Wall Shear Stress Measurements using Magnetic Resonance Imaging

• Applicants: Professor Dr. Herbert Egger; Professor Dr. Jürgen Hennig; Professor Dr.-Ing. Cameron Tropea
• Subject Area: Fluid Mechanics ; Mathematics ; Medical Physics, Biomedical Technology
• Term: from 2016 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 321941734

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.

• DFG Programme: Research Grants