Distributed Compressed Sensing for Accelerated MRI

Ricardo Otazo¹, Daniel K. Sodickson¹

A framework for combining parallel imaging with compressed sensing is presented using the theory of distributed compressed sensing which extends compressed sensing to multiple sensors using the principle of joint sparsity. We present a greedy reconstruction algorithm named JOMP (Joint Orthogonal Matching Pursuit) that uses intra- and inter-coil correlations to jointly sparsify the multi-coil image instead of sparsifying the individual images. We show that for a sufficient number of coils, the number of measurements required by JOMP-PMRI to reconstruct a truly sparse image is very close to the image sparsity level. The performance of JOMP-PMRI with compressible images is assessed with a simulated brain image to show feasibility of higher accelerations with increasing number of coils.