Abstract #0411

Numerical Body Model Inference for Personalized RF Exposure Prediction in Neuroimaging at 7T

Wyger Brink¹, Sahar Yousefi^1,2, Prernna Bhatnagar¹, Marius Staring², Rob Remis³, and Andrew Webb¹

¹C.J. Gorter Center, dept. of Radiology, Leiden University Medical Center, Leiden, Netherlands, ²Division of Image Processing, dept. of Radiology, Leiden University Medical Center, Leiden, Netherlands, ³Circuits and Systems, dept. of Microelectronics, Delft University of Technology, Delft, Netherlands

Compliance with RF exposure limits in ultra-high field MRI is typically based on “one-size-fits-all” safety margins to account for the intersubject variability of local SAR. In this work we have developed a semantic segmentation method based on deep learning, which is able to generate a subject-specific body model for personalized RF exposure prediction at 7T.

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