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

经颅磁刺激（TMS）是FDA批准用于治疗抑郁症，强迫症， 疾病和吸烟成瘾，并且正在进行新的心理健康适应症的临床试验。然而，在这方面， 患者之间的可变反应是TMS的显著局限性。一个促成因素可能是缺乏适当的 TMS靶向相关皮质区域的个性化和可重复性。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。 虽然少数FDA批准的TMS治疗设备包含神经导航，但其具有显著的 缺点，包括不舒适的跟踪头盔，由于头盔相对运动而降低的准确度， 到头部、耗时的配准以及专用光学或电磁跟踪设备的高成本。 能够解决传统神经导航局限性的新技术最近已经成为 可行的，包括廉价的消费级深度相机和先进的计算机视觉算法 从而允许对诸如面部和头部的自然对象的精确跟踪。我们的目标是利用这些进步 为TMS计算机视觉神经导航开发一种精确、低成本、无跟踪系统 （CVN）。目的1是使用消费级深度相机来检测对象头部上的关键点，包括 或者是附着在头部的传统反射标记，或者是用于无跟踪导航的解剖标志。 这些算法将利用摄像头的几个功能，包括可见光视频馈送、红外深度 扫描和多相机同步，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将配对两台摄像机， 获取可见光和红外光立体数据。立体数据将与不太精确的原始深度相结合 由每个相机提供的用于在3D空间中定位关键点的信息。目标3是跟踪 受试者的头部相对于TMS线圈。结合稀疏的关键点，精确度较低但密集的表面 每个摄像机产生的信息，以及多帧时间信息，CVN将自动注册 头部定位到基于MRI个体头部模型，或者如果一个不可用，则个性化头部模板 一个模型库。将相对于TMS线圈计算头部位置，该位置将使用 相同的方法和永久安装的反射标记。我们将微调头部跟踪算法， 来自不同人类受试者样本的数据，以及完整的CVN系统将被测试并与 传统的神经导航设备都与台式测量和健康志愿者的研究， 确定准确性和再现性。总的来说，拟议的神经导航技术可以协同 目前的趋势是基于fMRI的TMS靶向个性化， 心理健康障碍的干预措施。

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.