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.. _references:

References
==========

.. [Christiaens2015] D. Christiaens, M. Reisert, T. Dhollander, S. Sunaert, P. Suetens, and F. Maes.
   *Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.*
   NeuroImage, 123 (2015), pp. 89–101.
   [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811915007168>`__\ ]

.. [CorderoGrande2019] L. Cordero-Grande, D. Christiaens, J. Hutter, A.N. Price, J.V. Hajnal
   *Complex diffusion-weighted image estimation via matrix recovery under general noise models.*
   NeuroImage 200 (2019), pp. 391-404
   [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811919305348>`__\ ]

.. [Dhollander2014] T. Dhollander, L. Emsell, W. Van Hecke, F. Maes, S. Sunaert, and P. Suetens.
   *Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography.*
   NeuroImage, 94 (2014), pp. 312–336.
   [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811913012676>`__\ ]

.. [Dhollander2016a] T. Dhollander, D. Raffelt, and A. Connelly.
   *A novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+b=0) diffusion MRI data.*
   Proceedings of the 24th annual meeting of the International Society of Magnetic Resonance in Medicine (2016), pp. 3010.
   [`full text link <https://www.researchgate.net/publication/301766619_A_novel_iterative_approach_to_reap_the_benefits_of_multi-tissue_CSD_from_just_single-shell_b0_diffusion_MRI_data>`__\ ]

.. [Dhollander2016b] T. Dhollander, D. Raffelt, and A. Connelly.
   *Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image.*
   ISMRM Workshop on Breaking the Barriers of Diffusion MRI (2016), pp. 5.
   [`full text link <https://www.researchgate.net/publication/307863133_Unsupervised_3-tissue_response_function_estimation_from_single-shell_or_multi-shell_diffusion_MR_data_without_a_co-registered_T1_image>`__\ ]

.. [Dhollander2017] T. Dhollander, D. Raffelt, and A. Connelly.
   *Towards interpretation of 3-tissue constrained spherical deconvolution results in pathology.*
   Proceedings of the 25th annual meeting of the International Society of Magnetic Resonance in Medicine (2017), pp. 1815.
   [`full text link <https://www.researchgate.net/publication/315836029_Towards_interpretation_of_3-tissue_constrained_spherical_deconvolution_results_in_pathology>`__\ ]

.. [Dhollander2018a] T. Dhollander, D. Raffelt, and A. Connelly.
   *Accuracy of response function estimation algorithms for 3-tissue spherical deconvolution of diverse quality diffusion MRI data.*
   Proceedings of the 26th annual meeting of the International Society of Magnetic Resonance in Medicine (2018), pp. 1569.
   [`full text link <https://www.researchgate.net/publication/324770874_Accuracy_of_response_function_estimation_algorithms_for_3-tissue_spherical_deconvolution_of_diverse_quality_diffusion_MRI_data>`__\ ]

.. [Dhollander2018b] T. Dhollander, J. Zanin, B.A. Nayagam, G. Rance, and A. Connelly.
   *Feasibility and benefits of 3-tissue constrained spherical deconvolution for studying the brains of babies.*
   Proceedings of the 26th annual meeting of the International Society of Magnetic Resonance in Medicine (2018), pp. 3077.
   [`full text link <https://www.researchgate.net/publication/324770875_Feasibility_and_benefits_of_3-tissue_constrained_spherical_deconvolution_for_studying_the_brains_of_babies>`__\ ]

.. [Dhollander2019] T. Dhollander, R. Mito, D. Raffelt, and A. Connelly.
   *Improved white matter response function estimation for 3-tissue constrained spherical deconvolution.*
   Proceedings of the 27th annual meeting of the International Society of Magnetic Resonance in Medicine (2019), pp. 555.
   [`full text link <https://www.researchgate.net/publication/331165168_Improved_white_matter_response_function_estimation_for_3-tissue_constrained_spherical_deconvolution>`__\ ]

.. [Jeurissen2014] B. Jeurissen, J.-D. Tournier, T. Dhollander, A. Connelly, and J.  Sijbers.
   *Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data.*
   NeuroImage, 103 (2014), pp. 411–426.
   [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811914006442>`__\ ]

.. [Mito2018a] R. Mito, D. Raffelt, T. Dhollander, D.N. Vaughan, J.-D. Tournier, O. Salvado, A. Brodtmann, C.C. Rowe, V.L. Villemagne, and A. Connelly.
   *Fibre-specific white matter reductions in Alzheimer's disease and mild cognitive impairment.*
   Brain, 141(3) (2018), pp. 888–902.
   [`full text link <http://dx.doi.org/10.1093/brain/awx355>`__\ ]

.. [Mito2018b] R. Mito, T. Dhollander, D. Raffelt, Y. Xia, O. Salvado, A. Brodtmann, C.C. Rowe, V.L. Villemagne, and A. Connelly.
   *Investigating microstructural heterogeneity of white matter hyperintensities in Alzheimer’s disease using single-shell 3-tissue constrained spherical deconvolution.*
   Proceedings of the 26th annual meeting of the International Society of Magnetic Resonance in Medicine (2018), pp. 135.
   [`full text link <https://www.researchgate.net/publication/324771728_Investigating_microstructural_heterogeneity_of_white_matter_hyperintensities_in_Alzheimer's_disease_using_single-shell_3-tissue_constrained_spherical_deconvolution>`__\ ]

