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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.