SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917611788304384 |
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| author | Franke, Loraine Park, Tae Young Luo, Jie Rathi, Yogesh Pieper, Steve Ning, Lipeng Haehn, Daniel |
| author_facet | Franke, Loraine Park, Tae Young Luo, Jie Rathi, Yogesh Pieper, Steve Ning, Lipeng Haehn, Daniel |
| contents | 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: \url{https://github.com/lorifranke/SlicerTMS}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_06459 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment Franke, Loraine Park, Tae Young Luo, Jie Rathi, Yogesh Pieper, Steve Ning, Lipeng Haehn, Daniel Signal Processing Graphics Human-Computer Interaction Image and Video Processing Neurons and Cognition 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: \url{https://github.com/lorifranke/SlicerTMS}. |
| title | SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment |
| topic | Signal Processing Graphics Human-Computer Interaction Image and Video Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2305.06459 |