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Abstract #0067

Entropy Aided K-t Group Sparse SENSE Method for Highly Accelerated Dynamic MRI

Muhammad Usman1, Claudia Prieto1, Tobias Schaeffter1, Philip G. Batchelor1

1Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom

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).