Abstract
Severe arterial stenosis encompasses complex flow structures especially when the blood flow rate exceeds the critical Reynolds number (Re ≥ 2000), resulting in ow instability and turbulence. Uncovering reduced-order ow characteristics in blood flow data facilitate understanding flow physics and efficient data-driven modeling. In this paper, we used Computational Fluid Dynamics (CFD) and 4D flow MRI data in a phantom model of arterial stenosis with 87% degree of narrowing for performing Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) on the velocity and pressure data. We found the required modes to reconstruct the CFD and 4D flow MRI velocity and pressure data in the phantom model and identified the most energetic modes with temporal dynamics of coherent structures. In addition, we evaluated the compromise between the simplicity and accuracy of the reconstructed data. These data-driven modeling techniques have the potential to reduce the complexity of 4D flow MRI data. We envisage that it can ultimately be applied to enhancing the resolution, denoising 4D flow MRI data, and impacting data collection requirements.
Published Version
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