bibtext: @InProceedings{ Fra_SlicerTMS_MICCAI2024,
                   author = { Franke, Loraine and Luo, Jie and Park, Tae Young and Kim, Nam Wook and Rathi, Yogesh and Pieper, Steve and Ning, Lipeng and Haehn, Daniel },
                   title = { { SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: We present a real-time visualization system for Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique for treating various brain disorders and mental health diseases. Our solution targets the current challenges of slow and labor-intensive practices in treatment planning. Integrating Deep Learning (DL), our system rapidly predicts electric field (E-field) distributions in 0.2 seconds for precise and effective brain stimulation. The core advancement lies in our tool’s real-time neuronavigation visualization capabilities, which support clinicians in making more informed decisions quickly and effectively. We assess our system’s performance through three studies: First, a real-world use case scenario in a clinical setting, providing concrete feedback on applicability and usability in a practical environment. Second, a comparative analysis with another TMS tool focusing on computational efficiency across various hardware platforms. Lastly, we conducted an expert user study to measure usability and influence in optimizing TMS treatment planning. The system is openly available for community use and further development on GitHub: https://github.com/lorifranke/SlicerTMS.
title: SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment
authors: Franke, Loraine Luo, Jie Park, Tae Young Kim, Nam Wook Rathi, Yogesh Pieper, Steve Ning, Lipeng Haehn, Daniel