1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
|
% 2015, Martin Uecker <uecker@eecs.berkeley.edu>
%
% Example script to use BART for the initial preprocessing
% (gridding) which is required - but not included - in the
% original Matlab RT-NLINV example. The example is for a
% single frame, but this should also work in a similar way
% for the RT-NLINV2 code which reconstructs a time-series
% of images from highly undersampled data using temporal
% regularization.
%
% Links to the Matlab code can be found here:
% http://www.eecs.berkeley.edu/~uecker/toolbox.html
%
% References:
%
% Uecker M et al., Nonlinear Inverse Reconstruction for Real-time MRI
% of the Human Heart Using Undersampled Radial FLASH,
% MRM 63:1456-1462 (2010)
%
% Uecker M et al., Real-time magnetic resonance imaging at 20 ms
% resolution, NMR in Biomedicine 23: 986-994 (2010)
%
% data set is included in the IRGNTV example
A = load('radial_cardiac_25_projections.mat');
% re-format trajectory for BART
t = zeros(3, 256, 25);
t(1,:,:) = real(A.k) * 384.;
t(2,:,:) = imag(A.k) * 384.;
% use adjoint nufft to interpolate data onto Cartesia grid
adj = bart('nufft -d384:384:1 -a ', t, reshape(A.rawdata, [1 256 25 12]));
% compute point-spread function
psf = bart('nufft -d384:384:1 -a ', t, ones(1, 256, 25));
% transform back to k-space
adjk = bart('fft -u 7', adj);
psfk = bart('fft -u 7', psf);
% use nlinv from RT-NLINV (nlinv2) matlab package
R = nlinv(squeeze(adjk), squeeze(psfk) * 1., 9, 'noncart');
|
