RF power absorption during MRI, expressed in terms of specific absorption rate (SAR), is an important safety issue, especially in multi-channel transmit MRI. To reduce uncertainties of local SAR estimates due to subject antatomical variations, patient-specific human body models can be applied in EM simulations of the RF transmit coil. In this work, we trained a U-net neural network on simulated CT scans to quickly create HBMs with four primary tissue classes (bone, lungs, fat, and water-based). Local SAR results using HBMs created with the U-net showed good agreement with those from ground truth models.
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