Ultrasound based neurosurgical navigation with uncertainty visualization

具有不确定性可视化的基于超声的神经外科导航

• 批准号: 10346234
• 负责人: SARAH FRISKEN
• 金额: $ 51.09万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-02 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10346234
• 关键词: 3-Dimensional; Address; Adoption; Algorithms; Area; Awareness; Brain; Brain Neoplasms; Clinical; Clinical Data; Complex; Data; Data Set; Databases; Decision Making; Environment; Excision; Financial compensation; Functional Imaging; Glioma; Goals; Image; Image-Guided Surgery; Institution; Institutional Review Boards; Lead; Location; Maps; Measurement; Measures; Methods; Mind; Modeling; Modernization; Multimodal Imaging; Navigation System; Neurologic Deficit; Neuronavigation; Neurosurgeon; Operating Rooms; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Physics; Prognosis; Radiation therapy; Reproducibility; Research; Residual Tumors; Risk; Soft Tissue Neoplasms; Spatial Distribution; Structure; Surgeon; Surgical Instruments; Surgically-Created Resection Cavity; System; Technology; Testing; Tissues; Uncertainty; Visualization; Work; anatomic imaging; base; brain shape; brain tissue; brain tumor resection; design; dynamic system; feature detection; image guided; image guided therapy; image registration; improved; innovation; insight; neurosurgery; novel; open source; shared database; software development; technology development; tumor; ultrasound

Surgical resection is the initial treatment for nearly all brain tumors and the extent of resection is strongly correlated with prognosis. However, because brain tumors, especially gliomas, are intimately involved in surrounding functioning brain tissue, aggressive resection must be balanced against the risk of causing new neurological deficits. Modern advances in anatomical and functional imaging and the widespread adoption of neuro-navigation now help neurosurgeons to plan and execute an optimal surgical approach. Unfortunately, changes in the shape of the brain during surgery, known as brain shift, invalidate the assumption of all commercial neuro-navigation systems that preoperative data can be mapped to patient coordinates using rigid registration. Because brain shift progresses during surgery, the rigid registration of neuro- navigation systems is least accurate at the critical final stages of resection when the marginal tissue is being removed. There has been more than 20 years of research invested in measuring, modeling and compensating for brain shift with the goal of providing neuro-navigation systems with an accurate nonrigid registration from preoperative image data to the patient’s brain in the presence of brain shift. While results are promising, they are not yet accurate enough to be incorporated into commercial systems. Nonrigid registration is subject to both measurement and modeling uncertainty that varies throughout 3D space. Most nonrigid registration methods do not attempt to quantify this uncertainty and, to our knowledge, there have been no attempts to present this uncertainty to the surgeon. We believe that it is important to make surgeons aware of this uncertainty so that they can make informed decisions, particularly in locations where uncertainty is high. In this project, we plan to investigate nonrigid registration algorithms that model registration uncertainty explicitly, semi-automatic and fully-automatic nonrigid registration methods that utilize registration uncertainty to iteratively guide registration improvements, and visualization paradigms for effective presentation of registration uncertainty to surgeons in the surgical environment. We hypothesize that effective representation and visualization of registration uncertainty for brain shift correction in neuro-navigation will 1) lead to iterative semi-automatic and fully-automatic nonrigid registration methods that improve registration accuracy and 2) allow neurosurgeons to make more informed decisions during tumor resections that will lead to increased clinical impact of image-guided neurosurgery. We will carry out the following Aims: 1. Develop novel feature- based image registration algorithms that represent uncertainty explicitly; 2. Use registration uncertainty maps to guide semi- and fully-automatic nonrigid registration; 3. Evaluate the utility of nonrigid registration with uncertainty visualization in a clinical setting.

手术切除是几乎所有脑肿瘤的初始治疗方法，切除程度与 预后。但是，由于脑肿瘤，尤其是神经胶质瘤，与周围的大脑密切相关 组织，侵略性切除必须与引起新神经系统缺陷的风险保持平衡。现代进步 解剖学和功能成像以及神经活动的宽度采用现在有助于神经外科医生计划和 执行最佳手术方法。不幸的是，手术过程中大脑形状的变化，称为大脑 班次，使所有商业神经循环系统的假设无效，术前数据可以映射到 患者使用刚性注册进行坐标。由于大脑在手术过程中的进展，因此 在去除边缘组织时，在切除的关键最终阶段，导航系统的准确性最低。 通过用于测量，建模和补偿大脑转移的研究已有20多年的研究 从术前图像数据到提供准确的非矛盾注册的神经循环系统的目的 在大脑转移的情况下，患者的大脑。虽然承诺结果，但它们还不够准确 并入商业系统。非辅助注册受测量和建模不确定性的约束 这在3D空间中各不相同。大多数非缔合的注册方法都不试图量化这种不确定性，并且 我们的知识，没有试图向外科医生提出这种不确定性。我们认为这很重要 使外科医生意识到这种不确定性，以便他们可以做出明智的决定，尤其是在 不确定性很高。在该项目中，我们计划调查对注册建模的非凝聚注册算法 使用注册 迭代指导注册改进和可视化范例的不确定性有效地呈现 在外科环境中对外科医生的注册不确定性。 我们假设有效的表示和可视化对大脑转移校正的注册不确定性 神经运动将1）导致迭代性半自动和完全自动的非辅助注册方法，以改善 注册精度和2）允许神经外科医生在肿瘤切除过程中做出更明智的决定 增加图像引导神经外科的临床影响。我们将执行以下目的：1。开发新颖的功能 - 明确表示不确定性的基于的图像注册算法； 2。使用注册不确定性图指导 半自动和完全自动的非刚性注册； 3。通过不确定性可视化评估非凝聚登记的效用 在临床环境中。