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Abstract #3413

Momentum optimization for iterative shrinkage algorithms in parallel MRI with sparsity-promoting regularization

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

MRI scan times can be accelerated by combining parallel MRI with sparse models. These models give rise to optimization problems that are traditionally minimized with variable splitting algorithms that require tuning of penalty parameters. We review a new algorithm, BARISTA, that circumvents penalty parameter tuning while preserving convergence speed. We then propose a new optimized momentum update term for BARISTA that gives a theoretically-predicted factor of 2 increase in convergence speed of the cost function, terming the new algorithm OMBARISTA. Our optimization experiments agreed with the theory predictions, and we propose using OMBARISTA in place of BARISTA in general settings.

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