Simone Angela Winkler1, Elizaveta Motovilova1, Sayim Gökyar1, Isabelle Saniour1, Fraser Robb2, and Akshay Chaudhari3
1Department of Radiology, Weill Cornell Medicine, New York, NY, United States, 2GE Healthcare, Aurora, OH, United States, 3Stanford University, Stanford, CA, United States
1Department of Radiology, Weill Cornell Medicine, New York, NY, United States, 2GE Healthcare, Aurora, OH, United States, 3Stanford University, Stanford, CA, United States
This work is a proof-of-concept demonstration of an artificial
intelligence (AI) based real-time MRI safety prediction software (MRSaiFE). We show local SAR prediction with a root-mean-square
error (RMSE) of <11% along with a structural similarity (SSIM) level of >84%.
Table 1: SAR prediction results of 3T and 7T images
Table 2: Training performance
