Learning space-time dictionaries for blind compressed sensing dynamic MRI reconstruction

Abstract

This work addresses the problem of recovering dynamic MR sequences from their undersampled projections using the Blind Compressed Sensing (BCS) framework. In BCS, reconstructing the sparse signal and estimating the sparsifying basis proceeds simultaneously. Best results in CS based dynamic MRI reconstruction have been achieved when both spatial correlation and temporal redundancies were exploited. Prior studies in BCS dynamic MRI reconstruction either accounted for spatial redundancy or temporal correlation, but not both. In this work, we propose to jointly exploit spatio-temporal correlation within the BCS framework. The improvement in reconstruction results is significant compared to prior BCS techniques in dynamic MRI.