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Abstract #4269

Real-time electric field estimation in transcranial magnetic stimulation using deep learning and magnetic resonance imaging

Guoping Xu1,2, Yogesh Rathi2,3, Joan A Camprodon3,4, and Lipeng Ning2,3
1Wuhan Institute of Technology, Wuhan, China, 2Brigham and Women's Hospital, Boston, MA, United States, 3Harvard Medical School, Boston, MA, United States, 4Massachusetts General Hospital, Boston, MA, United States

Transcranial magnetic stimulation (TMS) is an effective treatment approach for mental disorders. Accurate and rapid prediction of TMS-evoked electric field (E-field) in brain tissue is important for accurate targeting and to understand the mechanism of treatment response. The standard method for E-field prediction is based on physical modeling which usually takes long computational time. In this work, we introduce a method based on deep neural networks (DNNs) for real-time E-field prediction. We show that the trained DNN can predict high-precision whole-brain E-field in 0.24 seconds. Moreover, diffusion-MRI based tissue conductivity tensor can improve the prediction accuracy of E-field.

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