.},
  Title          = {Brain Anatomical Structure Segmentation by Hybrid
                   Discriminative/Generative Models},
  Journal        = {Transactions on Medical Imaging},
  Volume         = {in press},
  abstract       = {In this paper, a hybrid discriminative/generative
                   model for brain anatomical structure segmentation is
                   proposed. The learning aspect of the approach is
                   emphasized. In the discriminative appearance models,
                   various cues such as intensity and curvatures are
                   combined to locally capture the complex appearances of
                   different anatomical structures. A probabilistic
                   boosting tree (PBT) framework is adopted to learn
                   multi-class discriminative models that combine hundreds
                   of features across different scales. On the generative
                   side, Principal Component Analysis (PCA) shape models
                   are used to capture the global shape information about
                   each anatomical structure. The parameters to combine
                   the discriminative appearance and generative shape
                   models are also automatically learned. Thus low-level
                   and highlevel information is learned and integrated in
                   a hybrid model. Segmentations are obtained by
                   minimizing an energy function associated with the
                   proposed hybrid model. Finally, a gridface structure is
                   designed to explicitly represent the 3D region
                   topology. This representation handles an arbitrary
                   number of regions and facilitates fast surface
                   evolution. Our system was trained and tested on a set
                   of 3D MRI volumes and the results obtained are
                   encouraging.},
  file           = {attachment\:Tu2007TransMedIm.pdf:attachment\:Tu2007TransMedIm.pdf:PDF},
  year           = 2007
}

@Article{Duru2010,
  Author         = {Duru, Dilek G\"{o}ksel and Ozkan, Mehmed},
  Title          = {{Determination of neural fiber connections based on
                   data structure algorithm.}},
  Journal        = {Computational intelligence and neuroscience},
  Volume         = {2010},
  Pages          = {251928},
  abstract       = {The brain activity during perception or cognition is
                   mostly examined by functional magnetic resonance
                   imaging (fMRI). However, the cause of the detected
                   activity relies on the anatomy. Diffusion tensor
                   magnetic resonance imaging (DTMRI) as a noninvasive
                   modality providing in vivo anatomical information
                   allows determining neural fiber connections which leads
                   to brain mapping. Still a complete map of fiber paths
                   representing the human brain is missing in literature.
                   One of the main drawbacks of reliable fiber mapping is
                   the correct detection of the orientation of multiple
                   fibers within a single imaging voxel. In this study a
                   method based on linear data structures is proposed to
                   define the fiber paths regarding their diffusivity.
                   Another advantage of the proposed method is that the
                   analysis is applied on entire brain diffusion tensor
                   data. The implementation results are promising, so that
                   the method will be developed as a rapid fiber
                   tractography algorithm for the clinical use as future
                   study.},
  doi            = {10.1155/2010/251928},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Duru, Ozkan - 2010 - Determination
                   of neural fiber connections based on data structure
                   algorithm..pdf:pdf},
  issn           = {1687-5273},
  keywords       = {Algorithms,Brain,Brain: anatomy \& histology,Diffusion
                   Tensor Imaging,Diffusion Tensor Imaging:
                   methods,Humans,Image Processing,
                   Computer-Assisted,Image Processing, Computer-Assisted:
                   methods,Linear Models,Neural Pathways,Neural Pathways:
                   anatomy \& histology,Uncertainty},
  month          = jan,
  pmid           = {20069047},
  url            = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2801001\&tool=pmcentrez\&rendertype=abstract},
  year           = 2010
}

@Article{Tang1997,
  Author         = {Tang, Y and Nyengaard, J R},
  Title          = {{A stereological method for estimating the total
                   length and size of myelin fibers in human brain white
                   matter.}},
  Journal        = {Journal of neuroscience methods},
  Volume         = {73},
  Number         = {2},
  Pages          = {193--200},
  abstract       = {A practically unbiased stereological method to obtain
                   estimates of the volume and total length of nerve
                   fibers in brain white matter is described. The sampling
                   scheme is designed so that the majority of brain white
                   matter is left intact, thus providing the possibility
                   for resampling and further analysis. Uniform sampling
                   of one complete hemispherical white matter is
                   performed. The volume fraction of nerve fibers in white
                   matter is estimated by point counting. The total length
                   of nerve fibers was estimated from the product of the
                   volume of white matter, obtained with the Cavalieri
                   principle, and the fiber length density, obtained from
                   the isotropic, uniform random sections which were
                   ensured by the isector. The size of nerve fibers was
                   derived by measuring the profile diameter perpendicular
                   to its longest axis. The influence of the postmortem
                   fixation delay on nerve fiber parameters was
                   investigated in one dog and one pig. The criteria for
                   identification of nerve fiber profiles at light
                   microscopy were evaluated using electron microscopy.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tang, Nyengaard - 1997 - A
                   stereological method for estimating the total length
                   and size of myelin fibers in human brain white
                   matter..pdf:pdf},
  issn           = {0165-0270},
  keywords       = {Adolescent,Adult,Animals,Brain,Brain:
                   ultrastructure,Dogs,Female,Humans,Middle Aged,Models,
                   Neurological,Nerve Fibers, Myelinated,Nerve Fibers,
                   Myelinated: ultrastructure,Neurosciences,Neurosciences:
                   methods,Swine},
  month          = may,
  pmid           = {9196291},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/9196291},
  year           = 1997
}

@Article{margolis5nal,
  Author         = {Margolis, G. and Pickett, JP},
  Title          = {{New applications of the Luxol fast blue myelin
                   stain.}},
  Journal        = {Laboratory investigation; a journal of technical
                   methods and pathology},
  Volume         = {5},
  Number         = {6},
  Pages          = {459}
}

@Article{Ghosh2008,
  Author         = {Ghosh, Aurobrata and Tsigaridas, Elias and Descoteaux,
                   Maxime and Comon, Pierre and Mourrain, Bernard and
                   Deriche, Rachid},
  Title          = {{A polynomial based approach to extract the maxima of
                   an antipodally symmetric spherical function and its
                   application to extract fiber directions from the
                   Orientation Distribution Function in Diffusion MRI}},
  Journal        = {Tensor},
  Pages          = {237--248},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Ghosh et al. - 2008 - A polynomial
                   based approach to extract the maxima of an antipodally
                   symmetric spherical function and its application to
                   extract fiber directions from the Orientation
                   Distribution Function in Diffusion MRI.pdf:pdf},
  year           = 2008
}

@Article{HTJ+03,
  Author         = {Hagmann, P. and Thiran, J. P. and Jonasson, L. and
                   Vandergheynst, P. and Clarke, S. and Maeder, P. and
                   Meuli, R.},
  Title          = {D{TI} mapping of human brain connectivity: statistical
                   fibre tracking and virtual dissection.},
  Journal        = {Neuroimage},
  Volume         = {19},
  Number         = {3},
  Pages          = {545-54},
  abstract       = {Several approaches have been used to trace axonal
                   trajectories from diffusion MRI data. If such
                   techniques were first developed in a deterministic
                   framework reducing the diffusion information to one
                   single main direction, more recent approaches emerged
                   that were statistical in nature and that took into
                   account the whole diffusion information. Based on
                   diffusion tensor MRI data coming from normal brains,
                   this paper presents how brain connectivity could be
                   modelled globally by means of a random walk algorithm.
                   The mass of connections thus generated was then
                   virtually dissected to uncover different tracts.
                   Corticospinal, corticobulbar, and corticothalamic
                   tracts, the corpus callosum, the limbic system, several
                   cortical association bundles, the cerebellar peduncles,
                   and the medial lemniscus were all investigated. The
                   results were then displayed in the form of an in vivo
                   brain connectivity atlas. The connectivity pattern and
                   the individual fibre tracts were then compared to known
                   anatomical data; a good matching was found.},
  authoraddress  = {Signal Processing Institute, Swiss Federal Institute
                   of Technology, 1015 Lausanne, Switzerland.
                   patric.hagmann@epfl.ch},
  keywords       = {Algorithms ; Axons/physiology ; Brain/*anatomy \&
                   histology ; *Brain Mapping ; Cerebellum/anatomy \&
                   histology/physiology ; Cerebral Cortex/anatomy \&
                   histology/physiology ; Computer Graphics ; Humans ;
                   Image Processing, Computer-Assisted ; Magnetic
                   Resonance Imaging ; Models, Neurological ; Nerve
                   Fibers/*physiology ; Neural Pathways/*anatomy \&
                   histology ; Pyramidal Tracts/anatomy \&
                   histology/physiology ; Thalamus/anatomy \&
                   histology/physiology},
  language       = {eng},
  medline-aid    = {S1053811903001423 [pii]},
  medline-crdt   = {2003/07/26 05:00},
  medline-da     = {20030725},
  medline-dcom   = {20030909},
  medline-edat   = {2003/07/26 05:00},
  medline-fau    = {Hagmann, P ; Thiran, J-P ; Jonasson, L ;
                   Vandergheynst, P ; Clarke, S ; Maeder, P ; Meuli, R},
  medline-is     = {1053-8119 (Print)},
  medline-jid    = {9215515},
  medline-jt     = {NeuroImage},
  medline-lr     = {20041117},
  medline-mhda   = {2003/09/10 05:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {12880786},
  medline-pst    = {ppublish},
  medline-pt     = {Clinical Trial ; Journal Article},
  medline-sb     = {IM},
  medline-so     = {Neuroimage. 2003 Jul;19(3):545-54.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=12880786},
  year           = 2003
}

