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Abstract #0779

Physics-Constrained Neural Network for Synthesis of MR Parameter Maps and Clinical Contrast

Gawon Lee1, Ji Wan Son1, Ken SaKaie2, Kunio Nakamura3, Yufan Zheng3, Daniel Ontaneda4, Bruce Trapp5, Mark Lowe2, Dong Hye Ye6, and Se-hong Oh7
1Division of Biomedical engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Korea, Republic of, 2Imaging institute, Cleveland Clinic, Cleveland, OH, United States, 3Biomedical Engineering, Lerner Research Institue, Cleveland Clinic, Cleveland, OH, United States, 4Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, OH, United States, 5Department of Neurosciences, Cleveland Clinic, Cleveland, OH, United States, 6Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, United States, 7Biomedical engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Korea, Republic of

Synopsis

When a clinical MR scan is acquired, there might be missing tissue contrasts due to the corruption by patient’s motion during long scan time. In this study, we propose a method to synthesize the missing T2-weighted or FLAIR contrasts from a T1-weighted image using physics-constrained neural network. We incorporate the Bloch equations that generate MR contrast images from tissue parameter maps based on MR physical models into a synthesis neural network and show the improved performance compared with the existing U-Net directly synthesizing from a T1-weighted image.

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