Muhammad Usman1, Claudia Prieto1, Tobias Schaeffter1, Philip G. Batchelor1
1Division of Imaging
Sciences & Biomedical Engineering, King's College
Over the last few years, the combination of Compressed sensing (CS) and parallel imaging have been of great interest to accelerate MRI. For dynamic MRI, K-t sparse SENSE (K-t SS) has been proposed for combining the CS based K-t Sparse method with SENSE. Recently, K-t group sparse method (K-t GS) has been shown to outperform K-t Sparse for single coil reconstruction, by exploiting the sparsity and the structure within the sparse representation (x-f space) of dynamic MRI. In this work, we propose to extend K-t GS to parallel imaging acquisition in order to achieve higher acceleration factors by exploiting the spatial sensitive encoding from multiple coils. This approach has been called K-t group Sparse SENSE (K-t GSS). In contrast with the previous single-coil based K-t GS method for which a performance parameter is manually optimized for every frequency encode; we propose an entropy based scheme for automatic selection of this parameter. Results from retrospectively undersampled cardiac gated data show that K-t GSS outperformed K-t sparse SENSE at high acceleration factors (up to 16 fold).