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

Junfeng Zhou, Yijun Zhou, Ziyang Liu

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 24, 2026
    PubMed
    Summary
    This summary is machine-generated.

    We developed PLED, a fast and accurate pipeline for Transcranial Magnetic Stimulation (TMS) targeting. This method enhances personalized treatment by improving electric field estimation and coil placement optimization for better rehabilitation outcomes.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Medical Imaging

    Background:

    • Transcranial Magnetic Stimulation (TMS) is vital for neuromodulation in rehabilitation.
    • Accurate modeling of TMS-induced electric fields (E-fields) is crucial for personalized treatment efficacy.
    • Existing E-field calculation methods face challenges in speed, interpretability, and stability.

    Purpose of the Study:

    • To develop a comprehensive pipeline (PLED) for TMS targeting and optimization.
    • To integrate both numerical and deep learning methods for local E-field estimation.
    • To improve the speed and accuracy of coil placement optimization in TMS.

    Main Methods:

    • Developed the PLED pipeline incorporating local image patch extraction and tissue segmentation.
    • Implemented local E-field estimation and coil placement optimization.
    • Embedded prior information on tissue conductivity and primary E-field into a deep learning model.

    Main Results:

    • The PLED pipeline demonstrated over 40x speed improvement on CPU and 100x on GPU compared to classic numerical methods.
    • Achieved higher accuracy in E-field estimation and coil placement optimization across the brain.
    • Extensive experiments were conducted on four datasets with millions of local image patches.

    Conclusions:

    • The PLED pipeline provides rapid, accurate, and robust local E-field estimation and coil placement optimization.
    • This approach enhances stimulation efficacy and reduces data processing time for personalized TMS treatment.
    • The pipeline is expected to advance precise personalized TMS applications in rehabilitation.