SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment
Core Concepts
Transcranial Magnetic Stimulation (TMS) visualization system, SlicerTMS, enhances treatment planning with real-time E-field predictions and deep learning integration.
Abstract
1. Abstract:
Introduces a real-time visualization system, SlicerTMS, for TMS treatment.
Utilizes Deep Learning to predict E-field distributions rapidly.
Enhances neuronavigation visualization capabilities for informed decision-making.
2. Introduction:
Discusses the importance of precise coil placement in TMS therapy.
Highlights challenges in real-time prediction and visualization in clinical settings.
3. Implementation:
3.1 Neuronavigation Visualization Component:
Describes the architecture and components of SlicerTMS interface.
Provides insights into rendering predicted E-fields on different modalities.
3.2 Deep Learning Pipeline for E-Field Prediction:
Details the training process of a multi-scale 3D-Res-UNet model for E-field prediction.
Mentions the use of MRI images from the Human Connectome Project for training.
3.3 AR Component with WebXR:
Explains the web server connection for mobile-based AR interaction.
4. Performance Evaluation:
4.1 Technical Performance Experiments of SlicerTMS:
Presents timings for E-field prediction and visualization on different hardware configurations.
4.2 Comparative Performance Analysis:
Compares SlicerTMS with SimNIBS in terms of visualization speed on brain mesh.
5. Expert User Study:
Discusses an expert user study evaluating usability and workflow efficiency of SlicerTMS.
6. Discussion:
Highlights the advancements and limitations of SlicerTMS compared to existing tools like SimNIBS.
7. Conclusion and Future Work:
Concludes by emphasizing the potential impact of SlicerTMS on treatment planning and future improvements.
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SlicerTMS
Stats
Neural Network runs in less than 0.2 seconds on average.
Real-time visualization takes less than ten milliseconds on average.