  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 to
dangerous tissue heating/damage in the form of local SAR hotspots that cannot
currently be measured/monitored, thereby severely limiting the applicability of
the technology for clinical practice and in regulatory approval. The goal of
this 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 local
SAR-monitoring at sub-W/kg levels. We trained the software with a small
database of image as a feasibility study and achieved successful proof of
concept for both field strengths. SAR patterns were predicted with a residual
root mean squared error (RSME) of <11% along with a structural similarity
(SSIM) level of >84% for both field strengths (3T and 7T).
