Abstract #2693

Patient-specific Local SAR estimation by combined field mapping and deep-learning method

Shao Che^1,2, Zhuoxu Cui^1,2, Jin Liu³, Siyuan Ding³, Peng Cao⁴, Xiaoliang Zhang⁵, Xin Liu^1,2, Hairong Zheng^1,2, Dong Liang^1,2, and Ye Li^1,2

¹Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, China, ³United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China, ⁴The University of Hong Kong, Hongkong, China, ⁵Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States

Synopsis

Keywords: Safety, SafetyA method is proposed for real time patient-specific local SAR estimation based on B1 field mapping and machine-learning. The axial component of RF E-field is estimated by electric property tomography (EPT) method from B1 field, and the transversal component of RF E-field is predicted by a cycleGAN model trained with EM simulation input data. The safety factor of peak local SAR estimation is analyzed for a large set of random transmit weighting factors and the feasibility of the method is discussed.

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