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

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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.