HiResHemo: Hemodynamics at High Spatio-temporal Resolution by Comparative Visual Analysis of 4D PC-MRI Data and CFD Simulation Ensembles

• Applicants: Professorin Dr. Verena Hörr; Privatdozent Dr. Mathias Joachim Krause; Professor Dr.-Ing. Lars Linsen
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; Medical Physics, Biomedical Technology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 468824876

Advances in 4D phase-contrast magnetic resonance imaging (PC-MRI) allow for fast in-vivo measurements of unsteady blood flow in animals and humans. Still, the low spatio-temporal resolution, the low signal-to-noise ratio, and the uncertainty due to acceleration inhibit a proper analysis of the hemodynamic parameters which hamper their application as diagnostic markers in the clinical routine. Data-driven computational fluid dynamics (CFD) allow for simulated blood flow at high spatio-temporal resolution. Tuning the many simulation parameters to obtain a simulated blood flow that best matches the imaging data in an iterative procedure is a tedious task though and prone to detecting sub-optimal solutions that do not match the measured blood flow well enough. We propose to transform the task to a data analysis task by generating a simulation ensemble with many feasible parameter settings and analyzing the ensemble in comparison to the measured data. This approach requires multiple contributions: First, a fast reliable PC-MRI sequence will be developed to produce high-quality imaging data with low bias in-vitro and in-vivo in an appropriate measurement time. Second, an efficient data-driven CFD approach that allows for moving walls will be developed. Third, an effective analysis methodology for comparing measured and simulated data will be developed. To incorporate expert knowledge in the analysis process, we propose a user-centric approach that allows for interactive visual comparisons at a global level as well as in selected spatio-temporal regions of interest. Putting the three contributions together in a data assimilation process delivers a software tool to generate subject-specific blood flow fields from measured PC-MRI data at high spatio-temporal resolution. By compensating the limited spatio-temporal domains of MRI datasets it enables an accurate quantitative analysis of hemodynamic parameters. Using this software tool, we will provide a comprehensive description of the hemodynamic changes in the development and progression of atherosclerosis in a murine model.

• DFG Programme: Research Grants