Motion-Adaptive Spatio-Temporal Regularization (MASTeR) for Accelerated Dynamic MRI

Accelerated MRI techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated MRI is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this paper, a new recovery algorithm, motion-adaptive spatio-temporal regularization (MASTeR), is presented. MASTeR, which uses compressed sensing principles to recover dynamic MR images from highly undersampled kspace data, takes advantage of spatial and temporal structured sparsity in MR images. In contrast to existing algorithms, MASTeR models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of MASTeR is demonstrated with experiments on cardiac MRI for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation—another recently proposed recovery algorithm for dynamic MRI.