Fast reconstruction for accelerated multi-slice multi-contrast MRI

Clinical magnetic resonance imaging (MRI) protocols typically include multiple acquisitions of the same region of interest under different contrast settings. This paper presents an efficient algorithm to jointly reconstruct a set of undersampled images with different contrasts. The proposed method has faster reconstruction time and better quality as measured by the normalized root-mean-square error (RMSE) compared to the existing methods consisting of multi-contrast Fast Composite Splitting Algorithm (FCSA-MT), Multiple measurement vectors FOCal Underdetermined System Solver (M-FOCUSS), and total variation regularized compressed sensing (SparseMRI). To efficiently solve the £2, 1-regularized optimization problem, our proposed algorithm adopts the Split Bregman (SB) technique to divide the problem into sub-problems. We efficiently compute a closed-form solution to each of the sub-problems by implementing a 3D spatial gradient operator as element-wise multiplication in k-space. As demonstrated by the in vivo results, the proposed algorithm (SB-L21) offers 2x, 32x, and 66x faster reconstruction with lower RMSE averaged across all contrasts and slices compared to FCSA-MT, M-FOCUSS, and SparseMRI, respectively.