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

用于经颅磁刺激的准确、低成本、无跟踪器神经导航

• 批准号: 10615765
• 负责人: Juan Matias Di Martino
• 金额: $ 54.02万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615765
• 关键词: 3-Dimensional; Address; Adhesives; Algorithms; Anatomy; Atlases; Brain; Brain Diseases; Brain region; Clinical; Clinical Research; Clinical Trials; Computer Models; Computer Vision Systems; Consumption; Data; Dedications; Devices; Elasticity; Electromagnetics; Equipment; Face; Functional Magnetic Resonance Imaging; Goals; Goggles; Head; Individual; Libraries; Light; MRI Scans; Magnetic Resonance Imaging; Maintenance; Measurement; Measures; Mental Depression; Mental Health; Mental disorders; Methods; Minority; Modeling; Movement; Neuronavigation; Obsessive-Compulsive Disorder; Optics; Outcome; Patients; Pharmaceutical Preparations; Positioning Attribute; Procedures; Process; Protocols documentation; Reproducibility; Research; Sampling; Scalp structure; Scanning; Skin; Speed; Subject Headings; Surface; System; Techniques; Technology; Testing; Time; Transcranial magnetic stimulation; Treatment Efficacy; Visible Radiation; clinical practice; cost; efficacious intervention; experience; healthy volunteer; human subject; improved; magnetic field; new technology; noninvasive brain stimulation; predicting response; response; smoking addiction; success; synergism; tool; trend

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.

经颅磁刺激（TMS）进行FDA清理以治疗抑郁，强迫性强迫 疾病和吸烟成瘾，以及正在进行的临床试验，以实现新的心理健康迹象。然而， 跨患者的可变反应是TMS的重要限制。促成因素可能缺乏适当的 靶向相关皮质区域的TMS的个性化和可重复性。准确的个性化 靶向需要神经训练系统，该系统跟踪相对于患者头部的TMS线圈的位置。 虽然少数FDA清除的TMS处理设备融合了神经元活动，但它具有显着的 缺点，包括不舒服的跟踪头饰，降低了由于头饰运动相对的精度 在头部，耗时的注册以及专用光学或电磁跟踪设备的高成本。 可以解决常规神经训练局限的新技术最近已成为 可行的，包括廉价的消费级深度摄像机和先进的计算机视觉算法 允许准确跟踪天然物体，例如面部和头部。我们的目标是利用这些进步 为基于计算机视觉的神经训练开发准确，低成本和无跟踪的系统 （CVN）。 AIM 1是使用消费级深度摄像机来检测受试者头部的关键点，其中包括 用于头部上的常规反射标记，或用于无跟踪导航的解剖标记。 该算法将利用摄像机的几个功能，包括可见光视频供稿，红外深度 扫描和多相机同步可以一起处理以鲁棒提取3D空间 信息。 AIM 2是将头部关键点定位在3D空间中。为此，CVN将将两个摄像机与 获取可见和红外光立体声数据。立体声数据将与较少准确的原始深度相结合 每个相机提供的信息将关键点本地定位在3D空间中。目标3是跟踪 受试者的头部相对于TMS线圈。结合稀疏关键点，较少而密集的表面 每个相机生成的信息以及多帧的时间信息，CVN将自动注册 基于MRI的个人头模型的头部位置，或者如果不可用，则是个性化的头部模板 来自模型库。头部位置将相对于TMS线圈进行计算，该线圈将通过 相同的方法和永久安装的反射标记。我们将通过 来自各种人类受试者样本的数据，将测试完整的CVN系统，并将其与 传统的神经训练装置均采用基准测量以及对健康志愿者的研究 确定准确性和可重复性。总体而言，拟议的神经训练技术可以与 当前基于fMRI的TMS靶向性个性化的趋势，以使更精确和有效 精神疾病的干预措施。