@Article{Wassermann2004,
  Author         = {Wassermann, Demian and Deriche, Rachid},
  Title          = {{Simultaneous Manifold Learning and Clustering :
                   Grouping White Matter Fiber Tracts Using a Volumetric
                   White Matter Atlas}},
  Journal        = {International Journal of Computer Vision},
  Pages          = {1--8},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wassermann, Deriche - 2004 -
                   Simultaneous Manifold Learning and Clustering Grouping
                   White Matter Fiber Tracts Using a Volumetric White
                   Matter Atlas.pdf:pdf},
  year           = 2004
}

@Article{BKK+04,
  Author         = {Bodammer, N. and Kaufmann, J. and Kanowski, M. and
                   Tempelmann, C.},
  Title          = {Eddy current correction in diffusion-weighted imaging
                   using pairs of images acquired with opposite diffusion
                   gradient polarity.},
  Journal        = {Magn Reson Med},
  Volume         = {51},
  Number         = {1},
  Pages          = {188-93},
  abstract       = {In echo-planar-based diffusion-weighted imaging (DWI)
                   and diffusion tensor imaging (DTI), the evaluation of
                   diffusion parameters such as apparent diffusion
                   coefficients and anisotropy indices is affected by
                   image distortions that arise from residual eddy
                   currents produced by the diffusion-sensitizing
                   gradients. Correction methods that coregister
                   diffusion-weighted and non-diffusion-weighted images
                   suffer from the different contrast properties inherent
                   in these image types. Here, a postprocessing correction
                   scheme is introduced that makes use of the inverse
                   characteristics of distortions generated by gradients
                   with reversed polarity. In this approach, only
                   diffusion-weighted images with identical contrast are
                   included for correction. That is,
                   non-diffusion-weighted images are not needed as a
                   reference for registration. Furthermore, the
                   acquisition of an additional dataset with moderate
                   diffusion-weighting as suggested by Haselgrove and
                   Moore (Magn Reson Med 1996;36:960-964) is not required.
                   With phantom data it is shown that the theoretically
                   expected symmetry of distortions is preserved in the
                   images to a very high degree, demonstrating the
                   practicality of the new method. Results from human
                   brain images are also presented.},
  authoraddress  = {Department of Neurology II, Otto von Guericke
                   University Magdeburg, Germany.
                   bodammer@neuro2.med.uni-magdeburg.de},
  keywords       = {Algorithms ; Brain/*anatomy \& histology ; Diffusion
                   Magnetic Resonance Imaging/*methods ; Humans ; *Image
                   Processing, Computer-Assisted ; Phantoms, Imaging},
  language       = {eng},
  medline-aid    = {10.1002/mrm.10690 [doi]},
  medline-ci     = {Copyright 2003 Wiley-Liss, Inc.},
  medline-crdt   = {2004/01/06 05:00},
  medline-da     = {20040105},
  medline-dcom   = {20040507},
  medline-edat   = {2004/01/06 05:00},
  medline-fau    = {Bodammer, Nils ; Kaufmann, Jorn ; Kanowski, Martin ;
                   Tempelmann, Claus},
  medline-is     = {0740-3194 (Print)},
  medline-jid    = {8505245},
  medline-jt     = {Magnetic resonance in medicine : official journal of
                   the Society of Magnetic Resonance in Medicine / Society
                   of Magnetic Resonance in Medicine},
  medline-lr     = {20061115},
  medline-mhda   = {2004/05/08 05:00},
  medline-own    = {NLM},
  medline-pl     = {United States},
  medline-pmid   = {14705060},
  medline-pst    = {ppublish},
  medline-pt     = {Journal Article ; Research Support, Non-U.S. Gov't},
  medline-sb     = {IM},
  medline-so     = {Magn Reson Med. 2004 Jan;51(1):188-93.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=14705060},
  year           = 2004
}

@Article{Correia2009b,
  Author         = {Correia, Stephen and Lee, Stephanie Y and Voorn, Thom
                   and Tate, David F and Paul, Robert H and Salloway,
                   Stephen P and Malloy, Paul F and Laidlaw, David H},
  Title          = {{NIH Public Access}},
  Journal        = {Water},
  Volume         = {42},
  Number         = {2},
  Pages          = {568--581},
  doi            = {10.1016/j.neuroimage.2008.05.022.Quantitative},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Correia et al. - 2009 - NIH Public
                   Access.pdf:pdf},
  year           = 2009
}

@Article{Wang1999,
  Author         = {Wang, Y and Berg, P and Scherg, M},
  Title          = {{Common spatial subspace decomposition applied to
                   analysis of brain responses under multiple task
                   conditions: a simulation study.}},
  Journal        = {Clinical neurophysiology : official journal of the
                   International Federation of Clinical Neurophysiology},
  Volume         = {110},
  Number         = {4},
  Pages          = {604--14},
  abstract       = {A method, called common spatial subspace
                   decomposition, is presented which can extract signal
                   components specific to one condition from multiple
                   magnetoencephalography/electroencephalography data sets
                   of multiple task conditions. Signal matrices or
                   covariance matrices are decomposed using spatial
                   factors common to multiple conditions. The spatial
                   factors and corresponding spatial filters are then
                   dissociated into specific and common parts, according
                   to the common spatial subspace which exists among the
                   data sets. Finally, the specific signal components are
                   extracted using the corresponding spatial filters and
                   spatial factors. The relationship between this
                   decomposition and spatio-temporal source models is
                   described in this paper. Computer simulations suggest
                   that this method can facilitate the analysis of brain
                   responses under multiple task conditions and merits
                   further application.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Wang, Berg, Scherg - 1999 - Common
                   spatial subspace decomposition applied to analysis of
                   brain responses under multiple task conditions a
                   simulation study..pdf:pdf},
  issn           = {1388-2457},
  keywords       = {Brain,Brain Mapping,Brain: physiology,Computer
                   Simulation,Humans,Models, Neurological,Task Performance
                   and Analysis},
  month          = apr,
  pmid           = {10378728},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/10378728},
  year           = 1999
}

