Abstract #1354

Motion correction in MRI with large movements using deep learning and a novel hybrid loss function

Lei Zhang¹, Xiaoke Wang¹, Michael Rawson², Radu Balan³, Edward H. Herskovits¹, Linda Chang¹, Ze Wang¹, and Thomas Ernst¹

¹Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, ²Department of Mathematics, University of Maryland, College Park, MD, United States, ³Department of Mathematics and Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, MD, United States

Patient motion continues to be a major problem in MRI. We propose and validate a novel deep learning approach for the correction of large movements in brain MRI. Training pairs were generated using in-house MRI data of high quality, and simulated images with artifacts based on real head movements. The images predicted by the proposed DL method from motion-corrupted data have improved image quality compared with the original corrupted images in terms of a quantitative metric and visual assessment by experienced readers.

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