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generate_figures.m
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generate_figures.m
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% Generates the figures for the Augmented Space off-resonance paper
%
% Pablo Irarrazaval (June 2023)
%% Preamble
global VERBOSE
VERBOSE = true; % Print messages
addpath('Simulation code'); addpath(genpath('Recon code'));
fig_dir = '..\pdfFigures\'; fig_ext = '.pdf';
% What to do
DO_Sim1d = 0; % 1D simulations
DO_Sim2d = 1; % 2D simulations
DO_Phantom = 0; % Phantom
LoadPhantom = 1; % load from file because it is already computed
DO_Invivo = 0; % In-vivo
LoadInvivo = 1; % load from file because it is already computed
SAVE_figure = false;
%% 1D simulation
if DO_Sim1d
fig_base = 'sim1D_';
fcnt = 0; f = []; figname = [];
% Defines reconstruction experiment in data structure
data = [];
data.Nd = 128; % Image dimensions (Nx)
data.Tmax = 20e-3; % ACQ time in seconds
data.seq = '2DFT'; % Defines trajectory
data.pfun_name = 'Half plane'; % Defines field map
% data.pfun_name = 'Zero'; % Defines field map
% Defines simulation parameters in simdata structure
simdata = [];
simdata.Nd = data.Nd; % The dimensions again
simdata.type = 'Sum'; % Type of simulation
simdata.objfun_name = 'Centered square'; % Object to simulate
% Generates simulated data, creates m_hat and updates data and simdata
Simulate;
% pw = tukeywin(data.Nd,0.5); % Apodization filter (becaue of the low resolution)
% m_hat = m_hat.*pw;
% Shows experiment data
%-----------------------
% common parameters for plots
linewidth = 1.5;
fcnt = fcnt+1; f{fcnt} = figure(100); figname{fcnt} = 'object';
plot(simdata.x,real(simdata.m),simdata.x,imag(simdata.m),'LineWidth',linewidth);
% title('Object 1D');
legend('real','imag');legend('boxoff');
opt = []; opt.xlabel = '$x$'; opt.ylabel = '$m(x)$';
opt.yticks = {'1'}; opt.xticks = {'-16' '16'};
pretty_plot(opt)
fcnt = fcnt+1; f{fcnt} = figure(101); figname{fcnt} = 'fieldmap';
plot(simdata.x,data.p,'LineWidth',linewidth);
axis([-data.Nd/2 data.Nd/2 -20 20])
% title('Field map [Hz]');
opt = []; opt.xlabel = '$x$'; opt.ylabel = '$p(x)$';
opt.yticks = {'-15'};
pretty_plot(opt);
fcnt = fcnt+1; f{fcnt} = figure(102); figname{fcnt} = 'timemap';
plot(data.kx,data.t*1000,'LineWidth',linewidth);
axis([-0.5 0.5 0 22])
% title('Time map [ms]')
opt = []; opt.xlabel = '$k_x$'; opt.ylabel = '$t(k_x)$';
opt.yticks = {'20'};
pretty_plot(opt);
fcnt = fcnt+1; f{fcnt} = figure(103); figname{fcnt} = 'signal';
plot(data.kx,real(m_hat),data.kx,imag(m_hat),'LineWidth',linewidth);
axis([-0.5 0.5 -1 3])
% title('Simulated signal');
legend('real','imag');legend('boxoff');
opt = []; opt.xlabel = '$k_x$'; opt.ylabel = '$\hat{m}(k_x)$';
opt.yticks = {'2.5'}; opt.xticks = {'-0.5' '0.5'};
pretty_plot(opt);
% Reconstruct with different algorithms
%---------------------------------------
% DFT ****
m_dft = DFTrecon(m_hat,data);
fcnt = fcnt+1; f{fcnt} = figure(110); figname{fcnt} = 'dft';
plot(simdata.x,real(m_dft),simdata.x,imag(m_dft),'LineWidth',linewidth)
% title('DFT recon')
legend('real','imag');legend('boxoff');
opt = []; opt.xlabel = '$x$'; opt.ylabel = '$m(x)$';
opt.yticks = {'1'}; opt.xticks = {'-16' '16'};
pretty_plot(opt)
% FSEG ****
data.df = 1; % Frequency step in Hz
m_fseg = FSEGrecon(m_hat,data,'none'); % No need to interpolate because the
% field map is already segmented
fcnt = fcnt+1; f{fcnt} = figure(120); figname{fcnt} = 'fseg';
plot(simdata.x,real(m_fseg),simdata.x,imag(m_fseg),'LineWidth',linewidth)
% title('FSEG recon')
legend('real','imag');legend('boxoff');
opt = []; opt.xlabel = '$x$'; opt.ylabel = '$m(x)$';
opt.yticks = {'1'}; opt.xticks = {'-16' '16'};
pretty_plot(opt)
% CAS ****
data.df = 1; % Sampling period for frequency [Hz]
data.dt = 0.1e-3; % Sampling period for time [s]
data.w = 1; % For Uniform sampling no need to window data
