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Abstract #0545

k-T Group Sparse Reconstruction Method for Dynamic Compressed MRI

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

1King's College London, London, United Kingdom

Up to now, besides sparsity, the standard compressed sensing methods used in MR do not exploit any other prior information about the underlying signal. In general, the MR data in its sparse representation always exhibits some structure. As an example, for dynamic cardiac MR data, the signal support in its sparse representation (x-f space) is always in compact form. In this work, exploiting the structural properties of sparse representation, we propose a new formulation titled k-t group sparse compressed sensing. This formulation introduces a constraint that forces a group structure in sparse representation of the reconstructed signal. The k-t group sparse reconstruction achieves much higher temporal and spatial resolution than the standard L1 method at high acceleration factors (9-fold acceleration).