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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.
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.
Neural Network runs in less than 0.2 seconds on average. Real-time visualization takes less than ten milliseconds on average.

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by Loraine Fran... at 03-14-2024

Deeper Inquiries

How can real-time visualization systems like SlicerTMS revolutionize other areas of healthcare technology?


How can advancements in XR technologies further enhance the capabilities of systems like SlicerTMS?


What are potential drawbacks or criticisms regarding the integration of deep learning in TMS treatment planning?

TMS治療計画へ深層学習(DL)の統合へ対する潜在的欠点や批判点も存在します。 データ依存性: 深層学習モデルは大量のラベル付きデータセットからパラメーター調整される必要があるため,十分量及び質保証したデータ収集作業および前処理工程等多く時間費用要求 ブラックボックス問題: DL モデル内部表現複雑度高く, その意思決定根拠不透明性発生.この「ブラックボックス」特性ゆえ,信頼性低下およびエキスパート間共有困難 一般化能力不足: 計算済みE-Field予測精度高い場合でも未知条件下では予測失敗起き易い.これまだ開発段階ゆえ,一般化能力改善必要