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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