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: 10470020
• 负责人: JEFFREY H SIEWERDSEN
• 金额: $ 67.23万
• 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/10470020
• 关键词: 3-Dimensional; Acceleration; Affect; Algorithms; Alzheimer' s Disease; Amygdaloid structure; Anatomy; Brain; Cadaver; Cell Nucleus; Cerebrospinal Fluid; Clinical Research; Complication; Data; Deep Brain Stimulation; Dementia; Development; 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; radiological imaging; 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.

项目摘要 /摘要 新兴的神经外科技术为越来越多的运动范围提供了潜在的突破 疾病和痴呆症（包括阿尔茨海默氏病，图雷特综合症，自闭症，抑郁症甚至肥胖症）。这些 通过 使用新型的电极刺激剂将腹腔式递送到下丘脑的靶标。同时内窥镜 方法可靠地进入心室，例如脑脊液（CSF）和大脑移位的损失 这些新型DBS疗法的感兴趣区域最多约10毫米。因此，意识到这种好处 有前途的技术需要超出神经活动的最新技术的进步。此外，使用小说， 这种技术中的定向电子需要一种指导和确认刺激器位置的方法。特别是在 这种新型疗法发展的早期阶段，解决与几何精度有关的不确定性很重要 为了与潜在的神经生理学和其他可能影响安全性和结果的因素区分开。 我们建议开发和评估术中成像，注册和指导方面的以下进展 实现一个以克服的方式来实现机器人辅助心室方法的深脑靶标 神经运动的常规局限性并支持新型DBS疗法的出现： （AIM 1）使用3D图像重建方法开发高质量的术中锥束CT（CBCT） 图像质量超出了CBCT的常规限制，提供图像质量，足以驱动可变形的注册 Preop MRI，精确定位刺激器的位置，并提供针对并发症 /颅内出血的检查。 （AIM 2）开发3D-2D图像注册方法，将低剂量术中X光片与以下部件相关联 自动患者注册； （b）DBS电极的参数模型（包括新的定向刺激器） 超出常规跟踪的准确性和准确性，指导和确认刺激器放置。 （AIM 3）开发多模式可变形图像登记（MR-CBCT），以解决PREOP MRI和 术中CBCT - 尤其是CSF出口后室周围的深脑变形 - 使用快速，模态 - 不敏感的差异恶魔方法，用于将MRI /计划数据准确转化为CBCT和内窥镜检查。 （目标4）开发内窥镜视频注册以呈现3D图像并直接在内窥镜中计划信息 场景，在心室镜检查过程中提供目标和关键结构的准确可视化。 （目标5）将方法从目标1-4转换为临床研究，以定量评估现实情况下的性能 条件，并结合在机器人辅助心室（RAV）方法的集成系统中，用于DBS靶标。 该提案在颅底和脊柱神经外科手术中进行了以前的工作 下一代DBS，并为图像引导的外科机器人技术创建了一个集成系统，超出了艺术的状态 - 在未来的学术行业合作伙伴关系下的临床系统开发和翻译的有价值的测试床。