Reconstruction and Validation of Arterial Geometries for Computational Fluid Dynamics Using Multiple Temporal Frames of 4D Flow-MRI Magnitude Images

• Published in: Cardiovascular engineering and technology 2023-10, Vol.14 (5), p.655-676
• Main Authors: Black, Scott MacDonald, Maclean, Craig, Barrientos, Pauline Hall, Ritos, Konstantinos, Kazakidi, Asimina
• Format: Article
• Language: English
• Subjects: Aorta; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedicine; Blood flow; Blood vessels; Cardiology; Computational fluid dynamics; Fluid dynamics; Fluid flow; Hemodynamics; Image contrast; Image enhancement; Image reconstruction; Image segmentation; In vivo methods and tests; Incompressible flow; Magnetic resonance imaging; Mathematical models; Medical imaging; Metric space; Original; Original Article; Reynolds averaged Navier-Stokes method; Rigid walls; Three dimensional models; Time dependence
• ISSN: 1869-408X; 1869-4098
• DOI: 10.1007/s13239-023-00679-x

Purpose Segmentation and reconstruction of arterial blood vessels is a fundamental step in the translation of computational fluid dynamics (CFD) to the clinical practice. Four-dimensional flow magnetic resonance imaging (4D Flow-MRI) can provide detailed information of blood flow but processing this information to elucidate the underlying anatomical structures is challenging. In this study, we present a novel approach to create high-contrast anatomical images from retrospective 4D Flow-MRI data. Methods For healthy and clinical cases, the 3D instantaneous velocities at multiple cardiac time steps were superimposed directly onto the 4D Flow-MRI magnitude images and combined into a single composite frame. This new Composite Phase-Contrast Magnetic Resonance Angiogram (CPC-MRA) resulted in enhanced and uniform contrast within the lumen. These images were subsequently segmented and reconstructed to generate 3D arterial models for CFD. Using the time-dependent, 3D incompressible Reynolds-averaged Navier–Stokes equations, the transient aortic haemodynamics was computed within a rigid wall model of patient geometries. Results Validation of these models against the gold standard CT-based approach showed no statistically significant inter-modality difference regarding vessel radius or curvature (p > 0.05), and a similar Dice Similarity Coefficient and Hausdorff Distance. CFD-derived near-wall hemodynamics indicated a significant inter-modality difference (p > 0.05), though these absolute errors were small. When compared to the in vivo data, CFD-derived velocities were qualitatively similar. Conclusion This proof-of-concept study demonstrated that functional 4D Flow-MRI information can be utilized to retrospectively generate anatomical information for CFD models in the absence of standard imaging datasets and intravenous contrast.