期刊
ANNALS OF BIOMEDICAL ENGINEERING
卷 50, 期 8, 页码 1001-1016出版社
SPRINGER
DOI: 10.1007/s10439-022-02980-7
关键词
Computational fluid dynamics; 4D flow MRI; Adaptive mesh refinement; Congenital heart disease; Patient-specific
资金
- American Heart Association [19TPA34850066]
- GE Healthcare
- UW-Madison
- Advanced Computing Initiative
- Wisconsin Alumni Research Foundation
- Wisconsin Institutes for Discovery
- National Science Foundation
- U.S. Department of Energy's Office of Science
4D Flow MRI is a diagnostic tool that visualizes and quantifies patient-specific hemodynamics, aiding in optimizing treatment strategies for repairing COA. However, there are limitations in 4D Flow MRI, such as phase-offset errors, limited spatiotemporal resolution, aliasing, inaccuracies in slow aneurysmal flow, and distortion of images from metallic artifacts. To address these limitations, a CFD framework with AMR was developed to enhance the visualization and quantification of 4D Flow MRI. The framework was applied to five pediatric patients with COA, showing a close agreement between CFD flow rates and 4D Flow MRI. CFD also simulated flow conditions that were not captured by MRI, and AMR improved the spatial and temporal resolution. This computational framework has the potential to optimize the visualization and quantification of aneurysmal and in-stent flows and evaluate post-COA treatment hemodynamics.
4D Flow MRI is a diagnostic tool that can visualize and quantify patient-specific hemodynamics and help interventionalists optimize treatment strategies for repairing coarctation of the aorta (COA). Despite recent developments in 4D Flow MRI, shortcomings include phase-offset errors, limited spatiotemporal resolution, aliasing, inaccuracies due to slow aneurysmal flows, and distortion of images due to metallic artifact from vascular stents. To address these limitations, we developed a framework utilizing Computational Fluid Dynamics (CFD) with Adaptive Mesh Refinement (AMR) that enhances 4D Flow MRI visualization/quantification. We applied this framework to five pediatric patients with COA, providing in-vivo and in-silico datasets, pre- and post-intervention. These two data sets were compared and showed that CFD flow rates were within 9.6% of 4D Flow MRI, which is within a clinically acceptable range. CFD simulated slow aneurysmal flow, which MRI failed to capture due to high relative velocity encoding (V-enc). CFD successfully predicted in-stent blood flow, which was not visible in the in-vivo data due to susceptibility artifact. AMR improved spatial resolution by factors of 10(1) to 10(3) and temporal resolution four-fold. This computational framework has strong potential to optimize visualization/quantification of aneurysmal and in-stent flows, improve spatiotemporal resolution, and assess hemodynamic efficiency post-COA treatment.
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