@Article{Zhai2003,
  Author         = {Zhai, Guihua and Lin, Weili and Wilber, Kathy P and
                   Gerig, Guido and Gilmore, John H},
  Title          = {{Comparisons of regional white matter diffusion in
                   healthy neonates and adults performed with a 3.0-T
                   head-only MR imaging unit.}},
  Journal        = {Radiology},
  Volume         = {229},
  Number         = {3},
  Pages          = {673--81},
  abstract       = {PURPOSE: To evaluate the normal brains of adults and
                   neonates for regional and age-related differences in
                   apparent diffusion coefficient (ADC) and fractional
                   anisotropy (FA). MATERIALS AND METHODS: Eight healthy
                   adults and 20 healthy neonates were examined with a
                   3.0-T head-only magnetic resonance (MR) imaging unit by
                   using a single-shot diffusion-tensor sequence. Trace
                   ADC maps, FA maps, directional maps of the putative
                   directions of white matter (WM) tracts, and
                   fiber-tracking maps were obtained. Regions of
                   interest-eight in WM and one in gray matter (GM)-were
                   predefined for the ADC and FA measurements. The Student
                   t test was used to compare FA and ADC between adults
                   and neonates, whereas the Tukey multiple-comparison
                   test was used to compare FA and ADC in different brain
                   regions in the adult and neonate groups. RESULTS: A
                   global elevation in ADC (P <.001) in both GM and WM and
                   a reduction in FA (P <.001) in WM were observed in
                   neonates as compared with these values in adults. In
                   addition, significant regional variations in FA and ADC
                   were observed in both groups. Regional variations in FA
                   and ADC were less remarkable in adults, whereas
                   neonates had consistently higher FA values and lower
                   ADC values in the central WM as compared with these
                   values in the peripheral WM. Fiber tracking revealed
                   only major WM tracts in the neonates but fibers
                   extending to the peripheral WM in the adults.
                   CONCLUSION: There were regional differences in FA and
                   ADC values in the neonates; such variations were less
                   remarkable in the adults.},
  doi            = {10.1148/radiol.2293021462},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Zhai et al. - 2003 - Comparisons of
                   regional white matter diffusion in healthy neonates and
                   adults performed with a 3.0-T head-only MR imaging
                   unit..pdf:pdf},
  issn           = {0033-8419},
  keywords       = {Adult,Age Factors,Brain,Brain: anatomy \&
                   histology,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   instrumentat,Humans,Infant, Newborn,ROC Curve},
  month          = dec,
  pmid           = {14657305},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/14657305},
  year           = 2003
}

@Article{Fillard2009,
  Author         = {Fillard, P. and Poupon, C. and Mangin, J.F.},
  Title          = {{Spin Tracking: A Novel Global Tractography Algorithm}},
  Journal        = {NeuroImage},
  Volume         = {47},
  Pages          = {S127--S127},
  doi            = {10.1016/S1053-8119(09)71230-3},
  issn           = {10538119},
  url            = {http://dx.doi.org/10.1016/S1053-8119(09)71230-3},
  year           = 2009
}

@Article{Behrens2003NatureNeuroscience,
  Author         = {Behrens, T E J and Johansen-Berg, H and Woolrich, M W
                   and Wheeler-Kingshott, C A M and Boulby, P A and
                   Barker, G J and Sillery, E L and Sheehan, K and
                   Ciccarellu, O and Thompson, A J and Brady, J M and
                   Matthews, P M},
  Title          = {Non-invasive mapping of connections between human
                   thalamus and cortex using diffusion imaging},
  Journal        = {Nature Neuroscience},
  Volume         = {6},
  Number         = {7},
  Pages          = {750-757},
  abstract       = {Evidence concerning anatomical connectivities in the
                   human brain is sparse and based largely on limited
                   post-mortem observations. Diffusion tensor imaging has
                   previously been used to define large white-matter
                   tracts in the living human brain, but this technique
                   has had limited success in tracing pathways into gray
                   matter. Here we identified specific connections between
                   human thalamus and cortex using a novel probabilistic
                   tractography algorithm with diffusion imaging data.
                   Classification of thalamic gray matter based on
                   cortical connectivity patterns revealed distinct
                   subregions whose locations correspond to nuclei
                   described previously in histological studies. The
                   connections that we found between thalamus and cortex
                   were similar to those reported for non-human primates
                   and were reproducible between individuals. Our results
                   provide the first quantitative demonstration of
                   reliable inference of anatomical connectivity between
                   human gray matter structures using diffusion data and
                   the first connectivity-based segmentation of gray
                   matter.},
  file           = {attachment\:Behrens2003NatureNeuroscience.pdf:attachment\:Behrens2003NatureNeuroscience.pdf:PDF},
  publisher      = {Nature Publishing Group},
  year           = 2003
}

@Article{Joya,
  Author         = {Joy, Kenneth I},
  Title          = {{Numerical Methods for Particle Tracing in Vector
                   Fields}},
  Journal        = {Science},
  Pages          = {1--7},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Joy - Unknown - Numerical Methods
                   for Particle Tracing in Vector Fields.pdf:pdf}
}

@Article{Blankertz2008,
  Author         = {Blankertz, Benjamin and Tomioka, Ryota and Lemm,
                   Steven and Kawanabe, Motoaki and M\"{u}ller,
                   Klaus-robert},
  Title          = {{Optimizing Spatial Filters for Robust EEG
                   Single-Trial Analysis}},
  Journal        = {Signal Processing},
  Volume         = {XX},
  Pages          = {1--12},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Blankertz et al. - 2008 - Optimizing
                   Spatial Filters for Robust EEG Single-Trial
                   Analysis.pdf:pdf},
  year           = 2008
}

@Article{WirestamMRM2006,
  Author         = {Wirestam, R. and Bibic, A. and Latt, J. and
                   Brockstedt, S. and Stahlberg, F.},
  Title          = {{Denoising of complex MRI data by wavelet-domain
                   filtering: Application to high-b-value
                   diffusion-weighted imaging}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {56},
  Number         = {5},
  publisher      = {Wiley Subscription Services, Inc., A Wiley Company
                   Hoboken},
  year           = 2006
}

@Article{Lenglet2010,
  Author         = {Lenglet, Christophe and Series, I M A Preprint and
                   Hall, Lind and E, Church Street S and Aganj, Iman and
                   Sapiro, Guillermo},
  Title          = {{ODF MAXIMA EXTRACTION IN INSTITUTE FOR MATHEMATICS
                   AND ITS APPLICATIONS ODF Maxima Extraction in Spherical
                   Harmonic Representation via Analytical Search Space
                   Reduction}},
  Journal        = {Methods},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Lenglet et al. - 2010 - ODF MAXIMA
                   EXTRACTION IN INSTITUTE FOR MATHEMATICS AND ITS
                   APPLICATIONS ODF Maxima Extraction in Spherical
                   Harmonic Representation via Analytical Search Space
                   Reduction.pdf:pdf},
  year           = 2010
}

@Article{Bai2009,
  Author         = {Bai, Y},
  Title          = {{Correcting for Motion between Acquisitions in
                   Diffusion MR Imaging}},
  Journal        = {Chart},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Bai - 2009 - Correcting for Motion
                   between Acquisitions in Diffusion MR Imaging.pdf:pdf},
  year           = 2009
}

@Book{mcrobbie2006mpp,
  Author         = {McRobbie, D.W. and Moore, E.A. and Graves, M.J.},
  Title          = {{MRI from Picture to Proton}},
  Publisher      = {Cambridge University Press},
  year           = 2006
}

@Article{Tang1997a,
  Author         = {Tang, Y and Nyengaard, J R},
  Title          = {{A stereological method for estimating the total
                   length and size of myelin fibers in human brain white
                   matter.}},
  Journal        = {Journal of neuroscience methods},
  Volume         = {73},
  Number         = {2},
  Pages          = {193--200},
  abstract       = {A practically unbiased stereological method to obtain
                   estimates of the volume and total length of nerve
                   fibers in brain white matter is described. The sampling
                   scheme is designed so that the majority of brain white
                   matter is left intact, thus providing the possibility
                   for resampling and further analysis. Uniform sampling
                   of one complete hemispherical white matter is
                   performed. The volume fraction of nerve fibers in white
                   matter is estimated by point counting. The total length
                   of nerve fibers was estimated from the product of the
                   volume of white matter, obtained with the Cavalieri
                   principle, and the fiber length density, obtained from
                   the isotropic, uniform random sections which were
                   ensured by the isector. The size of nerve fibers was
                   derived by measuring the profile diameter perpendicular
                   to its longest axis. The influence of the postmortem
                   fixation delay on nerve fiber parameters was
                   investigated in one dog and one pig. The criteria for
                   identification of nerve fiber profiles at light
                   microscopy were evaluated using electron microscopy.},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Tang, Nyengaard - 1997 - A
                   stereological method for estimating the total length
                   and size of myelin fibers in human brain white
                   matter..pdf:pdf},
  issn           = {0165-0270},
  keywords       = {Adolescent,Adult,Animals,Brain,Brain:
                   ultrastructure,Dogs,Female,Humans,Middle Aged,Models,
                   Neurological,Nerve Fibers, Myelinated,Nerve Fibers,
                   Myelinated: ultrastructure,Neurosciences,Neurosciences:
                   methods,Swine},
  month          = may,
  pmid           = {9196291},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/9196291},
  year           = 1997
}

