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Abstract #3575

MRSaiFE: towards the real-time prediction of SAR in 3T and 7T MR RF coils - a feasibility study with 10 body models

Sayim Gokyar1, Isabelle Saniour1, Fraser Robb2, Arthur Nghiem3, Wolfgang Kainz4, Akshay S. Chaudhari5, and Simone Winkler1
1Radiology, Weill Cornell Medicine, New York, NY, United States, 2General Electric Healthcare, Aurora, OH, United States, 3Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States, 4Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States, 5Radiology, Stanford University, Stanford, CA, United States


Significant RF power deposition in the body causing local specific absorption rate (SAR) in the form of hotspots is an important safety concern at 3T (128 MHz) and, even more so, at 7T (298 MHz). In this work, we expand the proof-of-concept of artificial intelligence based real-time MRI safety prediction software (MRSaiFE) to 10 body models. We show that SAR patterns can be predicted with a mean squared error (MSE) of less than 1% and a structural similarity index of above 90% for 7T brain and above 85% for 3T body MRI.

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