Imaging, Guidance, and QA for Emerging High-Precision Neurosurgical Techniques

新兴高精度神经外科技术的成像、指导和质量保证

• 批准号: 10218277
• 负责人: JEFFREY H SIEWERDSEN
• 金额: $ 66.28万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10218277
• 关键词: 3-Dimensional; Acceleration; Affect; Alzheimer' s Disease; Amygdaloid structure; Anatomy; Brain; Cadaver; Cell Nucleus; Cerebrospinal Fluid; Clinical Research; Complication; Data; Deep Brain Stimulation; Dementia; Development; Diagnostic radiologic examination; Dose; Electrodes; Endoscopy; Future; Generations; Gilles de la Tourette syndrome; Hippocampus (Brain); Hypothalamic structure; Image; Image-Guided Surgery; Individual; Intracranial Hemorrhages; Magnetic Resonance Imaging; Mental Depression; Metals; Methods; Modality; Modeling; Morphologic artifacts; Motion; Movement Disorders; Neurologic; Neuronavigation; Obesity; Operative Surgical Procedures; Parkinson Disease; Patients; Performance; Quantitative Evaluations; Research; Resolution; Robotics; Spinal; Structure; Surface; System; Systems Integration; Techniques; Technology; Thalamic Nuclei; Three-Dimensional Image; Time; Transcend; Translating; Translations; Uncertainty; Ventricular; Visualization; Work; autism spectrum disorder; base; bone; brain parenchyma; clinical translation; cone-beam computed tomography; deep brain stimulator; design; image guided; image reconstruction; image registration; imaging system; improved; industry partner; interest; morphogens; multimodality; neurophysiology; neurosurgery; next generation; novel; novel therapeutics; prototype; reconstruction; research clinical testing; robot assistance; safety outcomes; simulation; skull base; soft tissue; success

PROJECT SUMMARY / ABSTRACT Emerging neurosurgical techniques offer potential breakthroughs in treatment of a growing spectrum of movement disorders and dementia (including Alzheimer’s disease, Tourette’s syndrome, autism, depression, and even obesity). These emerging surgical approaches extend the established success of deep-brain stimulation (DBS) in Parkinson’s disease by using novel electrode stimulators delivered trans-ventricularly to targets about the hypothalamus. While endoscopic approach provides reliable access to the ventricles, such access imparts a loss of cerebrospinal fluid (CSF) and brain shift up to ~10 mm in the very regions of interest for these novel DBS therapies. Therefore, realizing the benefit of such promising techniques requires advances beyond the state of the art in neuro-navigation. Moreover, the use of novel, directional electrodes in such techniques requires a means to guide and confirm stimulator placement. Especially in the early stages of development of such novel therapies, it is important to resolve uncertainties related to geometric precision in order to differentiate from underlying neurophysiology and other factors that may affect safety and outcome. We propose to develop and evaluate the following advances in intraoperative imaging, registration, and guidance to realize a platform for robot-assisted ventriculoscopic approach to deep-brain targets in a manner that overcomes conventional limitations of neuro-navigation and supports the emerging generation of novel DBS therapies: (Aim 1) Develop high-quality intraoperative cone-beam CT (CBCT) using 3D image reconstruction methods that propel image quality beyond conventional limits of CBCT, providing image quality sufficient to drive deformable registration with preop MRI, precisely localize stimulator placement, and provide a check against complication / intracranial hemorrhage. (Aim 2) Develop 3D-2D image registration methods to relate low-dose intraoperative radiographs with: (a) preop MRI for automatic patient registration; and (b) parametric models of DBS electrodes (including novel directional stimulators) for guidance and confirmation of stimulator placement with precision and accuracy beyond that of conventional tracking. (Aim 3) Develop multi-modality deformable image registration (MR-CBCT) to resolve alignment between preop MRI and intraoperative CBCT – particularly peri-ventricular deep-brain deformation following CSF egress – using a fast, modality- insensitive, diffeomorphic Demons method for accurate transformation of MRI / planning data to CBCT and endoscopy. (Aim 4) Develop endoscopic video registration to render 3D image and planning information directly in the endoscopic scene, providing accurate visualization of target and critical structures during ventriculoscopic approach. (Aim 5) Translate the methods from Aims 1-4 to clinical studies for quantitative evaluation of performance under realistic conditions, and combine within an integrated system for robot-assisted ventriculoscopy (RAV) approach to DBS targets. The proposal advances previous work in skull base and spinal neurosurgery, offering a high likelihood of success in enabling next-generation DBS, and creates an integrated system for image-guided surgical robotics beyond the state of the art – a valuable testbed for development and translation of clinical systems under future academic-industry partnership.

项目概要/摘要 新兴的神经外科技术为治疗不断增长的运动范围提供了潜在的突破 疾病和痴呆症（包括阿尔茨海默病、抽动秽语综合症、自闭症、抑郁症，甚至肥胖症）。这些 新兴的手术方法通过以下方式扩展了深部脑刺激（DBS）在帕金森病中的既定成功： 使用新型电极刺激器经心室传递到下丘脑周围的目标。内窥镜检查时 该方法提供了进入脑室的可靠通道，但这种通道会导致脑脊液 (CSF) 损失和脑转移 在这些新颖的 DBS 疗法感兴趣的区域可达约 10 毫米。因此，实现这样的好处 有前景的技术需要超越神经导航领域最先进的技术。而且，利用新颖的、 此类技术中的定向电极需要一种方法来引导和确认刺激器的放置。尤其是在 在此类新疗法开发的早期阶段，解决与几何精度相关的不确定性非常重要 为了区分潜在的神经生理学和其他可能影响安全性和结果的因素。 我们建议开发和评估术中成像、配准和指导方面的以下进展 实现一个机器人辅助脑室镜方法进入深部脑目标的平台，克服了 神经导航的传统局限性并支持新一代新型 DBS 疗法： （目标 1）使用 3D 图像重建方法开发高质量的术中锥形束 CT (CBCT) 图像质量超出 CBCT 的传统限制，提供足以驱动变形配准的图像质量 术前 MRI，精确定位刺激器的位置，并检查并发症/颅内出血。 （目标 2）开发 3D-2D 图像配准方法，将低剂量术中 X 线照片与以下各项相关联： (a) 术前 MRI 自动患者登记； (b) DBS 电极（包括新型定向刺激器）的参数模型 指导和确认刺激器放置的精确度和准确度超出了传统跟踪。 （目标 3）开发多模态可变形图像配准 (MR-CBCT)，以解决术前 MRI 和 术中 CBCT – 特别是脑脊液流出后的脑室周围深部脑变形 – 使用快速、方式- 不敏感的微分同胚方法，用于将 MRI/计划数据准确转换为 CBCT 和内窥镜检查。 （目标 4）开发内窥镜视频配准以直接在内窥镜中渲染 3D 图像和规划信息 场景，在心室镜方法期间提供目标​​和关键结构的准确可视化。 （目标5）将目标1-4的方法转化为临床研究，以定量评估现实条件下的表现 条件，并结合在一个集成系统中，用于机器人辅助心室镜检查 (RAV) 方法来实现 DBS 目标。 该提案推进了先前在颅底和脊柱神经外科方面的工作，为实现这一目标提供了很高的成功可能性 下一代 DBS，并为图像引导手术机器人创建了超越现有技术的集成系统 – 在未来学术-工业合作关系下，临床系统的开发和转化是一个有价值的测试平台。