for iter = 1:5 % Test different number of iterations
data.maxiter = iter;
m_casn = CAS_ls(m_hat,data,[],data.maxiter);
fcnt = fcnt+1; f{fcnt} = figure(130+iter); figname{fcnt} = sprintf('cas%d',iter);
plot(simdata.x,real(m_casn),simdata.x,imag(m_casn),'LineWidth',linewidth)
title(sprintf('CAS recon (%d iters)',data.maxiter))
legend('real','imag');legend('boxoff');
opt = []; opt.xlabel = '$x$'; opt.ylabel = '$m(x)$';
opt.yticks = {'1'}; opt.xticks = {'-16' '16'};
pretty_plot(opt)
end
% DAS ****
data.Nf = 512; % Dimension of discrete augmented space
data.df = 3; % Sampling period for frequency [Hz]
data.dt = 1/data.df/data.Nf; % Sampling period for time [s]
data.w = 1; % For Uniform sampling no need to window data
data.maxiter = 5;
m_das = DAS_ls(m_hat,data,[],data.maxiter);
fcnt = fcnt+1; f{fcnt} = figure(140); figname{fcnt} = 'das';
plot(simdata.x,real(m_das),simdata.x,imag(m_das),'LineWidth',linewidth)
title(sprintf('DAS recon (%d iters)',data.maxiter))
legend('real','imag');legend('boxoff');
opt = []; opt.xlabel = '$x$'; opt.ylabel = '$m(x)$';
opt.yticks = {'1'}; opt.xticks = {'-16' '16'};
pretty_plot(opt)
if SAVE_figure
for nn = 1:length(f)
exportgraphics(f{nn},[fig_dir fig_base figname{nn} fig_ext]);
end
end
end
%% 2D simulation
if DO_Sim2d
fig_base = 'sim2D_';
fcnt = 0; f = []; figname = []; % store fig handles and names
times = []; timnames = []; % store execution times and names
option = 2; % 1: Resolution, Bipolar gauss, EPI 8, T=20ms
% 2: Tubes, Bipolar gauss, EPI 8, T=30ms
% 3: Tubes, Gauss, Spiral 4, T=30ms
% 4: Tubes, Step field map, Spiral 4, T=30ms
% Defines reconstruction experiment in data structure
data = [];
data.Nd = [128 128]; % Image dimensions (Nx)
data.shift = data.Nd/2; % Position of origin
switch option
case 1
data.Tmax = 20e-3; % ACQ time in seconds
data.seq = 'EPI 8'; % Defines trajectory
Nshots = 8;
data.pfun_name = 'Bipolar gauss'; % Defines field map
simdata.objfun_name = 'Resolution'; % Object to simulate
case 2
data.Tmax = 30e-3; % ACQ time in seconds
data.seq = 'EPI 8'; % Defines trajectory
Nshots = 8;
data.pfun_name = 'Bipolar gauss'; % Defines field map
simdata.objfun_name = 'Tubes'; % Object to simulate
PrecomputedMFIweights = 'Recon code\MFIPrecomputedWeights_sim2D';
case 3
data.Tmax = 30e-3; % ACQ time in seconds
data.seq = 'Spiral 4'; % Defines trajectory
Nshots = 4;
data.pfun_name = 'Gauss'; % Defines field map
simdata.objfun_name = 'Tubes'; % Object to simulate
case 4
data.Tmax = 30e-3; % ACQ time in seconds
data.seq = 'Spiral 4'; % Defines trajectory
Nshots = 4;
data.pfun_name = 'Half plane'; % Defines field map
simdata.objfun_name = 'Tubes'; % Object to simulate
PrecomputedMFIweights = 'Recon code\MFIPrecomputedWeights_sim2Da';
end
% Defines simulation parameters in simdata structure
simdata.Nd = data.Nd; % The dimensions again
simdata.type = 'Sum'; % Type of simulation
% Generates simulated data, creates m_hat and updates data and simdata
Simulate;
Nsamples = data.L/Nshots; % number of samples per shot
if data.Uniform
pw = 1; mask = 1; % no pre-windows, no mask
else
% Low pass filters raw data before recon
pw = tukeywin(2*Nsamples,0.75); pw = repmat(pw(Nsamples+1:end),[1 Nshots]);
% circle mask to limit FOV to a circle
[xx,yy] = meshgrid(-data.Nd(1)/2:data.Nd(1)/2-1,-data.Nd(2)/2:data.Nd(2)/2-1);
mask = (xx.^2+yy.^2)<=(data.Nd(1)/2)^2;
end
% Shows experiment data
%-----------------------
% common parameters for plots
lims_obj = [0 1.1]; % same scale for all plots of objects
lims_err = [0 0.3]; % same scale for all plots of errors
fcnt = fcnt+1; f{fcnt} = figure(200); figname{fcnt} = 'object';
imshow(abs(simdata.m),lims_obj,'Init',300); set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(201); figname{fcnt} = 'fieldmap';
imshow(data.p,[],'Init',300); set(gca,'YDir','Normal');
% need to plot differently non-uniform time and raw data
if data.Uniform
fcnt = fcnt+1; f{fcnt} = figure(202); figname{fcnt} = 'timemap';
