Image-Based Numerical Predictions of Hemodynamics following Vascular Intervention

描述(申请人提供)：这项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导致的血管重塑。在计划导致血流改变的干预措施时，临床医生通常依赖于 直觉，而不是确凿的科学证据。减少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，这些方法可以用来探索各种干预选择。针对特定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，可以完成这项任务。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的病例。此外，CFD方法通常缺乏关于近端和远端循环中流动的信息。我们提出了一种新的方法，其中这些流动边界条件将通过使用时间分辨相衬磁共振测速仪(4D MRV)进行活体测量来获得。建议的基于图像的CFD方法将根据患者的具体情况应用于三种不同功能和解剖复杂性的血管介入治疗。这些包括：为血液透析通道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梭形脑动脉瘤；以及最后，血流分流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结果不成功的发生率很高。提出的基于图像的CFD方法可以评估相关血流动力学指标的术后价值，从而识别可能发生瘘管失败或标记为不适合的梭形动脉瘤治疗的早期指标，这些治疗可能导致负面发展，如重要穿孔的血栓闭塞。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处于国际领先地位。加州大学旧金山分校/弗吉尼亚州分校的全套临床和研究设施将可用于拟议的研究。为这个项目工作的团队有着成功和富有成效的合作的悠久历史。血管/神经血管外科医生将从计划通过上述介入程序之一进行治疗的患者中确定候选对象。CFD预测的血管适应性将与活体观察相关联，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，就可以将其用于血管干预的前瞻性患者建模，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带来一种建模工具，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.