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，该工具可以可靠地预测由血管干预引起的血管重塑。在导致流动变化的计划干预措施中，临床医生经常依靠 直觉而不是扎实的科学证据。降低这种不确定性为计算建模方法提供了一个令人兴奋的机会，该方法可用于探索各种介入选项。特定于患者的计算流体动力学（CFD）建模的最新进展表明，这些方法现在可能已经足够成熟。然而，采用术后流动建模的重要挑战是缺乏控制良好的情况，在这些情况下，已经证明了对随后的血管变化的准确预测。此外，CFD方法通常缺乏有关近端和远端循环中流动的信息。我们提出了一种新的方法，其中将使用时间分辨的相位对比度MR Velocimetry（4D MRV）在体内测量中获得这些流量边界条件。提出的基于图像的CFD方法将以患者特定的基础应用于具有不同功能和解剖复杂性不同的三种类型的血管干预措施。其中包括：为血液透析访问而创建的动静脉瘘的成熟；通过闭塞一个或多个近端血管治疗的螺和脑动脉瘤；最后，由流动剂支架处理的螺和脑动脉瘤。目前，那里 是梭形动脉瘤和动静脉瘘的高度治疗结果的高发病率。提出的基于图像的CFD方法可以评估相关血液动力学描述符的术后值，从而确定可能导致诸如重要的plotorator的血栓性闭塞的梭形动脉瘤的治疗方法。预计这可能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具有国际领导者。 UCSF/VA的各种临床和研究设施将用于拟议的研究。团队集会从事该项目的工作，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通过上述介入程序之一从计划治疗的患者中识别出候选受试者。为了微调和验证我们的建模方法，CFD预测的血管适应将与体内观测值相关。一旦建立了这种方法的功效和局限性，它可用于对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师提供指导。成功完成该项目将导致一个建模工具，能够先验预测各种治疗方案对术后重塑的影响，从而允许对患者进行分层和个性化治疗。