Image-Based Numerical Predictions of Hemodynamics following Vascular Intervention

血管介入后基于图像的血流动力学数值预测

• 批准号: 8458176
• 负责人: Vitaliy L Rayz
• 金额: $ 37.41万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-03 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8458176
• 关键词: Adoption; Affect; Anatomy; Aneurysm; Angiography; Arteries; Arteriovenous fistula; Biomechanics; Biomedical Research; Blood Circulation; Blood Vessels; Cerebral Aneurysm; Clinical Research; Clip; Collaborations; Computational Technique; Computer Simulation; Computing Methodologies; Descriptor; Development; Distal; Evaluation; Face; Failure; Fistula; Goals; Growth; Hemodialysis; Image; Incidence; Ink; International; Intervention; Intuition; Lead; Lesion; Liquid substance; Measurement; Methodology; Methods; Modeling; Monitor; Neck; Operative Surgical Procedures; Outcome; Patient Schedules; Patients; Pattern; Phase; Postoperative Period; Predictive Value; Procedures; Process; Radiology Specialty; Recording of previous events; Research; Risk; Solid; Solutions; Specific qualifier value; Stents; Stratification; Stress; Surgeon; Thrombus; Time; Treatment outcome; Uncertainty; Veins; Velocimetries; Work; base; case control; computerized tools; design; feeding; hemodynamics; in vivo; novel strategies; prospective; public health relevance; research facility; research study; residence; response; shear stress; simulation; time use; tool; treatment planning; vascular bed; virtual

DESCRIPTION (provided by applicant): The proposed research is aimed at developing a computational tool which would reliably predict blood vessel remodeling resulting from vascular interventions. In planning interventions which result in flow alterations, clinicians often rely on intuition rather than solid scientific evidence. Reducing this uncertainty provides an exciting opportunity for computational modeling methods which could be used to explore various interventional options. Recent advances in patient-specific computational fluid dynamics (CFD) modeling indicate that these methods might now be sufficiently mature for this task. However, an important challenge for the adoption of postoperative flow modeling is the scarcity of well-controlled cases where accurate predictions of subsequent vascular changes have been demonstrated. Furthermore, CFD methods generally lack information about the flow in the proximal and distal circulation. We propose a novel approach where these flow boundary conditions will be obtained with in vivo measurements using time-resolved phase-contrast MR velocimetry (4D MRV). The proposed image-based CFD methodology will be applied on a patient-specific basis to three types of vascular interventions with differing functional and anatomic complexities. These include: maturation of arteriovenous fistulas created for hemodialysis access; fusiform cerebral aneurysms treated by occlusion of one or more proximal vessels; and finally, fusiform cerebral aneurysms treated by flow diverter stents. Currently, there is a high incidence of unsuccessful treatment outcomes in both fusiform aneurysms and arteriovenous fistulas. The proposed image-based CFD methodology can evaluate postoperative values of relevant hemodynamic descriptors and thus identify early indicators of a likely fistula failure, or flag as unsuitable, treatments of fusiform aneurysms that could lead to negative developments such as thrombotic occlusion of a vital perforator. It is expected that this could help in selecting appropriate treatment options and thus increase the number of favorable outcomes. UCSF/VASF has international leaders in vascular surgery, radiology and biomedical research. The full array of clinical and research facilities at UCSF/VA will be available for the proposed research studies. The team assembled to work on this project has a long history of successful and productive collaboration. The vascular/neurovascular surgeons will identify candidate subjects from patients scheduled for treatment by one of the interventional procedures specified above. The CFD-predicted vessel adaptations will be correlated to in vivo observations in order to fine-tune and validate our modeling methods. Once the efficacy and limitations of this methodology are established, it can be used for prospective patient-specific modeling of vascular interventions in order to provide guidance to vascular and neurovascular surgeons. Successful completion of the project will lead to a modeling tool capable of predicting a priori the impact of various treatment options on postoperative vessel remodeling, thereby permitting stratification of patients and individualized treatment.

描述（由申请人提供）：拟议的研究旨在开发一种计算工具，能够可靠地预测血管干预引起的血管重塑。在规划导致血流改变的干预措施时，临床医生通常依赖于 直觉而不是可靠的科学证据。减少这种不确定性为计算建模方法提供了令人兴奋的机会，可用于探索各种干预方案。患者特定计算流体动力学 (CFD) 建模的最新进展表明，这些方法对于这项任务来说可能已经足够成熟。然而，采用术后血流模型的一个重要挑战是缺乏能够准确预测后续血管变化的良好控制病例。此外，CFD 方法通常缺乏有关近端和远端循环中的流量的信息。我们提出了一种新颖的方法，其中这些流动边界条件将通过使用时间分辨相衬磁共振测速（4D MRV）的体内测量来获得。所提出的基于图像的 CFD 方法将根据患者具体情况应用于具有不同功能和解剖复杂性的三种类型的血管干预措施。这些包括： 为血液透析通路创建的动静脉瘘的成熟；通过闭塞一根或多根近端血管治疗的梭形脑动脉瘤；最后，通过分流器支架治疗梭形脑动脉瘤。目前，有 梭形动脉瘤和动静脉瘘的治疗失败率很高。所提出的基于图像的 CFD 方法可以评估相关血流动力学描述符的术后值，从而识别可能的瘘管失败的早期指标，或标记为不合适的梭形动脉瘤治疗，这可能导致负面发展，例如重要穿支的血栓闭塞。预计这将有助于选择适当的治疗方案，从而增加有利结果的数量。 UCSF/VASF 在血管外科、放射学和生物医学研究方面拥有国际领先水平。 UCSF/VA 的全套临床和研究设施将可用于拟议的研究。参与该项目的团队有着悠久的成功和富有成效的合作历史。血管/神经血管外科医生将从计划通过上述指定的介入手术之一进行治疗的患者中识别候选受试者。 CFD 预测的血管适应性将与体内观察相关联，以便微调和验证我们的建模方法。一旦确定了这种方法的有效性和局限性，它就可以用于血管干预的前瞻性患者特异性建模，以便为血管和神经血管外科医生提供指导。该项目的成功完成将产生一种建模工具，能够预先预测各种治疗方案对术后血管重塑的影响，从而允许对患者进行分层和个体化治疗。