Image-Based Numerical Predictions of Hemodynamics following Vascular Intervention

描述（由申请人提供）：拟议的研究旨在开发一种计算工具，该工具将可靠地预测血管介入治疗引起的血管重塑。在计划导致血流改变的干预时，临床医生通常依赖于 而不是科学证据。减少这种不确定性提供了一个令人兴奋的机会，计算建模方法，可用于探索各种干预措施。在患者特定的计算流体动力学（CFD）建模的最新进展表明，这些方法现在可能是足够成熟的这项任务。然而，采用术后血流建模的一个重要挑战是缺乏控制良好的情况下，随后的血管变化的准确预测已被证明。此外，CFD方法通常缺乏关于近端和远端循环中的流动的信息。我们提出了一种新的方法，这些流动边界条件将获得在体内测量使用时间分辨相衬MR测速（4D MRV）。所提出的基于图像的CFD方法将在患者特定的基础上应用于具有不同功能和解剖复杂性的三种类型的血管介入。其中包括：用于血液透析通路的动静脉瘘成熟;通过闭塞一条或多条近端血管治疗的梭形脑动脉瘤;以及最后，通过血流导向支架治疗的梭形脑动脉瘤。目前 梭形动脉瘤和动静脉瘘的治疗失败率很高。提出的基于图像的CFD方法可以评价相关血流动力学描述符的术后值，从而识别可能的瘘失败的早期指标，或标记为不适合的梭形动脉瘤治疗，可能导致负面发展，如重要交通支的血栓闭塞。预计这可能有助于选择适当的治疗方案，从而增加有利结局的数量。UCSF/VASF在血管外科，放射学和生物医学研究方面具有国际领先地位。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长期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接受上述介入手术治疗的患者中确定候选受试者。CFD预测的血管适应性将与体内观察结果相关，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，它就可以用于血管介入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，能够先验地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性化治疗。

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.