Vessel Identification and Tracing in DSA Image Series for Cerebrovascular Surgical Planning

用于脑血管手术计划的 DSA 图像系列中的血管识别和追踪

• 批准号: 10726103
• 负责人: Nazim Haouchine
• 金额: $ 17.9万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10726103
• 关键词: 3-Dimensional; Advanced Development; Algorithms; Anatomy; Angiography; Architecture; Arteries; Arteriovenous malformation; Biomedical Technology; Blood Circulation; Blood Vessels; Blood flow; Classification; Clinical; Closure by clamp; Code; Color; Data; Development; Diagnosis; Digital Subtraction Angiography; Dose; Excision; Failure; Geometry; Goals; Graph; Health; Hemorrhage; Image; Individual; Infarction; Interruption; Intervention; Intuition; Label; Machine Learning; Methods; Mission; Morbidity - disease rate; Operative Surgical Procedures; Outcome; Pathology; Patients; Pattern; Planning Techniques; Postoperative Period; Procedures; Protocols documentation; Public Health; Research; Retrospective Studies; Roentgen Rays; Series; Shapes; Techniques; Technology; Testing; Time; United States National Institutes of Health; Veins; Venous; Visual; Visualization; Visualization software; brain arteriovenous malformations; cerebrovascular; cerebrovascular surgery; classification algorithm; clinical practice; convolutional neural network; cost; deep neural network; feeding; grasp; hemodynamics; image processing; image visualization; improved; independent component analysis; innovation; malformation; neurovascular; new technology; novel; preservation; segmentation algorithm; support tools; tool; treatment planning

Project Summary Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where veins and arteries are entangled and need to be carefully identified. The presented project aims at enhancing DSA image series to remove this difficulty. Our long-term goal is to contribute toward the development of intuitive and interpretable visualization tools to improve the diagnosis, planning and treatement of neurovascular pathologies. Our overall objectives in this project are to (i) develop a new method, based on machine learning, to localize the AVM and distinguish between veins and arteries surrounding it in DSA image series, and (ii) develop an algorithm that classifies arteries as terminal, en passage or bystanders. In addition to examine the impact of our approach in planning neurovascular surgeries through a retrospective study. The rationale for this project is that such technology will likely enhance DSA imaging and provide an interpretable tool to clinicians that will facilitate planning cerebral AVM procedures, and furthermore, provide a decision support tool that can be used during surgery to help review and correlate the anatomic findings seen in the surgical field to the preoperative angiogram. To attain the overall objectives, the following two specific aims will be pursued: (1) develop an image processing algorithm for AVM localisation and artery/vein classification and (2) develop an algorithm that can identify arteries as terminal, en passage or bystanders. Under the first aim, we will test our working hypothesis to show that it is possible to localize an AVM in DSA image series and to distinguish between feeding arteries and draining veins surrounding or creating the entanglement, using deep neural networks to outline the shape of the AVM in the images and independent component analysis to understand blood flow disruption. For the second aim, we will establish a set of rules to classify arteries contributing or not to the AVM and implement these rules into a dynamic instance segmentation algorithm that will trace vessels individually, in a DSA image series. This algorithm with rely on a foreground/background subtraction to genrate a vascular graph and on deep neural network to classify vascular junctions to produce an instanciated graph. Using this graph and the pre-defined rules it will be possible to visually distinguish between the different artery patterns. The proposed project is innovative because it will be possible to automatically distinguish veins from arteries and classify arteries as terminal, en passage, or bystander in a DSA image series without altering standard clinical routines. The proposed project is significant because it will enhance DSA imaging with an intuitive visualization allowing clinicians to better understand AVM-induced vessel entanglement in order to preserve vessels from being mistakenly clamped during surgery, thus avoiding intraoperative hemorrhaging or postsurgical deficits. These results are expected to have an important positive impact because they will ultimately provide new opportunities for the development of novel planning techniques to improve the treatment of neurovascular malformations.

项目摘要 尽管数字减法血管造影（DSA）是可视化脑血管的最重要成像 解剖学，临床医生的解释仍然很困难。治疗动静脉时尤其如此 畸形（AVM），静脉和动脉被纠缠并需要仔细识别。提出的 项目旨在增强DSA图像系列以消除这一困难。我们的长期目标是为 开发直观和可解释的可视化工具，以改善诊断，计划和治疗 神经血管病理。我们在该项目中的总体目标是（i）基于 机器学习，定位AVM并区分DSA图像中周围的静脉和动脉 系列和（ii）开发一种将动脉分类为终端，EN密码或旁观者的算法。此外 通过回顾性研究来研究我们方法在计划神经血管手术中的影响。 该项目的理由是，此类技术可能会增强DSA成像并提供可解释的工具 向临床医生提供促进计划脑AVM程序的临床医生，并提供决策支持 可以在手术过程中使用的工具，以帮助审查和关联手术场中看到的解剖结构 术前血管造影。为了实现总体目标，将实现以下两个特定目标： （1）开发用于AVM定位和动脉/静脉分类的图像处理算法，（2） 可以将动脉识别为终端，EN通道或旁观者的算法。在第一个目标下，我们将测试我们的 工作假设表明可以将AVM定位在DSA图像系列中并区分 使用深层神经网络来喂养动脉和排水静脉或创建纠缠 在图像和独立组件分析中概述AVM的形状，以了解血流 破坏。为了第二个目标，我们将建立一组规则，以对动脉进行分类或不为AVM进行分类 并将这些规则实施到动态实例分割算法中，该算法将单独跟踪容器 在DSA图像系列中。该算法依赖于前景/背景减法来基因造成血管 图形和深度神经网络上，以对血管连接进行分类以产生实体图。使用此 图和预先确定的规则可以在视觉上区分不同动脉模式。 拟议的项目具有创新性，因为可以自动将静脉与动脉区分开 将动脉分类为DSA图像系列中的终端，EN通道或旁观者，而不改变标准临床 例程。拟议的项目非常重要，因为它将通过直观的可视化增强DSA成像 允许临床医生更好地理解AVM引起的船只纠缠，以保护船只 手术期间错误地夹紧，因此避免了术中出血或术后降低。 预计结果将产生重要的积极影响，因为它们最终将提供新的机会 为了开发新的计划技术，以改善神经血管畸形的治疗。