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Abstract #3992

SAR-Efficient RF Shim Prediction via Machine Learning

Julianna D. Ianni1,2, Zhipeng Cao1,2, and William A. Grissom1,2,3,4

1Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 2Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, 3Department of Radiology, Vanderbilt University, Nashville, TN, United States, 4Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States

A method is presented for prediction of patient-tailored, SAR-efficient RF shims via machine learning. An iterative training scheme allows fast prediction of SAR-efficient shims for new head phantoms using little B1+ map data.

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