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Abstract #4807

U-Net Segmentation for Human Body Models for SAR Simulations

Isabelle Heukensfeldt Jansen1, Matthew Tarasek1, Johan Reimann1, and Desmond Teck Beng Yeo1

1General Electric, Niskayuna, NY, United States

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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