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 虽然数字减影血管造影(DSA)是显示脑血管的最重要的成像方法 解剖学上，临床医生对它的解释仍然不是fi崇拜。在动静脉治疗时尤其如此。 静脉畸形(fi)，静脉和动脉纠缠在一起，需要仔细识别。已提交的 该项目旨在增强数字减影血管造影图像系列，以消除这种diffi崇拜。我们的长期目标是为 开发直观和可解释的可视化工具，以改进诊断、计划和治疗 神经血管病理性疾病。我们在这个项目中的总体目标是：(I)开发一种新的方法，基于 机器学习，用于定位AVM并在DSA图像中区分其周围的静脉和动脉 提出了一种将ES动脉分类为终端、中间通道或旁观者的算法；(Ii)提出了一种将ES动脉分类为fi的算法。此外 通过一项回顾研究来检验我们的方法在计划神经血管手术中的影响。这个 这个项目的基本原理是，这种技术可能会增强DSA成像，并提供一种可解释的工具 为临床医生提供便利的脑动静脉畸形手术计划，并进一步提供决策支持 一种可在手术过程中使用的工具，用于帮助检查和关联外科fi手术中所见的解剖fi病变 去做手术前的血管造影。为了实现总体目标，将实现以下两个具体目标：fic： (1)开发了一种动静脉畸形定位和动静脉fi分类的图像处理算法；(2)开发了一种 一种算法，可以识别动脉是终点站、通道或旁观者。在fi的第一个目标下，我们将测试我们的 工作假说，表明可以在DSA图像序列中定位AVM并区分 供血动脉和引流周围或造成缠绕的静脉，使用深度神经网络来 在图像中勾勒出动静脉畸形的形状，并进行独立成分分析以了解血液fl的变化 颠覆。对于第二个目标，我们将建立一套规则来对导致AVM的动脉进行分类 并将这些规则实施到将单独跟踪血管的动态实例分割算法中， 在DSA图像系列中。该算法依赖于前景/背景相减来生成血管 图和深度神经网络来对血管连接进行分类，以产生实例化的图。使用这个 图表和Pre-defiNed规则将有可能在视觉上区分不同的动脉图案。 拟议的项目具有创新性，因为它将有可能自动区分静脉和动脉，并 在不改变标准临床的情况下，在DSA图像序列中将动脉分类为末端、前通道或旁观者 例行程序。建议的项目是显著的fi不能，因为它将增强数字减影血管成像与直观的可视化 使临床医生更好地了解AVM引起的血管缠绕，以保护血管免受 在手术中错误地夹住，从而避免了术中出血或手术后的fi。这些 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 用于开发新的规划技术，以改进神经血管畸形的治疗。