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Abstract #1162

MGRAPPA: Motion Corrected GRAPPA for MRI

Michael Rawson1, Xiaoke Wang2, Ze Wang2, Radu Balan1,3, and Thomas Ernst2
1Department of Mathematics, University of Maryland at College Park, College Park, MD, United States, 2Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, 3Center for Scientific Computation and Mathematical Modeling, University of Maryland at College Park, College Park, MD, United States

We introduce an approximation and resulting method called MGRAPPA to allow high speed MRI scans robust to subject motion using prospective motion correction and GRAPPA [1,2]. In experiments on both simulated data and in vivo data, we observe high accuracy and robustness to subject movement in L2 (Frobenius) norm error including a 41% improvement in the in vivo experiment.

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