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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