Cardiovascular diseases are overwhelming the healthcare systems, and the costs are anticipated to increase in the years to come (Murray & Lopez, 1997), not to the mention the personal tragedy for those affected (Gage, Cardinalli, & Owens, 1996). Systemic risk factors are well known to correlate with cardiovascular diseases in general, but, for instance, arterial plaques and brain aneurysms are focalized, highlighting the role of local hemodynamics. Furthermore, blood-flow induced wall shear stress (WSS) is known to contribute to vessel wall adaption and remodeling (A. M. Malek, Alper, & Izumo, 1999; Morbiducci et al., 2016), but is challenging to measure in-vivo. On the other hand, medical images are routinely available and have been extensively used in combination with computational fluid dynamics to study the initiation, progression, and outcome of vascular pathologies (Taylor & Steinman, 2010).
Patient-specific medical image-based computational fluid dynamics has been widely used to reveal fundamental insight into mechanisms of cardiovascular disease, for instance, correlating morphology to adverse vascular remodeling.However, segmentation of medical images is laborious, error-prone, and a bottleneck in the development of large databases that are needed to capture the natural variability in morphology. Instead, idealized models, where morphological features are parameterized, have been used to investigate the correlation with flow features, but at the cost of limited understanding of the complexity of cardiovascular flows. To combine the advantages of both approaches, we developed a tool that preserves the patient-specificness inherent in medical images while allowing for parametric alteration of the morphology. In our opensource framework morphMan we convert the segmented surface to a Voronoi diagram, modify the diagram to change the morphological features of interest, and then convert back to a new surface. In this paper, we present algorithms for modifying bifurcation angles, location of branches, cross-sectional area, vessel curvature, shape of bends, and surface roughness. We show qualitative and quantitative validation of the algorithms, performing with an accuracy exceeding 97% in general, and proof-of-concept on combining the tool with computational fluid dynamics. By combining morphMan with appropriate clinical measurements, one could explore the morphological parameter space and resulting hemodynamic response using only a handful of segmented surfaces, effectively minimizing the main bottleneck in image-based computational fluid dynamics. K E Y W O R D S
In medical research, it has become increasingly common to use image-based computational fluid dynamics (CFD) to study vascular pathology. Hemodynamic forces, such as wall shear stress (WSS), are believed to play a crucial role in vessel wall adaptation and remodeling. However, measuring these forces directly is challenging due to limitations in current measurement techniques. Additionally, there is significant variability in CFD modeling choices and simulation results, which can make it difficult to compare and interpret findings across studies. To address this, we aim to create an automated CFD pipeline for modeling cardiovascular flows that is objective and consistent, where modeling choices are backed up by rigorous research. The Vascular Modeling Pypeline (VaMPy) is an entry-level high-performance CFD pipeline with a high-level Python interface that lets the user easily extend or modify the functionality.
Computational fluid dynamics (CFD) in combination with patient-specific medical images has been used to correlate flow phenotypes with disease initiation, progression and outcome, in search of a prospective clinical tool. A large number of CFD software packages are available, but are typically based on rigid domains and low-order finite volume methods, and are often implemented in massive low-level C++ libraries. Furthermore, only a handful of solvers have been appropriately verified and validated for their intended use. Our goal was to develop, verify and validate an open-source CFD solver for moving domains, with applications to cardiovascular flows. The solver is an extension of the CFD solver Oasis, which is based on the finite element method and implemented using the FEniCS open source framework. The new solver, named OasisMove, extends Oasis by expressing the Navier-Stokes equations in the arbitrary Lagrangian-Eulerian formulation, which is suitable for handling moving domains. For code verification we used the method of manufactured solutions for a moving 2D vortex problem, and for validation we compared our results against existing high-resolution simulations and laboratory experiments for two moving domain problems of varying complexity. Verification results showed that the L 2 error followed the theoretical convergence rates. The temporal accuracy was second-order, while the spatial accuracy was second-and third-order using P 1 =P 1 and P 2 =P 1 finite elements, respectively.Validation results showed good agreement with existing benchmark results, by reproducing lift and drag coefficients with less than 1% error, and demonstrating the solver's ability to capture vortex patterns in transitional and turbulentlike flow regimes. In conclusion, we have shown that OasisMove is an opensource, accurate and reliable solver for cardiovascular flows in moving domains.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.