SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment

TL;DR

SlicerTMS is a real-time visualization system that enhances TMS treatment planning by rapidly predicting electric fields and providing neuronavigation support, thereby improving clinical decision-making and efficiency.

Contribution

The paper introduces a novel real-time neuronavigation visualization tool for TMS that integrates deep learning for rapid electric field prediction, addressing current slow and labor-intensive practices.

Findings

System predicts electric fields in 0.2 seconds

Improves clinical decision-making in TMS treatment

Demonstrates usability and efficiency through multiple studies

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: \url{https://github.com/lorifranke/SlicerTMS}.