Wall Shear Stress Measurements using Magnetic Resonance Imaging

该项目的目标是通过4D流动MRI和先进的流动动力学模拟和重建来系统地评估壁面剪切应力（WSS）测量，以便建立WSS量化的真实测量技术。这些测量将为动脉粥样硬化病变和脑动脉瘤的发展提供必要的信息。该项目涉及三个学科：医学成像、流体力学和数学，每个学科都为以前测量WSS的尝试提供了补充和创新的组成部分。在医学成像方面，4D Flow MRI将与新开发的序列一起使用：加速数据采集方案将被研究，以提供更高的空间分辨率，以改善近墙描绘，高度欠采样，压缩传感技术将被利用，以实现更高的时间分辨率。这些方法将由多通道射频线圈支持，旨在改善靠近墙壁的信号质量。从流体力学的角度来看，总体策略是提供先验已知WSS的流动模型，从而能够对实验得出的WSS数据进行系统和定量的评估。该程序预见了从简单到复杂的进展，无论是在流动几何上，还是从稳定流动到振荡流动，所有这些都在适当的跨临界雷诺数范围内，就像在血管血流的医学成像中通常发现的那样。在数学上，创新在于通过变分数据同化方法增强WSS估计，将其作为线性化流模型约束的最优控制问题。将开发预条件迭代正则化方法来有效地求解逆问题，目标是在几分钟内提供稳定的计算解。该项目的独特性，以及该领域创新步骤的前景，在于将这三个学科结合起来，集中精力为临床应用奠定坚实的基础，迄今为止，临床应用基于更多的启发式方法，受到当前方法的限制。

The goal of this project is to systematically assess wall shear stress (WSS) measurements via 4D Flow MRI and advanced simulation and reconstruction of flow dynamics in order to establish ground-truth measurement techniques for WSS quantification. Such measurements will provide essential information relevant to the development of atherosclerotic lesions and cerebral aneurysms. The project involves three disciplines: Medical Imaging, Fluid Mechanics and Mathematics, each bringing complementary and innovative components to previous attempts at measuring WSS. From the medical imaging side 4D Flow MRI will be employed with newly developed sequences: accelerated data acquisition schemes will be investigated in order to provide increased spatial resolution for improved near wall delineation, and highly undersampled, compressed sensing techniques will be exploited to achieve higher temporal resolution. These approaches will be supported by multichannel RF coils, aiming at improving the signal quality close to the wall. The overall strategy from the fluid mechanics perspective is to provide flow models in which the WSS is known a priori, thus enabling a systematic and quantitative assessment of the experimentally derived WSS data. The program foresees a progression from simple to complex, both in flow geometry and also from steady flow to oscillating flow, all within an appropriate transcritical Reynolds number regime as typically found in medical imaging of blood flow in vessels. Mathematically, the innovation lies in the enhancement of the WSS estimates through variational data assimilation methods, cast as optimal control problems constrained by linearized flow models. Preconditioned iterative regularization methods will be developed to efficiently solve the inverse problem, with the goal to provide a stable computational solution within a few minutes. The uniqueness of the project, and also the promise of innovative steps in the field, lies in the combination of these three disciplines in a concentrated effort to establish a firm basis for applications in a clinical setting, which are so far based on more heuristic approaches with limitations set by the current methodologies.