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A to-date unsolved and highly limiting safety concern for Ultra High-Field(UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF)power in the body, quantified by the specific absorption rate (SAR), leading todangerous tissue heating/damage in the form of local SAR hotspots that cannotcurrently be measured/monitored, thereby severely limiting the applicability ofthe technology for clinical practice and in regulatory approval. The goal ofthis study has been to show proof of concept of an artificial intelligence (AI)based exam-integrated real-time MRI safety prediction software (MRSaiFE)facilitating the safe generation of 3T and 7T images by means of accurate localSAR-monitoring at sub-W/kg levels. We trained the software with a smalldatabase of image as a feasibility study and achieved successful proof ofconcept for both field strengths. SAR patterns were predicted with a residualroot mean squared error (RSME) of <11% along with a structural similarity(SSIM) level of >84% for both field strengths (3T and 7T).

MRSaiFE: Tissue Heating Prediction for MRI: a Feasibility Study