Real-time visualization and precision targeting in transcranial magnetic stimulation

项目摘要 标题：经颅磁刺激中的实时可视化和精确定位 经颅磁刺激(TMS)是一种基于无创设备的神经调节技术，用于探测 神经网络和治疗精神障碍，如严重抑郁障碍(MDD)和强迫症- 强迫症(OCD)。TMS的治疗效果依赖于放置TMS线圈进行准确刺激 潜在的与疾病相关的大脑靶点。由于TMS诱发电场(E-场)受复杂因素的影响 组织结构和大脑几何，它依赖于使用计算算法，如边界元素 建模(BEM)和有限元建模(FE)，以估计刺激部位。但相对较长的时间 这些方法的计算时间限制了脑内刺激目标的实时可视化 标测和计算治疗计划的最佳线圈位置。在这笔赠款中，我们建议开发 一种基于深度神经网络的电场加速预报方法我们将开发一种新型的深层神经-- 利用具有各向异性的有限元算法计算的训练数据来预测电场的网络结构 组织传导性。然后，我们将训练好的神经网络集成到3DSlicer软件模块中，用于实际- 时间电磁场可视化。此外，我们还将开发一种计算算法来搜索最优线圈 在临床可行的时间内最大限度地刺激选定的大脑靶点的位置。其结果是 这笔拨款将把深度学习技术转化为一种有用的工具，以加强TMS在临床上的应用 研究。

Project Summary Title: Real-time visualization and precision targeting in transcranial magnetic stimulation Transcranial magnetic stimulation (TMS) is a non-invasive device-based neuromodulation technique for probing neuronal networks and treating mental disorders such as major depressive disorder (MDD) and Obsessive- Compulsive Disorder (OCD). The treatment efficacy of TMS relies on placing TMS coils to accurately stimulate the underlying disease-related brain target. Since TMS-evoked electric field (E-field) is affected by complex tissue structures and brain geometry, it relies on using computational algorithms, such as boundary element modeling (BEM) and finite element modeling (FEM), to estimate the stimulation site. But the relative long computation time of these methods is a limitation for real-time visualization of the stimulation target during brain mapping and for computing the optimal coil position for treatment planning. In this grant, we propose to develop a deep-neural-network based method to accelerate E-field prediction. We will develop a novel deep-neural- network architecture to predict E-field by using training data computed using the FEM algorithm with anisotropic tissue conductivity. Then, we will integrate the trained neural network into a 3DSlicer software module for real- time E-field visualization. Moreover, we will develop a computational algorithm to search for the optimal coil position to maximize the stimulation of a selected brain target within a clinically feasible time. The outcome of this grant will transform deep-learning techniques into a useful tool to enhance the application of TMS in clinical research.