imshow(data.t*1000,[],'Init',300);set(gca,'YDir','Normal');
% Warning: check if signal appears transposed
fcnt = fcnt+1; f{fcnt} = figure(203); figname{fcnt} = 'signal';
imshow(abs(m_hat),[],'Init',300);set(gca,'YDir','Normal');
else
fcnt = fcnt+1; f{fcnt} = figure(202); figname{fcnt} = 'timemap';
plot3(data.kx,data.ky,data.t);
fcnt = fcnt+1; f{fcnt} = figure(203); figname{fcnt} = 'signal';
plot3(data.kx,data.ky,abs(m_hat),'.');
end
% Reconstruct with different algorithms
%---------------------------------------
% DFT *****************************************************************
ii = length(times); tic;
m_dft = mask.*DFTrecon(m_hat.*pw(:),data);
times(ii+1) = toc; timnames{ii+1} = 'DFT';
RMSE_dft = norm(m_dft-simdata.m);
fcnt = fcnt+1; f{fcnt} = figure(210); figname{fcnt} = 'dft';
imshow(abs(m_dft),lims_obj,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(211); figname{fcnt} = 'dfterror';
imshow(abs(m_dft-simdata.m),lims_err,'Init',300);set(gca,'YDir','Normal');
% FSEG ****************************************************************
data.df = 3; % Frequency step in Hz
ii = length(times); tic;
m_fseg = mask.*FSEGrecon(m_hat.*pw(:),data,'none');
times(ii+1) = toc; timnames{ii+1} = 'FSEG';
RMSE_fseg = norm(m_fseg-simdata.m);
ii = length(times); tic;
m_fseg_linear = mask.*FSEGrecon(m_hat.*pw(:),data,'linear');
times(ii+1) = toc; timnames{ii+1} = 'FSEG_linear';
RMSE_fseg_linear = norm(m_fseg_linear-simdata.m);
ii = length(times); tic;
m_fseg_mfi = mask.*FSEGrecon(m_hat.*pw(:),data,'MFI',PrecomputedMFIweights);
times(ii+1) = toc; timnames{ii+1} = 'FSEG_mfi';
RMSE_fseg_mfi = norm(m_fseg_mfi-simdata.m);
fcnt = fcnt+1; f{fcnt} = figure(220); figname{fcnt} = 'fseg';
imshow(abs(m_fseg),lims_obj,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(221); figname{fcnt} = 'fsegerror';
imshow(abs(m_fseg-simdata.m),lims_err,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(222); figname{fcnt} = 'fseglinear';
imshow(abs(m_fseg_linear),lims_obj,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(223); figname{fcnt} = 'fseglinearerror';
imshow(abs(m_fseg_linear-simdata.m),lims_err,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(224); figname{fcnt} = 'fsegmfi';
imshow(abs(m_fseg_mfi),lims_obj,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(225); figname{fcnt} = 'fsegmfierror';
imshow(abs(m_fseg_mfi-simdata.m),lims_err,'Init',300);set(gca,'YDir','Normal');
% CAS *****************************************************************
data.df = 3; % Sampling period for frequency [Hz]
data.dt = 0.1e-3; % Sampling period for time [s]
data.w = 1; % For Uniform sampling no need to window data
data.maxiter = 7;
ii = length(times); tic;
m_cas = mask.*CAS_ls(m_hat.*pw(:),data,[],data.maxiter);
times(ii+1) = toc; timnames{ii+1} = 'CAS';
RMSE_cas = norm(m_cas-simdata.m);
fcnt = fcnt+1; f{fcnt} = figure(230); figname{fcnt} = 'cas';
imshow(abs(m_cas),lims_obj,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(231); figname{fcnt} = 'caserror';
imshow(abs(m_cas-simdata.m),lims_err,'Init',300);set(gca,'YDir','Normal');
% DAS *****************************************************************
data.Nf = 512; % Dimension of discrete augmented space
data.df = 3; % Sampling period for frequency [Hz]
data.dt = 1/data.df/data.Nf; % Sampling period for time [s]
data.w = 1; % For Uniform sampling no need to window data
data.maxiter = 8;
ii = length(times); tic;
m_das = mask.*DAS_ls(m_hat.*pw(:),data,[],data.maxiter);
times(ii+1) = toc; timnames{ii+1} = 'DAS';
RMSE_das = norm(m_das-simdata.m);
fcnt = fcnt+1; f{fcnt} = figure(240); figname{fcnt} = 'das';
imshow(abs(m_das),lims_obj,'Init',300);set(gca,'YDir','Normal');
fcnt = fcnt+1; f{fcnt} = figure(241); figname{fcnt} = 'daserror';
imshow(abs(m_das-simdata.m),lims_err,'Init',300);set(gca,'YDir','Normal');
% Print errors
fprintf('RMSE DFT = %f FSEG = %f DAS = %f CAS = %f\n',...
