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Abstract #0516

A Maximum Likelihood Approach to Simultaneous Multislice Magnetic Resonance Fingerprinting

Bo Zhao1,2, Berkin Bilgic1,2, Jason Stockmann1,2, Lawrence L. Wald1,2, and Kawin Setsompop1,2

1Martinos Center for Biomedical Imaging, Chalestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States

Magnetic resonance fingerprinting is an efficient quantitative MRI paradigm, which simultaneously acquires multiple MR tissue parameters. Recently, simultaneous multislice (SMS) acquisition has been used to further speed up MRF experiments. In this abstract, we present a maximum likelihood formulation to enable improved SMS-MRF reconstruction. We further describe an algorithm based on variable splitting, the alternating direction method of multipliers, and the variable projection method to solve the resulting nonlinear and nonconvex optimization problem. Representative results are shown to demonstrate that the proposed method enables more accurate MR tissue parameter maps compared to the recent SMS-MRF approach utilizing direct pattern matching.

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