Abstract #3992

SAR-Efficient RF Shim Prediction via Machine Learning

Julianna D. Ianni^1,2, Zhipeng Cao^1,2, and William A. Grissom^1,2,3,4

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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