RMSE_dft,RMSE_fseg,RMSE_das,RMSE_cas);
% Print times
fprintf('Execution times\n')
for nn = 1:length(times)
fprintf('%12s: %5.2f ms\n',timnames{nn},times(nn)*1000);
end
fprintf('Execution times\n')
for nn = 1:length(times)
fprintf('%12s: %5.2f s\n',timnames{nn},times(nn));
end
if SAVE_figure
for nn = 1:length(f)
exportgraphics(f{nn},[fig_dir fig_base figname{nn} fig_ext]);
end
end
end
%% Phantom acquisition
if DO_Phantom
fig_base = 'phantom_';
fcnt = 0; f = []; figname = []; % store fig handles and names
times = []; timnames = []; % store execution times and names
% Speeds things up reconstructing only one coil
COIL = 0; % 0 for all coils
FIGURE_TYPE = 'abs'; % 'abs' or 'complex'
% Defines reconstruction experiment in data structure
data = [];
% Reads the data. It contains:
% - M % the raw data
% - Nd % object size
% - Ncoils, Nsamples, Nshots % other size variables
% - Shift % to center reconstruction
% - csm % coil sensitivity maps
% - dcf % density compensation function
% - fm % field map
% - tm % time map
% - k_spx, k_spy % k-space trajectory
load('Data\phantom_data.mat');
% Fills in strcture from loaded variables
data.Nd = Nd; % Dimensions (reverse order: z,y,x)
data.shift = Shift; % Moves origin
data.Nc = Ncoils; % Number of coils
data.Nsamples = Nsamples; % Number of samples per shot
data.Nshots = Nshots; % Number of shots
data.Uniform = false; % Uniform or nonuniform sampling
data.kx = k_spx(:); % kx
data.ky = k_spy(:); % ky
data.dcf = dcf(:); % k-space density compensation (ony non-uniform)
data.L = length(data.kx); % Number of samples for non-uniform
data.t = tm(:); % Time map
data.p = fm; % Field map
data.FTst = prepares_nufft(data); % Prepares NUFFT
if LoadPhantom % already computed and saved to this file
load('phantom_figs.mat');
else
USEpar = true; % Enables parellel for's in recons
if USEpar && (COIL==0)
poolobj = gcp; % Get the current parallel pool and creates it if not available
NumWorkers = poolobj.NumWorkers;
else, NumWorkers = 0; end
% Low pass filters raw data before recon
pw = tukeywin(2*Nsamples,0.75); pw = repmat(pw(Nsamples+1:end),[1 Nshots]);
% circle mask to limit FOC to a circle
[xx,yy] = meshgrid(-Nd(1)/2:Nd(1)/2-1,-Nd(2)/2:Nd(2)/2-1);
mask = (xx.^2+yy.^2)<=(Nd(1)/2)^2;
% Function to combine coils using CSM
if COIL==0 % All coils
csm_sq = max(0.5,sum(abs(csm).^2,numel(data.Nd)+1)); % 0.5 avoids div by zero
csmof = @(x) (sum(conj(csm).*x,numel(data.Nd)+1)./csm_sq);
else % One coil
csm_sq = (max(0.5,abs(csm(:,:,COIL)).^2)); % 0.5 avoids div by zero
csmof = @(x) ((conj(csm(:,:,COIL)).*x))./csm_sq;
end
end
% Shows experiment data
%-----------------------
% common parameters for plots
fcnt = fcnt+1; f{fcnt} = figure(301); figname{fcnt} = 'fieldmap';
imshow(data.p,[],'Init',300); set(gca,'YDir','Normal');
% title('Field map [Hz]');
fcnt = fcnt+1; f{fcnt} = figure(302); figname{fcnt} = 'timemap';
plot3(data.kx,data.ky,data.t);