@Article{Bullmore2009,
  Author         = {Bullmore, E and Sporns, O},
  Title          = {{Complex brain networks: graph theoretical analysis of
                   structural and functional systems}},
  Journal        = {Nature Reviews Neuroscience},
  Volume         = {10},
  Number         = {3},
  Pages          = {186--198},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Bullmore, Sporns - 2009 - Complex
                   brain networks graph theoretical analysis of structural
                   and functional systems.pdf:pdf},
  year           = 2009
}

@Article{Pajevic1999,
  Author         = {Pajevic, Sinisa and Pierpaoli, Carlo},
  Title          = {{Color schemes to represent the orientation of
                   anisotropic tissues from diffusion tensor data:
                   Application to white matter fiber tract mapping in the
                   human brain}},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {42},
  Number         = {3},
  abstract       = {This paper investigates the use of color to represent
                   the directional information contained in the diffusion
                   tensor. Ideally, one wants to take into account both
                   the properties of human color vision and of the given
                   display hardware to produce a representation in which
                   differences in the orientation of anisotropic
                   structures are proportional to the perceived
                   differences in color. It is argued here that such a
                   goal cannot be achieved in general and therefore,
                   empirical or heuristic schemes, which avoid some of the
                   common artifacts of previously proposed approaches, are
                   implemented. Directionally encoded color (DEC) maps of
                   the human brain obtained using these schemes clearly
                   show the main association, projection, and commissural
                   white matter pathways. In the brainstem, motor and
                   sensory pathways are easily identified and can be
                   differentiated from the transverse pontine fibers and
                   the cerebellar peduncles. DEC maps obtained from
                   diffusion tensor imaging data provide a simple and
                   effective way to visualize fiber direction, useful for
                   investigating the structural anatomy of different
                   organs. Magn Reson Med 42:526-540, 1999. © 1999
                   Wiley-Liss, Inc.},
  doi            = {10.1002/(SICI)1522-2594(199909)42:3<526::AID-MRM15>3.0.CO;2-J},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Pajevic, Pierpaoli - 1999 - Color
                   schemes to represent the orientation of anisotropic
                   tissues from diffusion tensor data Application to white
                   matter fiber tract mapping in the human brain.pdf:pdf},
  url            = {http://www3.interscience.wiley.com/journal/63500786/abstract},
  year           = 1999
}

@Article{DauguetNeuroImage2007,
  Author         = {Dauguet, J. and Peled, S. and Berezovskii, V. and
                   Delzescaux, T. and Warfield, S. K. and Born, R. and
                   Westin, C. F.},
  Title          = {Comparison of fiber tracts derived from in-vivo {DTI}
                   tractography with 3{D} histological neural tract tracer
                   reconstruction on a macaque brain.},
  Journal        = {Neuroimage},
  Volume         = {37},
  Number         = {2},
  Pages          = {530-8},
  abstract       = {Since the introduction of diffusion weighted imaging
                   (DWI) as a method for examining neural connectivity,
                   its accuracy has not been formally evaluated. In this
                   study, we directly compared connections that were
                   visualized using injected neural tract tracers
                   (WGA-HRP) with those obtained using in-vivo diffusion
                   tensor imaging (DTI) tractography. First, we injected
                   the tracer at multiple sites in the brain of a macaque
                   monkey; second, we reconstructed the histological
                   sections of the labeled fiber tracts in 3D; third, we
                   segmented and registered the fibers (somatosensory and
                   motor tracts) with the anatomical in-vivo MRI from the
                   same animal; and last, we conducted fiber tracing along
                   the same pathways on the DTI data using a classical
                   diffusion tracing technique with the injection sites as
                   seeds. To evaluate the performance of DTI fiber
                   tracing, we compared the fibers derived from the DTI
                   tractography with those segmented from the histology.
                   We also studied the influence of the parameters
                   controlling the tractography by comparing Dice
                   superimposition coefficients between histology and DTI
                   segmentations. While there was generally good visual
                   agreement between the two methods, our quantitative
                   comparisons reveal certain limitations of DTI
                   tractography, particularly for regions at remote
                   locations from seeds. We have thus demonstrated the
                   importance of appropriate settings for realistic
                   tractography results.},
  authoraddress  = {Computational Radiology Laboratory, Children's
                   Hospital, Harvard Medical School, Boston, USA.
                   dauguet@bwh.harvard.edu},
  keywords       = {Animals ; Anisotropy ; Brain/*anatomy \& histology ;
                   *Diffusion Magnetic Resonance Imaging ; Image
                   Processing, Computer-Assisted ; *Imaging,
                   Three-Dimensional ; Immunohistochemistry ; Macaca ;
                   Nerve Fibers/ultrastructure ; Neural Pathways/*cytology},
  language       = {eng},
  medline-aid    = {S1053-8119(07)00328-X [pii] ;
                   10.1016/j.neuroimage.2007.04.067 [doi]},
  medline-crdt   = {2007/07/03 09:00},
  medline-da     = {20070730},
  medline-dcom   = {20071012},
  medline-dep    = {20070524},
  medline-edat   = {2007/07/03 09:00},
  medline-fau    = {Dauguet, Julien ; Peled, Sharon ; Berezovskii,
                   Vladimir ; Delzescaux, Thierry ; Warfield, Simon K ;
                   Born, Richard ; Westin, Carl-Fredrik},
  medline-gr     = {P01 HD18655/HD/NICHD NIH HHS/United States ;
                   P30-EY12196/EY/NEI NIH HHS/United States ; P41
                   RR013218/RR/NCRR NIH HHS/United States ; R01
                   HL074942/HL/NHLBI NIH HHS/United States ; R01
                   RR021885/RR/NCRR NIH HHS/United States ;
                   R01-MH50747/MH/NIMH NIH HHS/United States ; R21
                   MH067054/MH/NIMH NIH HHS/United States ; U41
                   RR019703/RR/NCRR NIH HHS/United States ; U54
                   EB005149/EB/NIBIB NIH HHS/United States},
  medline-is     = {1053-8119 (Print)},
  medline-jid    = {9215515},
  medline-jt     = {NeuroImage},
  medline-lr     = {20071203},
  medline-mhda   = {2007/10/13 09:00},
  medline-own    = {NLM},
  medline-phst   = {2007/01/25 [received] ; 2007/04/05 [revised] ;
                   2007/04/10 [accepted] ; 2007/05/24 [aheadofprint]},
  medline-pl     = {United States},
  medline-pmid   = {17604650},
  medline-pst    = {ppublish},
  medline-pt     = {Comparative Study ; Journal Article ; Research
                   Support, N.I.H., Extramural ; Research Support,
                   Non-U.S. Gov't ; Research Support, U.S. Gov't,
                   Non-P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Neuroimage. 2007 Aug 15;37(2):530-8. Epub 2007 May 24.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17604650},
  year           = 2007
}

