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Abstract #2486

MRSaiFE: towards the real-time prediction of tissue heating in MRI - a feasibility study

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

A crucial safety concern for UHF MRI is the significant RF power deposition in the body in the form of local specific absorption rate (SAR) hotspots, leading to dangerous tissue heating/damage. This work is a proof-of-concept demonstration of an artificial intelligence (AI) based real-time MRI safety prediction software (MRSaiFE) facilitating safe generation of 3T and 7T images by means of accurate local SAR-monitoring at sub-W/kg levels. This feasibility study demonstrates that SAR patterns can be predicted with a root-mean-square error (RMSE) of <11% along with a structural similarity (SSIM) level of >84% for both field strengths.

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