Abstract #1162

MGRAPPA: Motion Corrected GRAPPA for MRI

Michael Rawson¹, Xiaoke Wang², Ze Wang², Radu Balan^1,3, 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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