Abstract #2491

A Novel Specific Absorption Rate Prediction Framework Using Multi-Task Feedback Generative Adversarial Learning: Application to 10.5 T Head MRI

Jinyoung Kim¹, Alireza Sadeghi-Tarakameh¹, Angel Torrado-Carvajal^2,3, and Yigitcan Eryaman¹

¹Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, ²Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ³Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain

In this study, we propose a novel local SAR prediction framework based on deep learning. To this end, we introduce multi-task feedback adversarial learning to simultaneously predict local SAR distribution and its peak SAR value. The proposed model learns a mapping between simulated B1+ magnitude/tissue property maps and local SAR provided by EM simulations. Given query inputs with the properly trained model, the generator produces the local SAR distribution slice by slice, and the local SAR peak estimator predicts the upper bound of local SAR values. Validation results show that the proposed model may allow online subject-specific local SAR prediction.

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