@Article{Jonasson2007,
  Author         = {Jonasson, Lisa and Bresson, Xavier and Thiran,
                   Jean-Philippe and Wedeen, Van J and Hagmann, Patric},
  Title          = {{Representing diffusion MRI in 5-D simplifies
                   regularization and segmentation of white matter
                   tracts.}},
  Journal        = {IEEE transactions on medical imaging},
  Volume         = {26},
  Number         = {11},
  Pages          = {1547--54},
  abstract       = {We present a new five-dimensional (5-D) space
                   representation of diffusion magnetic resonance imaging
                   (dMRI) of high angular resolution. This 5-D space is
                   basically a non-Euclidean space of position and
                   orientation in which crossing fiber tracts can be
                   clearly disentangled, that cannot be separated in
                   three-dimensional position space. This new
                   representation provides many possibilities for
                   processing and analysis since classical methods for
                   scalar images can be extended to higher dimensions even
                   if the spaces are not Euclidean. In this paper, we show
                   examples of how regularization and segmentation of dMRI
                   is simplified with this new representation. The
                   regularization is used with the purpose of denoising
                   and but also to facilitate the segmentation task by
                   using several scales, each scale representing a
                   different level of resolution. We implement in five
                   dimensions the Chan-Vese method combined with active
                   contours without edges for the segmentation and the
                   total variation functional for the regularization. The
                   purpose of this paper is to explore the possibility of
                   segmenting white matter structures directly as entirely
                   separated bundles in this 5-D space. We will present
                   results from a synthetic model and results on real data
                   of a human brain acquired with diffusion spectrum
                   magnetic resonance imaging (MRI), one of the dMRI of
                   high angular resolution available. These results will
                   lead us to the conclusion that this new
                   high-dimensional representation indeed simplifies the
                   problem of segmentation and regularization.},
  doi            = {10.1109/TMI.2007.899168},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Jonasson et al. - 2007 -
                   Representing diffusion MRI in 5-D simplifies
                   regularization and segmentation of white matter
                   tracts..pdf:pdf},
  issn           = {0278-0062},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   anatomy \& histology,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Humans,Image Enhancement,Image Enhancement:
                   methods,Image Interpretation, Computer-Assisted,Image
                   Interpretation, Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Nerve Fibers, Myelinated,Nerve Fibers,
                   Myelinated: ultrastructure,Pattern Recognition,
                   Automated,Pattern Recognition, Automated:
                   methods,Reproducibility of Results,Sensitivity and
                   Specificity},
  month          = nov,
  pmid           = {18041269},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/18041269},
  year           = 2007
}

@Article{Frenkel2003,
  Author         = {Frenkel, Max and Basri, Ronen},
  Title          = {{Using the Fast Marching Method}},
  Pages          = {35--51},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Frenkel, Basri - 2003 - Using the
                   Fast Marching Method.pdf:pdf},
  year           = 2003
}

@Article{Laidlaw,
  Author         = {Laidlaw, David H},
  Title          = {{Similarity Coloring of DTI Fiber Tracts}},
  Journal        = {Science},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Laidlaw - Unknown - Similarity
                   Coloring of DTI Fiber Tracts.pdf:pdf}
}

@Article{Parker2004BJR,
  Author         = {Parker, G J M},
  Title          = {{Analysis of MR diffusion weighted images}},
  Journal        = {Br J Radiol},
  Volume         = {77},
  Number         = {suppl_2},
  Pages          = {S176-185},
  abstract       = {Diffusion-weighted MR images provide information that
                   is present in no other imaging modality. Whilst some of
                   this information may be appreciated visually in
                   diffusion weighted images, much of it may be extracted
                   only with the aid of data post-processing. This review
                   summarizes the methods available for interpreting
                   diffusion weighted imaging (DWI) information using the
                   diffusion tensor and other models of the DWI signal.
                   This is followed by an overview of methods that allow
                   the estimation of fibre tract orientation and that
                   provide estimates of the routes and degree of
                   anatomical cerebral white matter connectivity. },
  doi            = {10.1259/bjr/81090732},
  eprint         = {http://bjr.birjournals.org/cgi/reprint/77/suppl_2/S176.pdf},
  file           = {attachment\:Parker2004BJR.pdf:attachment\:Parker2004BJR.pdf:PDF},
  url            = {http://bjr.birjournals.org/cgi/content/abstract/77/suppl_2/S176},
  year           = 2004
}

@Article{Prentice1984,
  Author         = {Prentice, Michael J.},
  Title          = {{A distribution-free method of interval estimation for
                   unsigned directional data}},
  Journal        = {Biometrika},
  Volume         = {71},
  Number         = {1},
  Pages          = {147--154},
  doi            = {10.1093/biomet/71.1.147},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Prentice - 1984 - A
                   distribution-free method of interval estimation for
                   unsigned directional data.pdf:pdf},
  issn           = {0006-3444},
  url            = {http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/71.1.147},
  year           = 1984
}

@Article{MelieGarcia2008NeuroImage,
  Author         = {Melie-Garcia, Lester and Canales-Rodriguez, Erick J.
                   and Aleman-Gomez, Yasser and Lin, Ching-Po and
                   Iturria-Medina, Yasser and Valdes-Hernandez, Pedro A. },
  Title          = {A bayesian framework to identify principal intravoxel
                   diffusion profiles based on diffusion-weighted
                   \{{M}{R}\} imaging},
  Journal        = {NeuroImage},
  Volume         = {42},
  Number         = {2},
  Pages          = {750-770},
  abstract       = {In this paper we introduce a new method to
                   characterize the intravoxel anisotropy based on
                   diffusion-weighted imaging (DWI). The proposed
                   solution, under a fully Bayesian formalism, deals with
                   the problem of joint Bayesian Model selection and
                   parameter estimation to reconstruct the principal
                   diffusion profiles or primary fiber orientations in a
                   voxel. We develop an efficient stochastic algorithm
                   based on the reversible jump Markov chain Monte Carlo
                   (RJMCMC) method in order to perform the Bayesian
                   computation. RJMCMC is a good choice for this problem
                   because of its ability to jump between models of
                   different dimensionality. This methodology provides
                   posterior estimates of the parameters of interest
                   (fiber orientation, diffusivities etc) unconditional of
                   the model assumed. It also gives an empirical posterior
                   distribution of the number of primary nerve fiber
                   orientations given the DWI data. Different probability
                   maps can be assessed using this methodology: 1) the
                   intravoxel fiber orientation map (or orientational
                   distribution function) that gives the probability of
                   finding a fiber in a particular spatial orientation; 2)
                   a three-dimensional map of the probability of finding a
                   particular number of fibers in each voxel; 3) a
                   three-dimensional MaxPro (maximum probability) map that
                   provides the most probable number of fibers for each
                   voxel. In order to study the performance and
                   reliability of the presented approach, we tested it on
                   synthetic data; an ex-vivo phantom of intersecting
                   capillaries; and DWI data from a human subject.},
  file           = {attachment\:MelieGarcia2008NeuroImage.pdf:attachment\:MelieGarcia2008NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4SD6SK8-3/2/8c1ea05184c975fa63eb37b877737d9f},
  year           = 2008
}

@Article{Dougherty2005,
  Author         = {Dougherty, Robert F and Ben-Shachar, Michal and
                   Bammer, Roland and Brewer, Alyssa a and Wandell, Brian
                   a},
  Title          = {{Functional organization of human occipital-callosal
                   fiber tracts.}},
  Journal        = {Proceedings of the National Academy of Sciences of the
                   United States of America},
  Volume         = {102},
  Number         = {20},
  Pages          = {7350--5},
  abstract       = {Diffusion tensor imaging (DTI) and fiber tracking (FT)
                   were used to measure the occipital lobe fiber tracts
                   connecting the two hemispheres in individual human
                   subjects. These tracts are important for normal vision.
                   Also, damage to portions of these tracts is associated
                   with alexia. To assess the reliability of the DTI-FT
                   measurements, occipital-callosal projections were
                   estimated from each subject's left and right
                   hemispheres independently. The left and right estimates
                   converged onto the same positions within the splenium.
                   We further characterized the properties of the
                   estimated occipital-callosal fiber tracts by combining
                   them with functional MRI. We used functional MRI to
                   identify visual field maps in cortex and labeled fibers
                   by the cortical functional response at the fiber
                   endpoint. This labeling reveals a regular organization
                   of the fibers within the splenium. The dorsal visual
                   maps (dorsal V3, V3A, V3B, V7) send projections through
                   a large band in the middle of the splenium, whereas
                   ventral visual maps (ventral V3, V4) send projections
                   through the inferior-anterior corner of the splenium.
                   The agreement between the independent left/right
                   estimates, further supported by previous descriptions
                   of homologous tracts in macaque, validates the DTI-FT
                   methods. However, a principal limitation of these
                   methods is low sensitivity: a large number of fiber
                   tracts that connect homotopic regions of ventral and
                   lateral visual cortex were undetected. We conclude that
                   most of the estimated tracts are real and can be
                   localized with a precision of 1-2 mm, but many tracts
                   are missed because of data and algorithm limitations.},
  doi            = {10.1073/pnas.0500003102},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Dougherty et al. - 2005 - Functional
                   organization of human occipital-callosal fiber
                   tracts..pdf:pdf},
  issn           = {0027-8424},
  keywords       = {Adult,Algorithms,Brain Mapping,Corpus Callosum,Corpus
                   Callosum: cytology,Echo-Planar Imaging,Echo-Planar
                   Imaging: methods,Female,Humans,Magnetic Resonance
                   Imaging,Male,Middle Aged,Occipital Lobe,Occipital Lobe:
                   cytology,Visual Fields,Visual Fields: physiology},
  month          = may,
  pmid           = {15883384},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/15883384},
  year           = 2005
}