% title('Time map [ms]');
% Reconstruct with different algorithms
%---------------------------------------
% DFT *****************************************************************
if ~LoadPhantom
if COIL>0
s = M(:,:,COIL).*pw;
m_dft = mask.*nufft_adj(s(:).*data.dcf,data.FTst).'/sqrt(prod(data.Nd));
else
m_dft = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
s = M(:,:,n).*pw;
m_dft(:,:,n) = mask.*nufft_adj(s(:).*data.dcf,data.FTst).'/sqrt(prod(data.Nd));
end
times(ii+1) = toc; timnames{ii+1} = 'DFT';
end
% Combine coils
m_dft_csm = csmof(m_dft);
end
fcnt = fcnt+1; f{fcnt} = figure(310); figname{fcnt} = 'dft';
titname = 'DFT recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_dft_csm),[],'Init',300);
set(gca,'YDir','Normal'); % title(titname);
case 'complex'
mkimshowsubplot('complex',m_dft_csm,'common',titname);
end
% FSEG NONE ***********************************************************
if ~LoadPhantom
data.df = 10; % Frequency step in Hz
FSEG_INTERP = 'none';
if COIL>0
s = M(:,:,COIL).*pw;
m_seg = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
else
fprintf('FSEG\n');
m_seg = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_seg(:,:,n) = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
end
times(ii+1) = toc; timnames{ii+1} = 'FSEG';
fprintf('\n');
end
% Combine coils
m_seg_csm = csmof(m_seg);
end
fcnt = fcnt+1; f{fcnt} = figure(320); figname{fcnt} = 'fseg';
titname = 'FSEG recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_seg_csm),[],'Init',300);
set(gca,'YDir','Normal'); % title(titname);
case 'complex'
mkimshowsubplot('complex',m_seg_csm,'common',titname);
end
% FSEG LINEAR ***********************************************************
if ~LoadPhantom
data.df = 10; % Frequency step in Hz
FSEG_INTERP = 'linear';
if COIL>0
s = M(:,:,COIL).*pw;
m_seg_linear = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
else
fprintf('FSEG linear\n');
m_seg_linear = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_seg_linear(:,:,n) = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
end
times(ii+1) = toc; timnames{ii+1} = 'FSEG_linear';
fprintf('\n');
end
% Combine coils
m_seg_linear_csm = csmof(m_seg_linear);
end
fcnt = fcnt+1; f{fcnt} = figure(321); figname{fcnt} = 'fseglinear';
titname = 'FSEG linear recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_seg_linear_csm),[],'Init',300);
set(gca,'YDir','Normal'); % title(titname);
case 'complex'
mkimshowsubplot('complex',m_seg_linear_csm,'common',titname);
end
% FSEG MFI ***********************************************************
if ~LoadPhantom
data.df = 10; % Frequency step in Hz
FSEG_INTERP = 'MFI';
if COIL>0
s = M(:,:,COIL).*pw;
m_seg_mfi = mask.*FSEGrecon(s(:),data,FSEG_INTERP,...
'Recon code\MFIPrecomputedWeights_phantom');
else
fprintf('FSEG mfi\n');
m_seg_mfi = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_seg_mfi(:,:,n) = mask.*FSEGrecon(s(:),data,FSEG_INTERP,...
