A generalized motion compensated compressed sensing scheme for highly accelerated myocardial perfusion MRI

Background Compressed sensing (CS) based myocardial perfusion MRI methods that promote sparsity in temporal transform domains such as temporal Fourier (x-f), temporal PCA (x-PCA), temporal total variation (x-TV) have shown promise to accelerate breath held scans [Otazo et al, 10, Pedersen et al, 09, Adluru et al, 07]. However the performances of these schemes can degrade in the presence of motion if the sparse representations in these transforms are significantly disturbed. In this work, we propose to address this challenge by jointly estimating and compensating for the motion during the CS reconstruction (MC-CS). The proposed scheme employs a variable splitting based optimization strategy [Lingala et al 2011] to enable joint motion estimation along with reconstruction. Unlike existing MC-CS methods, the novelties enabled by this optimization are a generalized formulation capable of handling any temporal sparsifying transform, no requirement of fully sampled prescans or navigators for motion estimation. We compare the performance of the MC-CS method with three different sparsifying transforms on free breathing myocardial perfusion data.