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Abstract #4400

Accelerating SENSE-type MR image reconstruction algorithms with incremental gradients

Matthew J. Muckley 1,2 , Douglas C. Noll 1,2 , and Jeffrey A. Fessler 1,2

1 Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 2 Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

Algorithms that minimize SENSE-type image reconstruction cost functions almost always compute the gradient of a data consistency term at each iteration of the algorithm. Incremental gradient methods approximate the full gradient of the data consistency term by computing the gradient using a subset of the data. Since these subset gradients require less computation time, using them as a proxy for the full gradient significantly accelerates convergence. The method is general enough to be applied to any MR image reconstruction problem involving multiple receive coils with a SENSE model. Four-fold acceleration is shown with a low rank plus sparse model.

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