'Recon code\MFIPrecomputedWeights_phantom');
end
times(ii+1) = toc; timnames{ii+1} = 'FSEG_mfi';
fprintf('\n');
end
% Combine coils
m_seg_mfi_csm = csmof(m_seg_mfi);
end
fcnt = fcnt+1; f{fcnt} = figure(322); figname{fcnt} = 'fsegmfi';
titname = 'FSEG MFI recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_seg_mfi_csm),[],'Init',300);
set(gca,'YDir','Normal'); % title(titname);
case 'complex'
mkimshowsubplot('complex',m_seg_mfi_csm,'common',titname);
end
% CAS *****************************************************************
if ~LoadPhantom
data.df = 10; % Sampling period for frequency [Hz]
data.dt = 0.3e-3; % Sampling period for time [s]
data.maxiter = 10; % Iterations
% First iteration uses density as weights
data.w = sqrt(data.dcf);
if COIL>0
s = M(:,:,COIL).*pw;
m_cas = mask.*CAS_ls(s(:),data,[],1); % First iteration
data.w = 1; % For rest of iterations
s = M(:,:,COIL).*pw;
x0 = m_cas;
m_cas = mask.*CAS_ls(s(:),data,[],data.maxiter-1,[],[],x0(:));
else
fprintf('CAS\n');
m_cas = zeros([data.Nd data.Nc]);
% First iteration
fprintf('First iteration with weight of sqrt(dcf):\n')
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_cas(:,:,n) = mask.*CAS_ls(s(:),data,[],1);
end
fprintf('\n');
% Rest of iterations
data.w = 1;
fprintf('Rest of iterations with weight of one:\n')
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
x0 = m_cas(:,:,n);
m_cas(:,:,n) = mask.*CAS_ls(s(:),data,[],data.maxiter-1,[],[],x0(:));
end
times(ii+1) = toc; timnames{ii+1} = 'CAS';
fprintf('\n');
end
% Combine coils
m_cas_csm = csmof(m_cas);
end
fcnt = fcnt+1; f{fcnt} = figure(330); figname{fcnt} = 'cas';
titname = 'CAS recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_cas_csm),[],'Init',300);
set(gca,'YDir','Normal'); %title(titname);
case 'complex'
mkimshowsubplot('complex',m_cas_csm,'common',titname);
end
% DAS *****************************************************************
if ~LoadPhantom
data.Nf = 256; % Dimension of discrete augmented space
data.df = 10; % Sampling period for frequency [Hz]
data.dt = 1/data.df/data.Nf; % Sampling period for time [s]
data.maxiter = 8;
% First iteration uses density as weights
data.w = sqrt(data.dcf);
if COIL>0
s = M(:,:,COIL).*pw;
m_das = mask.*DAS_ls(s(:),data,[],1); % First iteration
data.w = 1; % For rest of iterations
s = M(:,:,COIL).*pw;
x0 = m_das;
m_das = mask.*DAS_ls(s(:),data,[],data.maxiter-1,[],[],x0(:));
else
fprintf('DAS\n');
m_das = zeros([data.Nd data.Nc]);
% First iteration
fprintf('First iteration with weight of sqrt(dcf):\n')
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_das(:,:,n) = mask.*DAS_ls(s(:),data,[],1);
end
fprintf('\n');
% Rest of iterations
data.w = 1;
fprintf('Rest of iterations with weight of one:\n')
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
x0 = m_das(:,:,n);
m_das(:,:,n) = mask.*DAS_ls(s(:),data,[],data.maxiter-1,[],[],x0(:));
end
times(ii+1) = toc; timnames{ii+1} = 'DAS';
fprintf('\n');
end
% Combine coils
m_das_csm = csmof(m_das);
end
fcnt = fcnt+1; f{fcnt} = figure(340); figname{fcnt} = 'das';
titname = 'DAS recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_das_csm),[],'Init',300);
set(gca,'YDir','Normal'); % title(titname);
case 'complex'
mkimshowsubplot('complex',m_das_csm,'common',titname);
end
%------------------------
load('Data\phantom_reference.mat');
fcnt = fcnt+1; f{fcnt} = figure(350); figname{fcnt} = 'ref';
titname = 'Reference';
switch FIGURE_TYPE
case 'abs'
imshow(abs(phantom_reference),[],'Init',300);
set(gca,'YDir','Normal'); % title(titname);
case 'complex'
mkimshowsubplot('complex',phantom_reference,'common',titname);
end
% if ~LoadPhantom
% save('phantom_figs.mat','m_*');
% end
% Print times
fprintf('Execution times\n')
for nn = 1:length(times)
fprintf('%12s: %5.2f ms\n',timnames{nn},times(nn)*1000);
end
fprintf('Execution times\n')
for nn = 1:length(times)
fprintf('%12s: %5.2f s\n',timnames{nn},times(nn));
end
if SAVE_figure
for nn = 1:length(f)
exportgraphics(f{nn},[fig_dir fig_base figname{nn} fig_ext]);
end
end
end
%% In-vivo acquisition
if DO_Invivo
FIGURE_TYPE = 'abszoom'; % 'abs', 'abszoom' or 'complex'
fig_base = 'invivozoom_'; % change for different types of figures
fcnt = 0; f = []; figname = []; % store fig handles and names
times = []; timnames = []; % store execution times and names
% Speeds things up reconstructing only one coil
COIL = 0; % 0 for all coils
% Defines reconstruction experiment in data structure
data = [];
% Reads the data. It contains:
% - M % the raw data
% - Nd % object size
% - Ncoils, Nsamples, Nshots % other size variables
% - Shift % to center reconstruction
% - csm % coil sensitivity maps
% - dcf % density compensation function
% - fm % field map
% - tm % time map
% - k_spx, k_spy % k-space trajectory
