Accurate, low-cost, trackerless neuronavigation for transcranial magnetic stimulation

经颅磁刺激(TMS)被FDA批准用于治疗抑郁症、强迫症 精神障碍和吸烟成瘾，目前正在进行新的精神健康指征的临床试验。然而， 不同患者的反应不同是TMS的一个重要限制。一个促成因素可能是缺乏适当的 靶向相关皮质区域的TMS的个体化和重复性。精准个性化 靶向需要神经导航系统来跟踪TMS线圈相对于患者头部的位置。 虽然FDA批准的TMS治疗设备中有一小部分包含神经导航，但它具有重要的意义 缺点，包括不舒服的跟踪头盔，由于头盔相对运动的准确性降低 对于头部来说，专用光学或电磁跟踪设备的注册耗时且成本高。 可以解决传统神经导航局限性的新技术最近已经成为 可行，包括廉价的消费级深度摄像头和先进的计算机视觉算法 允许准确跟踪自然对象，如面部和头部。我们的目标是利用这些进步 开发一种精确、低成本、无轨迹的TMS计算机视觉神经导航系统 (CVN)。目标1是使用消费级深度相机来检测受试者头部的关键点，包括 要么是固定在头部的传统反射标记，要么是用于无轨导航的解剖地标。 这些算法将利用摄像机的几个功能，包括可见光视频馈送、红外深度 扫描和多摄像机同步，可以一起处理，以稳健地提取3D空间 信息。目标2是在3D空间中定位头部关键点。为此，CVN将配对两个摄像头以 获取可见光和红外光立体数据。立体数据将与精度较低的原始深度进行组合 每个摄像机提供的信息，用于在3D空间中定位关键点。目标3是跟踪 受试者头部相对于TMS线圈。结合稀疏关键点，精度较低但密度较高的曲面 每个摄像头产生的信息，以及多帧时间信息，CVN会自动注册 将头部位置转换为基于MRI的个人头部模型或个性化头部模板(如果没有的话) 从一个模型库。磁头位置将相对于TMS线圈进行计算，该线圈将使用 相同的方法和永久安装的反射标记。我们将对头部跟踪算法进行微调 来自不同样本的人类受试者的数据和完整的CVN系统将被测试并与 传统的神经导航设备既有台式测量，也有健康志愿者研究 测定准确度和重现性。总体而言，拟议的神经导航技术可以与 当前基于功能磁共振成像的TMS目标个性化的趋势，以实现更精确和更有效的 对精神健康障碍的干预。

Transcranial magnetic stimulation (TMS) is FDA-cleared for the treatment of depression, obsessive compulsive disorder, and smoking addiction, and there are ongoing clinical trials for new mental health indications. However, variable response across patients is a significant limitation of TMS. A contributing factor may be a lack of proper individualization and reproducibility of the TMS targeting to relevant cortical regions. Accurate individualized targeting requires neuronavigation systems that track the position of the TMS coil relative to the patient's head. While a small minority of the FDA-cleared TMS treatment devices incorporate neuronavigation, it has significant drawbacks, including uncomfortable tracking headgear, reduced accuracy due to headgear movement relative to the head, time-consuming registration, and high cost of dedicated optical or electromagnetic tracking devices. Novel technologies that could address the limitations of conventional neuronavigation have recently become feasible, including inexpensive consumer-grade depth cameras and advanced computer vision algorithms allowing accurate tracking of natural objects such as faces and heads. Our goal is to leverage these advances to develop an accurate, low-cost, and trackerless system for TMS computer-vision-based neuronavigation (CVN). Aim 1 is to use consumer-grade depth cameras to detect keypoints on the subject's head comprising either conventional reflective markers attached to the head or anatomical landmarks for trackerless navigation. The algorithms will leverage several features of the cameras, including visible light video feed, infrared depth scanning, and multi-camera synchronization that can be processed together to robustly extract 3D spatial information. Aim 2 is to localize the head keypoints in 3D space. To this end, CVN will pair two cameras to acquire visible and infrared light stereo data. The stereo data will be combined with the less accurate raw depth information provided by each camera to localize the keypoints in 3D space. Aim 3 is to track the position of the subject's head relative to the TMS coil. Combining the sparse keypoints, the less accurate but dense surface information generated by each camera, and multi-frame temporal information, CVN will automatically register the head position to an MRI-based individual head model or, if one is unavailable, a personalized head template from a model library. The head position will be computed relative to the TMS coil, which will be tracked with the same methods and permanently mounted reflective markers. We will fine tune the head tracking algorithms with data from a diverse sample of human subjects, and the complete CVN system will be tested and compared to a conventional neuronavigation device both with bench-top measurements and in a study of healthy volunteers to determine accuracy and reproducibility. Overall, the proposed neuronavigation technology could synergize with current trends toward fMRI-based personalization of TMS targeting to enable more precise and efficacious interventions for mental health disorders.