Feasibility of predicting MRI tissue heating (MRSaiFE) using experimental training data

Motivation: MRI poses safety risks due to tissue damage via SAR hotspots. We have previously developed MRSaiFE, an AI-based SAR prediction tool. Goal(s): This study expands MRSaiFE with experimental, in vivo, training data. Approach: Images from a subject are segmented into a numerical model that is simulated to obtain SAR. The MRSaiFE input is the scanned image, and the predicted output SAR is obtained from training on the simulated SAR. Results: Good agreement (0.4% MSE, 6% RMSE, and 81% SSIM) demonstrates feasibility of using 1) experimental training data and 2) scanned input images, enabling future prediction from in vivo localizers. Impact: By replacing conservative SAR margins with patient-specific values, MRSaiFE offers potential for enhanced sensitivity, resolution, or reduced scan time. Additionally, it could notably enhance safety in patients with medical implants, hyperthermia treatments, and in MRI procedures at ultra-high fields.