load('Data\invivo_data.mat');
% Fills in strcture from loaded variables
data.Nd = Nd; % Dimensions (reverse order: z,y,x)
data.shift = Shift; % Moves origin
data.Nc = Ncoils; % Number of coils
data.Nsamples = Nsamples; % Number of samples per shot
data.Nshots = Nshots; % Number of shots
data.Uniform = false; % Uniform or nonuniform sampling
data.kx = k_spx(:); % kx
data.ky = k_spy(:); % ky
data.dcf = dcf(:); % k-space density compensation (ony non-uniform)
data.L = length(data.kx); % Number of samples for non-uniform
data.t = tm(:); % Time map
data.p = fm; % Field map
data.FTst = prepares_nufft(data); % Prepares NUFFT
if LoadInvivo % already computed and saved to this file
load('invivo_figs.mat');
else
USEpar = true; % Enables parellel for's in recons
if USEpar && (COIL==0)
poolobj = gcp; % Get the current parallel pool and creates it if not available
NumWorkers = poolobj.NumWorkers;
else, NumWorkers = 0; end
% Low pass filters raw data before recon
pw = tukeywin(2*Nsamples,0.75); pw = repmat(pw(Nsamples+1:end),[1 Nshots]);
% circle mask to limit FOC to a circle
[xx,yy] = meshgrid(-Nd(1)/2:Nd(1)/2-1,-Nd(2)/2:Nd(2)/2-1);
mask = (xx.^2+yy.^2)<=(Nd(1)/2)^2;
% Function to combine coils using CSM
if COIL==0 % All coils
csm_sq = max(0.5,sum(abs(csm).^2,numel(data.Nd)+1)); % 0.5 avoids div by zero
csmof = @(x) (sum(conj(csm).*x,numel(data.Nd)+1)./csm_sq);
else % One coil
csm_sq = (max(0.5,abs(csm(:,:,COIL)).^2)); % 0.5 avoids div by zero
csmof = @(x) ((conj(csm(:,:,COIL)).*x))./csm_sq;
end
end
% Shows experiment data
%-----------------------
% common parameters for plots
fcnt = fcnt+1; f{fcnt} = figure(401); figname{fcnt} = 'fieldmap';
imshow(data.p,[],'Init',300); set(gca,'YDir','Normal');
% title('Field map [Hz]');
fcnt = fcnt+1; f{fcnt} = figure(402); figname{fcnt} = 'timemap';
plot3(data.kx,data.ky,data.t);
% title('Time map [ms]');
% Reconstruct with different algorithms
%---------------------------------------
% DFT *****************************************************************
if ~LoadInvivo
if COIL>0
s = M(:,:,COIL).*pw;
m_dft = mask.*nufft_adj(s(:).*data.dcf,data.FTst).'/sqrt(prod(data.Nd));
else
m_dft = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
s = M(:,:,n).*pw;
m_dft(:,:,n) = mask.*nufft_adj(s(:).*data.dcf,data.FTst).'/sqrt(prod(data.Nd));
end
times(ii+1) = toc; timnames{ii+1} = 'DFT';
end
% Combine coils
m_dft_csm = csmof(m_dft);
end
fcnt = fcnt+1; f{fcnt} = figure(410); figname{fcnt} = 'dft';
titname = 'DFT recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_dft_csm),[],'Init',300);
set(gca,'YDir','Normal'); %title(titname);
case 'abszoom'
imshowzoomed(abs(m_dft_csm),[42 59 30 30]);
case 'complex'
mkimshowsubplot('complex',m_dft_csm,'common',titname);
end
% FSEG NONE ****************************************************************
if ~LoadInvivo
data.df = 10; % Frequency step in Hz
FSEG_INTERP = 'none';
if COIL>0
s = M(:,:,COIL).*pw;
m_seg = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
else
fprintf('FSEG\n');
m_seg = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_seg(:,:,n) = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
end
times(ii+1) = toc; timnames{ii+1} = 'FSEG';
fprintf('\n');
end
% Combine coils
m_seg_csm = csmof(m_seg);
end
fcnt = fcnt+1; f{fcnt} = figure(420); figname{fcnt} = 'fseg';
titname = 'FSEG recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_seg_csm),[],'Init',300);
set(gca,'YDir','Normal'); %title(titname);
case 'abszoom'
imshowzoomed(abs(m_seg_csm),[42 59 30 30]);
case 'complex'
mkimshowsubplot('complex',m_seg_csm,'common',titname);
end
% FSEG LINEAR ****************************************************************
if ~LoadInvivo
data.df = 10; % Frequency step in Hz
FSEG_INTERP = 'linear';
if COIL>0
s = M(:,:,COIL).*pw;
m_seg_linear = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
else
fprintf('FSEG\n');
m_seg_linear = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_seg_linear(:,:,n) = mask.*FSEGrecon(s(:),data,FSEG_INTERP);
end
times(ii+1) = toc; timnames{ii+1} = 'FSEG_linear';
fprintf('\n');
end
% Combine coils
m_seg_linear_csm = csmof(m_seg_linear);
end
fcnt = fcnt+1; f{fcnt} = figure(421); figname{fcnt} = 'fseglinear';
titname = 'FSEG linear recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_seg_linear_csm),[],'Init',300);
set(gca,'YDir','Normal'); %title(titname);
case 'abszoom'
imshowzoomed(abs(m_seg_linear_csm),[42 59 30 30]);
case 'complex'
mkimshowsubplot('complex',m_seg_linear_csm,'common',titname);
end
% FSEG MFI ****************************************************************
if ~LoadInvivo
data.df = 10; % Frequency step in Hz
FSEG_INTERP = 'MFI';
if COIL>0
s = M(:,:,COIL).*pw;
m_seg_mfi = mask.*FSEGrecon(s(:),data,FSEG_INTERP,...
