Park Tae Young, Franke Loraine, Pieper Steve, Haehn Daniel, Ning Lipeng
Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea.
Division of Biomedical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, 02792 Republic of Korea.
Biomed Eng Lett. 2024 Mar 29;14(3):393-405. doi: 10.1007/s13534-024-00373-4. eCollection 2024 May.
Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models.
经颅磁刺激（TMS）是一种基于设备的神经调节技术，越来越多地用于治疗脑部疾病。电场（E-field）建模是几种TMS临床应用中的一项重要技术，包括以精确的刺激密度对脑靶点进行精确刺激以治疗精神障碍，以及为神经外科手术规划对脑功能区进行定位。传统的电场建模方法通常计算时间较长。快速算法通常是在显著较低的空间分辨率下开发的，这降低了预测准确性，并限制了它们在实时或近实时TMS应用中的使用。这篇综述论文讨论了几种用于实时或近实时TMS电场建模的现代算法及其优缺点。所综述的方法包括诸如基表示技术和基于深度神经网络的方法等技术。本文还对可将电场建模与导航TMS集成的软件工具进行了综述，包括一种基于深度神经网络模型的用于实时导航电场映射的最新软件。
