Meeting Banner
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here