'Recon code\MFIPrecomputedWeights_invivo');
else
fprintf('FSEG\n');
m_seg_mfi = zeros([data.Nd data.Nc]);
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_seg_mfi(:,:,n) = mask.*FSEGrecon(s(:),data,FSEG_INTERP,...
'Recon code\MFIPrecomputedWeights_invivo');
end
times(ii+1) = toc; timnames{ii+1} = 'FSEG_mfi';
fprintf('\n');
end
% Combine coils
m_seg_mfi_csm = csmof(m_seg_mfi);
end
fcnt = fcnt+1; f{fcnt} = figure(422); figname{fcnt} = 'fsegmfi';
titname = 'FSEG MFI recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_seg_mfi_csm),[],'Init',300);
set(gca,'YDir','Normal'); %title(titname);
case 'abszoom'
imshowzoomed(abs(m_seg_mfi_csm),[42 59 30 30]);
case 'complex'
mkimshowsubplot('complex',m_seg_mfi_csm,'common',titname);
end
% CAS *****************************************************************
if ~LoadInvivo
data.df = 10; % Sampling period for frequency [Hz]
data.dt = 0.3e-3; % Sampling period for time [s]
data.maxiter = 8; % Iterations
% First iteration uses density as weights
data.w = sqrt(data.dcf);
if COIL>0
s = M(:,:,COIL).*pw;
m_cas = mask.*CAS_ls(s(:),data,[],1); % First iteration
data.w = 1; % For rest of iterations
s = M(:,:,COIL).*pw;
x0 = m_cas;
m_cas = mask.*CAS_ls(s(:),data,[],data.maxiter-1,[],[],x0(:));
else
fprintf('CAS\n');
m_cas = zeros([data.Nd data.Nc]);
% First iteration
fprintf('First iteration with weight of sqrt(dcf):\n')
ii = length(times); tic;
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
m_cas(:,:,n) = mask.*CAS_ls(s(:),data,[],1);
end
fprintf('\n');
% Rest of iterations
data.w = 1;
fprintf('Rest of iterations with weight of one:\n')
parfor (n = 1:data.Nc, NumWorkers) % Recon coil by coil
fprintf('%d: ',n);
s = M(:,:,n).*pw;
x0 = m_cas(:,:,n);
m_cas(:,:,n) = mask.*CAS_ls(s(:),data,[],data.maxiter-1,[],[],x0(:));
end
times(ii+1) = toc; timnames{ii+1} = 'CAS';
fprintf('\n');
end
% Combine coils
m_cas_csm = csmof(m_cas);
end
fcnt = fcnt+1; f{fcnt} = figure(430); figname{fcnt} = 'cas';
titname = 'CAS recon';
switch FIGURE_TYPE
case 'abs'
imshow(abs(m_cas_csm),[],'Init',300);
set(gca,'YDir','Normal'); %title(titname);
case 'abszoom'
imshowzoomed(abs(m_cas_csm),[42 59 30 30]);
case 'complex'
mkimshowsubplot('complex',m_cas_csm,'common',titname);
end
% DAS *****************************************************************
if ~LoadInvivo
data.Nf = 256; % Dimension of discrete augmented space
data.df = 10; % Sampling period for frequency [Hz]
data.dt = 1/data.df/data.Nf; % Sampling period for time [s]
data.maxiter = 8;