.. [Raffelt2011] D. Raffelt, J.-D. Tournier, J. Fripp, S Crozier, A. Connelly, O. Salvado.
   *Symmetric diffeomorphic registration of fibre orientation distributions.*
   NeuroImage 56 (2011), pp. 1171–1180.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/21316463>`__\ ]

.. [Raffelt2012] D. Raffelt, J.-D. Tournier, S. Rose, G.R. Ridgway, R. Henderson, S. Crozier, O. Salvado, A. Connelly.
   *Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images.*
   NeuroImage 59 (2012), pp. 3976–3994.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/22036682>`__\ ]

.. [Raffelt2015] D.A. Raffelt, R.E. Smith, G.R. Ridgway, J.-D. Tournier, D.N. Vaughan, S. Rose, R. Henderson, A. Connelly.
   *Connectivity-Based Fixel Enhancement: Whole-Brain Statistical Analysis of Diffusion MRI Measures in the Presence of Crossing Fibres.*
   NeuroImage 117 (2015), pp. 40–55.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/26004503>`__\ ]

.. [Raffelt2017] D.A. Raffelt, J.-D. Tournier, R.E. Smith, D.N. Vaughan, G. Jackson, G.R. Ridgway, A. Connelly.
   *Investigating White Matter Fibre Density and Morphology using Fixel-Based Analysis.*
   NeuroImage, 144 (2017), pp. 58-73.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/27639350>`__\ ]

.. [Reisert2011] M. Reisert, I. Mader, C. Anastasopoulos, M. Weigel, S. Schnell, and V. Kiselev.
   *Global fiber reconstruction becomes practical.*
   NeuroImage, 54 (2011) pp. 955–962.
   [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811910011973>`__\ ]

.. [Smith2012] R.E. Smith, J.-D. Tournier, F. Calamante, A. Connelly.
   *Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information.*
   NeuroImage 62 (2012), pp. 1924–1938.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/22705374>`__\ ]

.. [Smith2013] R.E. Smith, J.-D. Tournier, F. Calamante, A. Connelly.
   *SIFT: Spherical-deconvolution informed filtering of tractograms.*
   NeuroImage 67 (2013), pp. 298–312.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/23238430>`__\ ]

.. [Smith2015] R.E. Smith, J.-D. Tournier, F. Calamante, A. Connelly.
   *SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography.*
   NeuroImage 119 (2015), pp. 338-51.
   [`full text link <https://www.ncbi.nlm.nih.gov/pubmed/26163802>`__\ ]

.. [Smith2019a] R.E. Smith, D. Dimond, S. Bray, A. Connelly.
   *Mitigation of DWI brain cropping in Fixel-Based Analysis.*
   In Proc OHBM (2019), W765
   [`full text link <https://www.researchgate.net/publication/332495497_Mitigation_of_DWI_brain_cropping_in_Fixel-Based_Analysis>`__\ ]

.. [Tax2014] C.M.W. Tax, B. Jeurissen, S.B.Vos, M.A. Viergever, and A. Leemans.
   *Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.*
   NeuroImage, 86 (2014), pp. 67–80.
   [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811913008367>`__\ ]

.. [Tournier2004] J.-D. Tournier, F. Calamante, D.G. Gadian, and A. Connelly.
   *Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.*
   NeuroImage, 23 (2004), pp. 1176–85.
   [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811904004100>`__\ ]

.. [Tournier2007] J.-D. Tournier, F. Calamante, and A. Connelly.
   *Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.*
   Neuroimage, 35 (2007), pp. 1459–72.
   [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811907001243>`__\ ]

.. [Tournier2012] J.-D. Tournier, F. Calamante, A. Connelly.
   *MRtrix: Diffusion tractography in crossing fiber regions.*
   INT J IMAG SYST TECH, 22 (2012), pp. 53-66.
   [`full text link <http://onlinelibrary.wiley.com/doi/10.1002/ima.22005/abstract>`__\ ]

.. [Tournier2013] J.-D. Tournier, F. Calamante, and A. Connelly.
   *Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging.*
   NMR Biomed., 26 (2013), pp. 1775–86.
   [`full text link <https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.3017>`__\ ]
   
.. [Tournier2019] J.-D. Tournier, R. E. Smith, D. Raffelt, R. Tabbara, T. Dhollander, M. Pietsch, D. Christiaens, B. Jeurissen, C.-H. Yeh, and A. Connelly.
   *MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.*
   NeuroImage, 202 (2019), pp. 116–37.
   [`fulltext link <https://www.sciencedirect.com/science/article/pii/S1053811919307281>`__\ ]

.. [Veraart2016a] J. Veraart, E. Fieremans, and D.S. Novikov.
   *Diffusion MRI noise mapping using random matrix theory.*
   Magn. Res. Med. 76(5) (2016), pp. 1582–1593.
   [`full text link <https://doi.org/10.1002/mrm.26059>`__\ ]

.. [Veraart2016b] J. Veraart, D.S. Novikov, D. Christiaens, B. Ades-aron, J. Sijbers, and E. Fieremans
   *Denoising of diffusion MRI using random matrix theory.*
   NeuroImage 142 (2016), pp. 394–406.
   [`full text link <http://dx.doi.org/10.1016/j.neuroimage.2016.08.016>`__\ ]