@Article{Behrens2007NeuroImage,
  Author         = {Behrens, T.E.J. and Johansen-Berg, H. and Jbabdi, S.
                   and Rushworth, M.F.S. and Woolrich, M.W.},
  Title          = {Probabilistic diffusion tractography with multiple
                   fibre orientations: What can we gain?},
  Journal        = {NeuroImage},
  Volume         = {34},
  Number         = {1},
  Pages          = {144-155},
  abstract       = {We present a direct extension of probabilistic
                   diffusion tractography to the case of multiple fibre
                   orientations. Using automatic relevance determination,
                   we are able to perform online selection of the number
                   of fibre orientations supported by the data at each
                   voxel, simplifying the problem of tracking in a
                   multi-orientation field. We then apply the identical
                   probabilistic algorithm to tractography in the multi-
                   and single-fibre cases in a number of example systems
                   which have previously been tracked successfully or
                   unsuccessfully with single-fibre tractography. We show
                   that multi-fibre tractography offers significant
                   advantages in sensitivity when tracking non-dominant
                   fibre populations, but does not dramatically change
                   tractography results for the dominant pathways.},
  file           = {attachment\:Behrens2007NeuroImage.pdf:attachment\:Behrens2007NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4M6SBH3-4/2/043728426dfb426bd39df3b8d3751bed},
  year           = 2007
}

@Article{Catani2002NeuroImage,
  Author         = {Catani, Marco and Howard, Robert J. and Pajevic,
                   Sinisa and Jones, Derek K.},
  Title          = {Virtual {in vivo} interactive dissection of white
                   matter fasciculi in the human brain },
  Journal        = {NeuroImage},
  Volume         = {17},
  Pages          = {77-94},
  abstract       = {This work reports the use of diffusion tensor magnetic
                   resonance tractography to visualize the
                   three-dimensional (3D) structure of the major white
                   matter fasciculi within living human brain.
                   Specifically, we applied this technique to visualize in
                   vivo (i) the superior longitudinal (arcuate)
                   fasciculus, (ii) the inferior longitudinal fasciculus,
                   (iii) the superior fronto-occipital (subcallosal)
                   fasciculus, (iv) the inferior frontooccipital
                   fasciculus, (v) the uncinate fasciculus, (vi) the
                   cingulum, (vii) the anterior commissure, (viii) the
                   corpus callosum, (ix) the internal capsule, and (x) the
                   fornix. These fasciculi were first isolated and were
                   then interactively displayed as a 3D-rendered object.
                   The virtual tract maps obtained in vivo using this
                   approach were faithful to the classical descriptions of
                   white matter anatomy that have previously been
                   documented in postmortem studies. Since we have been
                   able to interactively delineate and visualize white
                   matter fasciculi over their entire length in vivo, in a
                   manner that has only previously been possible by
                   histological means, virtual in vivo interactive
                   dissection (VIVID) adds a new dimension to anatomical
                   descriptions of the living human brain.},
  doi            = {10.1006/nimg.2002.1136},
  file           = {attachment\:Catani2002NeuroImage.pdf:attachment\:Catani2002NeuroImage.pdf:PDF},
  publisher      = {Elsevier},
  year           = 2002
}

@Article{Marinucci2008a,
  Author         = {Marinucci, D and Pietrobon, D and Balbi, A and Baldi,
                   P and Cabella, P and Kerkyacharian, G and Natoli, P and
                   Picard, D and Vittorio, N},
  Title          = {{Spherical Needlets for CMB Data Analysis}},
  Volume         = {000},
  Number         = {February},
  arxivid        = {arXiv:0707.0844v1},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Marinucci et al. - 2008 - Spherical
                   Needlets for CMB Data Analysis.pdf:pdf},
  year           = 2008
}

@Article{DoughertyPNAS2005,
  Author         = {Dougherty, R. F. and Ben-Shachar, M. and Bammer, R.
                   and Brewer, A. A. and Wandell, B. A.},
  Title          = {Functional organization of human occipital-callosal
                   fiber tracts.},
  Journal        = {Proc Natl Acad Sci U S A},
  Volume         = {102},
  Number         = {20},
  Pages          = {7350-5},
  abstract       = {Diffusion tensor imaging (DTI) and fiber tracking (FT)
                   were used to measure the occipital lobe fiber tracts
                   connecting the two hemispheres in individual human
                   subjects. These tracts are important for normal vision.
                   Also, damage to portions of these tracts is associated
                   with alexia. To assess the reliability of the DTI-FT
                   measurements, occipital-callosal projections were
                   estimated from each subject's left and right
                   hemispheres independently. The left and right estimates
                   converged onto the same positions within the splenium.
                   We further characterized the properties of the
                   estimated occipital-callosal fiber tracts by combining
                   them with functional MRI. We used functional MRI to
                   identify visual field maps in cortex and labeled fibers
                   by the cortical functional response at the fiber
                   endpoint. This labeling reveals a regular organization
                   of the fibers within the splenium. The dorsal visual
                   maps (dorsal V3, V3A, V3B, V7) send projections through
                   a large band in the middle of the splenium, whereas
                   ventral visual maps (ventral V3, V4) send projections
                   through the inferior-anterior corner of the splenium.
                   The agreement between the independent left/right
                   estimates, further supported by previous descriptions
                   of homologous tracts in macaque, validates the DTI-FT
                   methods. However, a principal limitation of these
                   methods is low sensitivity: a large number of fiber
                   tracts that connect homotopic regions of ventral and
                   lateral visual cortex were undetected. We conclude that
                   most of the estimated tracts are real and can be
                   localized with a precision of 1-2 mm, but many tracts
                   are missed because of data and algorithm limitations.},
  authoraddress  = {Stanford Institute for Reading and Learning,
                   Department of Psychology, Stanford University,
                   Stanford, CA 94305, USA. bobd@stanford.edu},
  keywords       = {Adult ; Algorithms ; *Brain Mapping ; Corpus
                   Callosum/*cytology ; Echo-Planar Imaging/methods ;
                   Female ; Humans ; Magnetic Resonance Imaging ; Male ;
                   Middle Aged ; Occipital Lobe/*cytology ; Visual
                   Fields/physiology},
  language       = {eng},
  medline-aid    = {0500003102 [pii] ; 10.1073/pnas.0500003102 [doi]},
  medline-crdt   = {2005/05/11 09:00},
  medline-da     = {20050518},
  medline-dcom   = {20050713},
  medline-dep    = {20050509},
  medline-edat   = {2005/05/11 09:00},
  medline-fau    = {Dougherty, Robert F ; Ben-Shachar, Michal ; Bammer,
                   Roland ; Brewer, Alyssa A ; Wandell, Brian A},
  medline-gr     = {EY-015000/EY/NEI NIH HHS/United States ;
                   EY-03164/EY/NEI NIH HHS/United States},
  medline-is     = {0027-8424 (Print)},
  medline-jid    = {7505876},
  medline-jt     = {Proceedings of the National Academy of Sciences of the
                   United States of America},
  medline-lr     = {20081120},
  medline-mhda   = {2005/07/14 09:00},
  medline-oid    = {NLM: PMC1129102},
  medline-own    = {NLM},
  medline-phst   = {2005/05/09 [aheadofprint]},
  medline-pl     = {United States},
  medline-pmc    = {PMC1129102},
  medline-pmid   = {15883384},
  medline-pst    = {ppublish},
  medline-pt     = {Comparative Study ; Journal Article ; Research
                   Support, N.I.H., Extramural ; Research Support,
                   Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.},
  medline-sb     = {IM},
  medline-so     = {Proc Natl Acad Sci U S A. 2005 May 17;102(20):7350-5.
                   Epub 2005 May 9.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=15883384},
  year           = 2005
}

