# ESPIRiT Reconstruction with L1-Wavelet Demo

This is a demo on how to generate ESPIRiT maps and use them to perform ESPIRiT reconstruction for parallel imaging. It is based on the paper Uecker et. al, MRM 2013 DOI 10.1002/mrm.24751. ESPIRiT is a method that finds the subspace of multi-coil data from a calibration region in k-space using a series of eigen-value decompositions in k-space and image space. Here we also use the "soft" sense idea (Uecker et. al, "ESPIRiT Reconstruction using Soft-SENSE", Proceedings of the ISMRM 2013, pp-127) by using the eigen values to weight the eigen-vectors.

## Contents

## Prepare DATA

Here we perform ESPIRiT calibration on data which has strong aliasing in the phase-encode direction. SENSE often fails with this type of data.

load brain_8ch DATA = DATA/max(max(max(abs(ifft2c(DATA))))) + eps; ksize = [6,6]; % ESPIRiT kernel-window-size eigThresh_k = 0.02; % threshold of eigenvectors in k-space eigThresh_im = 0.9; % threshold of eigenvectors in image space % parameters for L1-reconstruction with splitting nIterCG = 5; % number of CG iterations for the PI part nIterSplit = 15; % number of splitting iterations for CS part splitWeight = 0.4; % reasonable value lambda = 0.0025; % L1-Wavelet threshold [sx,sy,Nc] = size(DATA); % create a sampling mask to simulate x2 undersampling with autocalibration % lines mask = mask_randm_x4; mask = repmat(mask,[1,1,8]); ncalib = getCalibSize(mask_randm_x4); DATAc = DATA.*mask; calib = crop(DATAc,[ncalib,Nc]);

Display coil images:

im = ifft2c(DATAc); figure, imshow3(abs(im),[],[1,Nc]); title('magnitude of physical coil images'); colormap((gray(256))); colorbar; figure, imshow3(angle(im),[],[1,Nc]); title('phase of physical coil images'); colormap('default'); colorbar;

## Compute Eigen-Value Maps

Maps are computed in two steps.

% compute Calibration matrix, perform 1st SVD and convert singular vectors % into k-space kernels [k,S] = dat2Kernel(calib,ksize); idx = max(find(S >= S(1)*eigThresh_k));

Display the singular vectors and values of the calibration matrix

kdisp = reshape(k,[ksize(1)*ksize(2)*Nc,ksize(1)*ksize(2)*Nc]); figure, subplot(211), plot([1:ksize(1)*ksize(2)*Nc],S,'LineWidth',2); hold on, plot([1:ksize(1)*ksize(2)*Nc],S(1)*eigThresh_k,'r-','LineWidth',2); plot([idx,idx],[0,S(1)],'g--','LineWidth',2) legend('signular vector value','threshold') title('Singular Vectors') subplot(212), imagesc(abs(kdisp)), colormap(gray(256)); xlabel('Singular value #'); title('Singular vectors')

crop kernels and compute eigen-value decomposition in image space to get maps

[M,W] = kernelEig(k(:,:,:,1:idx),[sx,sy]);

show eigen-values and eigen-vectors. The last set of eigen-vectors corresponding to eigen-values 1 look like sensitivity maps

figure, imshow3(abs(W),[],[1,Nc]); title('Eigen Values in Image space'); colormap((gray(256))); colorbar; figure, imshow3(abs(M),[],[Nc,Nc]); title('Magnitude of Eigen Vectors'); colormap(gray(256)); colorbar; figure, imshow3(angle(M),[],[Nc,Nc]); title('Magnitude of Eigen Vectors'); colormap(jet(256)); colorbar;

Warning: Image is too big to fit on screen; displaying at 67% Warning: Image is too big to fit on screen; displaying at 67%

## Compute Soft-SENSE ESPIRiT Maps

crop sensitivity maps according to eigenvalues==1. Note that we have to use 2 sets of maps. Here we weight the 2 maps with the eigen-values

maps = M(:,:,:,end-1:end); % Weight the eigenvectors with soft-senses eigen-values weights = W(:,:,end-1:end) ; weights = (weights - eigThresh_im)./(1-eigThresh_im).* (W(:,:,end-1:end) > eigThresh_im); weights = -cos(pi*weights)/2 + 1/2; % create and ESPIRiT operator ESP = ESPIRiT(maps,weights);

## Reconsturctions

ESPIRiT CG reconstruction with soft-sense and 1 sets of maps

XOP = Wavelet('Daubechies_TI',4,6); FT = p2DFT(mask,[sx,sy,Nc]); disp('Performing ESPIRiT reconstruction from 2 maps') tic; [reskESPIRiT, resESPIRiT] = cgESPIRiT(DATAc,ESP, nIterCG*3, 0.01,DATAc*0); toc disp('Performing L1-ESPIRiT reconstruction from 2 maps') tic [resL1ESPIRiT] = cgL1ESPIRiT(DATAc, resESPIRiT*0, FT, ESP, nIterCG,XOP,lambda,splitWeight,nIterSplit); toc % GRAPPA reconstruction disp('Performing GRAPPA reconstruction ... slow in Matlab! ') tic; reskGRAPPA = GRAPPA(DATAc,calib,[5,5],0.01);toc resGRAPPA = ifft2c(reskGRAPPA);

Performing ESPIRiT reconstruction from 2 maps Elapsed time is 3.460917 seconds. Performing L1-ESPIRiT reconstruction from 2 maps Iteration: 1, consistency: 18.537199 Iteration: 2, consistency: 6.907292 Iteration: 3, consistency: 4.212248 Iteration: 4, consistency: 3.600387 Iteration: 5, consistency: 3.386763 Iteration: 6, consistency: 3.279046 Iteration: 7, consistency: 3.215016 Iteration: 8, consistency: 3.173626 Iteration: 9, consistency: 3.145386 Iteration: 10, consistency: 3.125352 Iteration: 11, consistency: 3.110704 Iteration: 12, consistency: 3.099735 Iteration: 13, consistency: 3.091360 Iteration: 14, consistency: 3.084862 Iteration: 15, consistency: 3.079751 Elapsed time is 22.371217 seconds. Performing GRAPPA reconstruction ... slow in Matlab! reconstructing coil 1 reconstructing coil 2 reconstructing coil 3 reconstructing coil 4 reconstructing coil 5 reconstructing coil 6 reconstructing coil 7 reconstructing coil 8 Elapsed time is 225.601446 seconds.

Note the typical center FOV aliasing in SENSE. Also, note that ESPIRiT has (very slightly) less error than GRAPPA

figure, imshow(cat(2,sos(resESPIRiT), sos(resL1ESPIRiT),sos(resGRAPPA)),[0,1]); title('ESPIRiT reconstruction vs L1-ESPIRiT vs GRAPPA') figure, imshow(cat(2,sos(ifft2c(reskESPIRiT-DATA)),sos(ifft2c(fft2c(ESP*resL1ESPIRiT)-DATA)),sos(ifft2c(reskGRAPPA-DATA))).^(1/2),[]) title('ESPIRiT reconstruction error vs L1-ESPIRiT vs GRAPPA')