A computational approach combining 4D Flow and CFD for improved determination of cerebral hemodynamics

结合 4D Flow 和 CFD 的计算方法可改进脑血流动力学测定

• 批准号: 10581283
• 负责人: Yan Wang
• 金额: $ 12.11万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-13 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10581283
• 关键词: 3-Dimensional; 4D MRI; Address; Blood Vessels; Caliber; Cardiac; Cerebrum; Clinical; Complex; Computational Geometry; Computer Models; Data; Data Set; Decision Making; Descriptor; Development; Diastole; Disease Progression; Evaluation; Evolution; Experimental Models; Funding; Generations; Geometry; Growth; Image; Intervention; Intracranial Aneurysm; Learning; Liquid substance; Magnetic Resonance Imaging; Manuals; Maps; Measures; Medical; Methods; Mission; Modeling; Motion; National Heart, Lung, and Blood Institute; Noise; Operative Surgical Procedures; Patient observation; Patient-Focused Outcomes; Patients; Phase; Process; Pulsatile Tinnitus; Reference Standards; Research; Resolution; Rupture; Sampling; Structure; System; Systole; Technology; Time; Training; Vascular Diseases; Veins; Venous; automated analysis; automated segmentation; cerebral hemodynamics; contrast enhanced; deep learning; hemodynamics; human data; improved; in vivo; intracranial artery; learning network; neural network; novel; predict clinical outcome; predictive marker; programs; secondary analysis; simulation; tool; treatment response; ultra high resolution; virtual

PROJECT SUMMARY This project aims to generate super-resolution time-resolved phase-contrast MRI (4D flow MRI) for improved quantification of cerebral hemodynamics. Our team has collected extensive in vivo 4D Flow data in several other funded projects. That data was processed with conventional manual methods and was limited by the relatively modest acquisition resolution. We have data of three levels of complexity. The first is extremely high-quality data in very carefully controlled flow models created with patient-specific geometries and flow conditions. Second, is in vivo data from the intracranial venous outflow tract, collected from 58 patients with pulsatile tinnitus (PT). In these territories, the flow has relatively little pulsatility but is geometrically complex with pronounced vorticity. Finally, we have 4D Flow data from 148 patients with intracranial aneurysms (IA). Using these existing datasets, we propose an incremental progression to develop advanced methods for improving 4D flow resolution. There is compelling evidence that hemodynamic parameters are of major importance in determining the evolution of vascular disease progression, and response to therapy. In principle, 4D Flow MRI can be used to determine the velocity field in three-dimensions and through the cardiac cycle. However, using acceptable acquisition times, the resolution is insufficient for reliable velocity mapping given the small caliber of the intracranial vessels. Patient-specific computational and experimental models can provide superior resolution, but their accuracy depends on modeling simplifications and assumptions. We propose to address the current limitations of 4D flow images by developing a deep learning based, super-resolution approach. In this approach, the flow in cerebral vessels will be imaged with 4D Flow MRI and simulated with patient-specific Computational Fluid Dynamics (CFD). In this study, we will first generate 3D CFD simulation of hemodynamics in patient-specific data. Then, we will develop a super-resolution neural network using CFD data to provide higher resolution 4D flow data. Successful accomplishment of this project will provide an evaluation tool that is validated for improved quantification of cerebral hemodynamics. A tool such as this could be used to stage interventional treatments and improve patient outcomes, in direct support of the National Heart, Lung, and Blood Institute mission.

项目摘要 该项目旨在生成超分辨率的时间分辨相对比MRI（4D Flow MRI）以进行改进 脑血动力学的定量。我们的团队已在其他几个中收集了大量的体内4D流数据 资助的项目。该数据是使用常规手动方法处理的，并受到相对限制 适度的采集解决。我们拥有三个复杂级别的数据。第一个是极高的数据 在非常仔细控制的流动模型中，由患者特异性的几何形状和流动条件产生。第二，是 颅内静脉流出道的体内数据，从58例搏动性耳鸣患者（PT）收集。在 在这些领土上，流动的脉动性相对较小，但几何复杂，具有明显的涡度。 最后，我们有来自148例颅内动脉瘤（IA）患者的4D流数据。使用这些现有数据集， 我们提出了一个增量进程，以开发提高4D流量分辨率的高级方法。 有令人信服的证据表明，血液动力学参数在确定 血管疾病进展的进化和对治疗的反应。原则上，4D流MRI可用于 在三维和心脏周期中确定速度场。但是，使用可接受的 获取时间，鉴于较小的口径，该分辨率不足以用于可靠的速度映射 颅内血管。特定于患者的计算和实验模型可以提供卓越的分辨率，但 它们的准确性取决于建模简化和假设。我们建议解决当前 通过开发基于深度学习的超分辨率方法来限制4D流图像。在这种方法中， 脑血管中的流将使用4D流MRI成像，并使用患者特定的计算模拟 流体动力学（CFD）。 在这项研究中，我们将首先生成患者特异性数据中血液动力学的3D CFD模拟。然后，我们会的 使用CFD数据开发超分辨率神经网络，以提供更高的分辨率4D流数据。成功的 该项目的完成将提供一个评估工具，该工具已得到验证，以改善量化的量化 脑血液动力学。这样的工具可用于进行介入和改善患者 成果直接支持国家心脏，肺和血液研究所任务。