@Article{ValentinaTomassini09192007,
  Author         = {Tomassini, Valentina and Jbabdi, Saad and Klein,
                   Johannes C. and Behrens, Timothy E. J. and Pozzilli,
                   Carlo and Matthews, Paul M. and Rushworth, Matthew F.
                   S. and Johansen-Berg, Heidi},
  Title          = {Diffusion-Weighted Imaging Tractography-Based
                   Parcellation of the Human Lateral Premotor Cortex
                   Identifies Dorsal and Ventral Subregions with
                   Anatomical and Functional Specializations},
  Journal        = {J. Neurosci.},
  Volume         = {27},
  Number         = {38},
  Pages          = {10259-10269},
  abstract       = {Lateral premotor cortex (PM) in the macaque monkey can
                   be segregated into structurally and functionally
                   distinct subregions, including a major division between
                   dorsal (PMd) and ventral (PMv) parts, which have
                   distinct cytoarchitecture, function, and patterns of
                   connectivity with both frontal and parietal cortical
                   areas. The borders of their subregions are less well
                   defined in the human brain. Here we use diffusion
                   tractography to identify a reproducible border between
                   dorsal and ventral subregions of human precentral
                   gyrus. We derive connectivity fingerprints for the two
                   subregions and demonstrate that each has a distinctive
                   pattern of connectivity with frontal cortex and lateral
                   parietal cortex, suggesting that these areas correspond
                   to human PMd and PMv. Although putative human PMd has a
                   high probability of connection with the superior
                   parietal lobule, dorsal prefrontal cortex, and
                   cingulate cortex, human PMv has a higher probability of
                   connection with the anterior inferior parietal lobule
                   and ventral prefrontal cortex. Finally, we assess the
                   correspondence between our PMd/PMv border and local
                   sulcal and functional anatomy. The location of the
                   border falls at the level of the gyral branch that
                   divides the inferior precentral sulcus from the
                   superior precentral sulcus and corresponded closely to
                   the location of a functional border defined using
                   previous functional magnetic resonance imaging studies.},
  doi            = {10.1523/JNEUROSCI.2144-07.2007},
  eprint         = {http://www.jneurosci.org/cgi/reprint/27/38/10259.pdf},
  file           = {attachment\:tomassini_parcellation_2007.pdf:attachment\:tomassini_parcellation_2007.pdf:PDF},
  url            = {http://www.jneurosci.org/cgi/content/abstract/27/38/10259},
  year           = 2007
}

@Article{Behrens2003MRM,
  Author         = {Behrens, T. E. J. and Woolrich, M. W. and Jenkinson,
                   M. and Johansen-Berg, H. and Nunes, R. G. and Clare, S.
                   and Matthews, P. M. and Brady, J. M. and Smith, S. M.},
  Title          = {Characterization and propagation of uncertainty in
                   diffusion-weighted \{{M}{R}\} imaging},
  Journal        = {Magnetic Resonance in Medicine},
  Volume         = {50},
  Pages          = {1077-1088},
  abstract       = {A fully probabilistic framework is presented for
                   estimating local probability density functions on
                   parameters of interest in a model of diffusion. This
                   technique is applied to the estimation of parameters in
                   the diffusion tensor model, and also to a simple
                   partial volume model of diffusion. In both cases the
                   parameters of interest include parameters defining
                   local fiber direction. A technique is then presented
                   for using these density functions to estimate global
                   connectivity (i.e., the probability of the existence of
                   a connection through the data field, between any two
                   distant points), allowing for the quantification of
                   belief in tractography results. This technique is then
                   applied to the estimation of the cortical connectivity
                   of the human thalamus. The resulting connectivity
                   distributions correspond well with predictions from
                   invasive tracer methods in nonhuman primate.},
  file           = {attachment\:Behrens2003MRM.pdf:attachment\:Behrens2003MRM.pdf:PDF},
  publisher      = {Wiley-Liss},
  year           = 2003
}

@Article{ODonnell_MICCAI06,
  Author         = {O'Donnell, L. and Westin, C. F.},
  Title          = {High-dimensional white matter atlas generation and
                   group analysis.},
  Journal        = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv},
  Volume         = {9},
  Number         = {Pt 2},
  Pages          = {243-51},
  abstract       = {We present a two-step process including white matter
                   atlas generation and automatic segmentation. Our atlas
                   generation method is based on population fiber
                   clustering. We produce an atlas which contains
                   high-dimensional descriptors of fiber bundles as well
                   as anatomical label information. We use the atlas to
                   automatically segment tractography in the white matter
                   of novel subjects and we present quantitative results
                   (FA measurements) in segmented white matter regions
                   from a small population. We demonstrate reproducibility
                   of these measurements across scans. In addition, we
                   introduce the idea of using clustering for automatic
                   matching of anatomical structures across hemispheres.},
  authoraddress  = {Computer Science and Artificial Intelligence
                   Laboratory, Massachusetts Institute of Technology,
                   Cambridge MA, USA. lauren@csail.mit.edu},
  keywords       = {Algorithms ; Anatomy, Artistic/methods ; *Artificial
                   Intelligence ; Brain/*anatomy \& histology ; Cluster
                   Analysis ; Computer Simulation ; Diffusion Magnetic
                   Resonance Imaging/*methods ; Humans ; Image
                   Enhancement/methods ; Image Interpretation,
                   Computer-Assisted/*methods ; Imaging,
                   Three-Dimensional/methods ; Medical Illustration ;
                   Models, Anatomic ; Nerve Fibers,
                   Myelinated/*ultrastructure ; Neural Pathways/*anatomy
                   \& histology ; Pattern Recognition, Automated/*methods
                   ; Reproducibility of Results ; Sensitivity and
                   Specificity},
  language       = {eng},
  medline-crdt   = {2007/03/16 09:00},
  medline-da     = {20070314},
  medline-dcom   = {20070406},
  medline-edat   = {2007/03/16 09:00},
  medline-fau    = {O'Donnell, Lauren ; Westin, Carl-Fredrik},
  medline-gr     = {P41 RR15241-01A1/RR/NCRR NIH HHS/United States ;
                   P41-RR13218/RR/NCRR NIH HHS/United States ; R01
                   AG20012-01/AG/NIA NIH HHS/United States ; R01 MH
                   50747/MH/NIMH NIH HHS/United States ;
                   U24-RR021382/RR/NCRR NIH HHS/United States ;
                   U54-EB005149/EB/NIBIB NIH HHS/United States},
  medline-jid    = {101249582},
  medline-jt     = {Medical image computing and computer-assisted
                   intervention : MICCAI ... International Conference on
                   Medical Image Computing and Computer-Assisted
                   Intervention},
  medline-lr     = {20071203},
  medline-mhda   = {2007/04/07 09:00},
  medline-own    = {NLM},
  medline-pl     = {Germany},
  medline-pmid   = {17354778},
  medline-pst    = {ppublish},
  medline-pt     = {Evaluation Studies ; Journal Article ; Research
                   Support, N.I.H., Extramural},
  medline-sb     = {IM},
  medline-so     = {Med Image Comput Comput Assist Interv Int Conf Med
                   Image Comput Comput Assist Interv. 2006;9(Pt 2):243-51.},
  medline-stat   = {MEDLINE},
  url            = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=17354778},
  year           = 2006
}

@Article{Correia2009,
  Author         = {Correia, Marta Morgado},
  Title          = {{Development of Methods for the Acquisition and
                   Analysis of Diffusion Weighted MRI Data}},
  Journal        = {Brain},
  Number         = {June},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Correia - 2009 - Development of
                   Methods for the Acquisition and Analysis of Diffusion
                   Weighted MRI Data.pdf:pdf},
  year           = 2009
}

