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Abstract #4852

Non-Convex Greedy Compressed Sensing for Phase Contrast MRI

Daehyun Yoon1, Jeffrey Fessler1, Jon-Fredrik Nielsen2, Anna Gilbert3, Douglas Noll2

1Electrical Engineering, University of Michigan, Ann Arbor, MI, United States; 2Biomedical Engineering, University of Michigan; 3Mathematics, University of Michigan

We propose a novel, non-convex greedy compressed sensing algorithm for phase-contrast MRI. Because the blood vessel distributions are sparse in the image domain, we model that the velocity encoded image has only sparse phase changes compared to the reference image without velocity encoding. Exploiting this sparsity in the velocity encoding phase, we developed a non-convex greedy compressed sensing algorithm to highly undersample the acquisition of the velocity encoded object. We also compared our proposed method to a convex optimization method and found out from the simulations that our method can achieve higher undersampling rates.