A Comprehensive Pipeline for Electric-Field-Guided Transcranial Magnetic Stimulation Targeting and Optimization

Objective: Transcranial magnetic stimulation (TMS), as a non-invasive means of neuromodulation, plays a crucial role in rehabilitation. Recent studies highlight that modeling the TMS-induced electric field (E-field) is essential to maximize the personalized treatment efficacy. Despite advancements in various E-field calculation methods, classic numerical calculation pipelines remain time-consuming and rely on whole head segmentation, and deep learning-based pipelines suffer from limited interpretability and stability. Methods: We develop a comprehensive pipeline that supports both numerical methods and deep learning methods for TMS targeting and optimization based on local E-field, called PLED. This pipeline mainly consists of local image patch extraction, tissue segmentation, local E-field estimation, and coil placement optimization. Notably, prior information about tissue conductivity and primary E-field from the coil is embedded into the deep learning model. Results: We have conducted extensive experiments on four datasets involving millions of local image patches in total. It is examined that our pipeline runs over 40 times faster on CPU and 100 times faster with GPU acceleration than classic numerical calculation pipelines for coil placement optimization. Meanwhile, compared to other deep learning-based pipelines, our pipeline achieves higher accuracy at most potential stimulation sites across the entire brain. Conclusion: Our proposed pipeline enables rapid, accurate, and robust local E-field estimation and coil placement optimization. Significance: Our pipeline would enhance stimulation efficacy and reduce data processing time in the precise personalized TMS treatment and rehabilitation.