@Article{jbabdi2007bfg,
  Author         = {Jbabdi, S. and Woolrich, MW and Andersson, JLR and
                   Behrens, TEJ},
  Title          = {{A Bayesian framework for global tractography}},
  Journal        = {Neuroimage},
  Volume         = {37},
  Number         = {1},
  Pages          = {116--129},
  publisher      = {Elsevier},
  year           = 2007
}

@Article{Jian2007aNeuroImage,
  Author         = {Jian, Bing and Vemuri, Baba C. and Ozarslan, Evren and
                   Carney, Paul R. and Mareci, Thomas H.},
  Title          = {A novel tensor distribution model for the
                   diffusion-weighted \{{M}{R}\} signal},
  Journal        = {NeuroImage},
  Volume         = {37},
  Number         = {1},
  Pages          = {164-176},
  abstract       = {Diffusion MRI is a non-invasive imaging technique that
                   allows the measurement of water molecule diffusion
                   through tissue in vivo. The directional features of
                   water diffusion allow one to infer the connectivity
                   patterns prevalent in tissue and possibly track changes
                   in this connectivity over time for various clinical
                   applications. In this paper, we present a novel
                   statistical model for diffusion-weighted MR signal
                   attenuation which postulates that the water molecule
                   diffusion can be characterized by a continuous mixture
                   of diffusion tensors. An interesting observation is
                   that this continuous mixture and the MR signal
                   attenuation are related through the Laplace transform
                   of a probability distribution over symmetric positive
                   definite matrices. We then show that when the mixing
                   distribution is a Wishart distribution, the resulting
                   closed form of the Laplace transform leads to a
                   Rigaut-type asymptotic fractal expression, which has
                   been phenomenologically used in the past to explain the
                   MR signal decay but never with a rigorous mathematical
                   justification until now. Our model not only includes
                   the traditional diffusion tensor model as a special
                   instance in the limiting case, but also can be adjusted
                   to describe complex tissue structure involving multiple
                   fiber populations. Using this new model in conjunction
                   with a spherical deconvolution approach, we present an
                   efficient scheme for estimating the water molecule
                   displacement probability functions on a voxel-by-voxel
                   basis. Experimental results on both simulations and
                   real data are presented to demonstrate the robustness
                   and accuracy of the proposed algorithms.},
  file           = {attachment\:Jian2007aNeuroImage.pdf:attachment\:Jian2007aNeuroImage.pdf:PDF},
  url            = {http://www.sciencedirect.com/science/article/B6WNP-4NMSRV9-3/2/b4bc62020864c9b5767ce1e87874128a},
  year           = 2007
}

@Article{Chen2006,
  Author         = {Chen, Bin and Guo, Hua and Song, Allen W},
  Title          = {{Correction for direction-dependent distortions in
                   diffusion tensor imaging using matched magnetic field
                   maps.}},
  Journal        = {NeuroImage},
  Volume         = {30},
  Number         = {1},
  Pages          = {121--9},
  abstract       = {Diffusion tensor imaging (DTI) has seen increased
                   usage in clinical and basic science research in the
                   past decade. By assessing the water diffusion
                   anisotropy within biological tissues, e.g. brain,
                   researchers can infer different fiber structures
                   important for neural pathways. A typical DTI data set
                   contains at least one base image and six
                   diffusion-weighted images along non-collinear encoding
                   directions. The resultant images can then be combined
                   to derive the three principal axes of the diffusion
                   tensor and their respective cross terms, which can in
                   turn be used to compute fractional anisotropy (FA)
                   maps, apparent diffusion coefficient (ADC) maps, and to
                   construct axonal fibers. The above operations all
                   assume that DTI images along different
                   diffusion-weighting directions for the same brain
                   register to each other without spatial distortions.
                   This assumption is generally false, as the large
                   diffusion-weighting gradients would usually induce eddy
                   currents to generate diffusion-weighting
                   direction-dependent field gradients, leading to
                   mis-registration within the DTI data set. Traditional
                   methods for correcting magnetic field-induced
                   distortions do not usually take into account these
                   direction-dependent eddy currents unique for DTI, and
                   they are usually time-consuming because multiple phase
                   images need to be acquired. In this report, we describe
                   our theory and implementation of an efficient and
                   effective method to correct for the main field and eddy
                   current-induced direction-dependent distortions for DTI
                   images under a unified framework to facilitate the
                   daily practice of DTI acquisitions.},
  doi            = {10.1016/j.neuroimage.2005.09.008},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Chen, Guo, Song - 2006 - Correction
                   for direction-dependent distortions in diffusion tensor
                   imaging using matched magnetic field maps..pdf:pdf},
  issn           = {1053-8119},
  keywords       = {Anisotropy,Artifacts,Brain,Brain Mapping,Brain:
                   anatomy \& histology,Diffusion Magnetic Resonance
                   Imaging,Diffusion Magnetic Resonance Imaging:
                   statistics \&,Echo-Planar Imaging,Echo-Planar Imaging:
                   statistics \& numerical data,Humans,Image
                   Enhancement,Image Enhancement: methods,Image
                   Processing, Computer-Assisted,Image Processing,
                   Computer-Assisted: statistics \& ,Mathematical
                   Computing,Nerve Fibers,Nerve Fibers:
                   ultrasonography,Neural Pathways,Neural Pathways:
                   anatomy \& histology,Phantoms, Imaging},
  month          = mar,
  pmid           = {16242966},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/16242966},
  year           = 2006
}

@Article{Corouge2006,
  Author         = {Corouge, Isabelle and Fletcher, P Thomas and Joshi,
                   Sarang and Gouttard, Sylvain and Gerig, Guido},
  Title          = {{Fiber tract-oriented statistics for quantitative
                   diffusion tensor MRI analysis.}},
  Journal        = {Medical image analysis},
  Volume         = {10},
  Number         = {5},
  Pages          = {786--98},
  abstract       = {Quantitative diffusion tensor imaging (DTI) has become
                   the major imaging modality to study properties of white
                   matter and the geometry of fiber tracts of the human
                   brain. Clinical studies mostly focus on regional
                   statistics of fractional anisotropy (FA) and mean
                   diffusivity (MD) derived from tensors. Existing
                   analysis techniques do not sufficiently take into
                   account that the measurements are tensors, and thus
                   require proper interpolation and statistics of tensors,
                   and that regions of interest are fiber tracts with
                   complex spatial geometry. We propose a new framework
                   for quantitative tract-oriented DTI analysis that
                   systematically includes tensor interpolation and
                   averaging, using nonlinear Riemannian symmetric space.
                   A new measure of tensor anisotropy, called geodesic
                   anisotropy (GA) is applied and compared with FA. As a
                   result, tracts of interest are represented by the
                   geometry of the medial spine attributed with tensor
                   statistics (average and variance) calculated within
                   cross-sections. Feasibility of our approach is
                   demonstrated on various fiber tracts of a single data
                   set. A validation study, based on six repeated scans of
                   the same subject, assesses the reproducibility of this
                   new DTI data analysis framework.},
  doi            = {10.1016/j.media.2006.07.003},
  file           = {:home/eg309/.local/share/data/Mendeley Ltd./Mendeley
                   Desktop/Downloaded/Corouge et al. - 2006 - Fiber
                   tract-oriented statistics for quantitative diffusion
                   tensor MRI analysis..pdf:pdf},
  issn           = {1361-8415},
  keywords       = {Algorithms,Artificial Intelligence,Brain,Brain:
                   cytology,Computer Simulation,Diffusion Magnetic
                   Resonance Imaging,Diffusion Magnetic Resonance Imaging:
                   methods,Feasibility Studies,Humans,Image
                   Enhancement,Image Enhancement: methods,Image
                   Interpretation, Computer-Assisted,Image Interpretation,
                   Computer-Assisted: methods,Imaging,
                   Three-Dimensional,Imaging, Three-Dimensional:
                   methods,Information Storage and Retrieval,Information
                   Storage and Retrieval: methods,Models,
                   Neurological,Models, Statistical,Neural Pathways,Neural
                   Pathways: cytology,Pattern Recognition,
                   Automated,Pattern Recognition, Automated:
                   methods,Reproducibility of Results,Sensitivity and
                   Specificity},
  pmid           = {16926104},
  url            = {http://www.ncbi.nlm.nih.gov/pubmed/16926104},
  year           = 2006
}