A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package

Lan, Hongzhi; Updegrove, Adam; Wilson, Nathan M.; Maher, Gabriel D.; Shadden, Shawn C.; Marsden, Alison L.

doi:10.1115/1.4038751

Cited by 95 publications

(59 citation statements)

References 77 publications

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“…Using a resistive model of an epicardial stenosis (0-80% diameter reduction) in series with the coronary microcirculation at maximal vasodilation, FFR was evaluated for changes in coronary microvascular resistance (0.1-0.6 mmHg.min/ml), aortic pressure (between 70 and 130 mmHg), and coronary outflow pressure (0-15 mmHg), and it was found that the sensitivity of FFR to these hemodynamic changes was highest for stenoses of intermediate severity (23). Recent studies employ either a patient-specific lumped-parameter model of the coronary circulation (9) or applied the SimVascular Cardiovascular Modeling Package (24). Meta-analysis of FFR vs. quantitative coronary angiography and non-invasive imaging for evaluations of myocardial ischemia resulted in relatively poor concordance among outcomes (22).…”

Section: Discussionmentioning

confidence: 99%

Fractional Flow Reserve Evaluated as Metric of Coronary Stenosis — A Mathematical Model Study

Faes

Meer

Heyndrickx³

et al. 2020

Front. Cardiovasc. Med.

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Introduction: Coronary arterial stenosis may impair myocardial perfusion with myocardial ischemia and associated morbidity and mortality as result. The myocardial fractional flow reserve (FFR) is clinically used as a stenosis-specific index. Aim: This study aims to identify the relation between the FFR and the degree of coronary arterial stenosis using a simple mathematical model of the coronary circulation. Methods: A mathematical model of the coronary circulation, including an arterial stenosis of variable degree, was developed. The relation between the FFR and the degree of stenosis (defined as the fractional cross sectional area narrowing) was investigated, including the influence of the aortic and venous pressures and the capillary resistance. An additional study concerning 22 patients with coronary artery disease permits comparison of clinical data and in silico findings. Results: The FFR shows an S-shaped relationship with the stenosis index. We found a marked influence of venous and aortic pressure and capillary resistance. The FFR is accompanied by a clinically relevant co-metric (FFR C), defined by the Pythagorean sum of the two pressures in the definition formula for FFR. In the patient group the FFR C is strongly related to the post-stenotic pressure (R = 0.91). The FFR C requires establishment of a validated cutoff point using future trials. Conclusion: The S-shaped dependence of FFR on the severity of the stenosis makes the FFR a measure of the ordinal scale. The marked influences of the aortic and venous pressures and the capillary resistance on the FFR will be interpreted as significant variations in intra-and inter-individual clinical findings. These fluctuations are partly connected to the neglect of considering the FFR C. At otherwise identical conditions the FFR as measured at baseline differs from the value obtained during hyperemic conditions. This expected observation requires further investigation, as the current hyperemia based evaluation fails to take advantage of available baseline data.

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Section: Discussionmentioning

confidence: 99%

Fractional Flow Reserve Evaluated as Metric of Coronary Stenosis — A Mathematical Model Study

Faes

Meer

Heyndrickx³

et al. 2020

Front. Cardiovasc. Med.

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“…A resistance boundary condition is used at the outlet equal to R = 1600 g/cm 4 /s, which produces a mean pressure of approximately ∆P = 100 mmHg, typical of the systemic circulation of a healthy subject. Simulations are performed with rigid and deformable walls (see Figure 1), using 1,002,436 tetrahedral elements for the fluid domain and 192,668 tetrahedral elements for the wall, generated in SimVascular with the TetGen mesh generator plugin [38,19]. We measure the wall clock time on the XSEDE Comet cluster for simulations consisting of 10 time steps of 1 millisecond each, using 38 and 48 cores for rigid and deformable simulations, respectively.…”

Section: Linear Solver Performance On Pipe Flow Benchmarkmentioning

confidence: 99%

Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels

2019

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Computing the solution of linear systems of equations is invariably the most time consuming task in the numerical solutions of PDEs in many fields of computational science. In this study, we focus on the numerical simulation of cardiovascular hemodynamics with rigid and deformable walls, discretized in space and time through the variational multiscale finite element method. We focus on three approaches: the problem agnostic generalized minimum residual (GMRES) and stabilized bi-conjugate gradient (BICGS) methods, and a recently proposed, problem specific, bi-partitioned (BIPN) method. We also perform a comparative analysis of several preconditioners, including diagonal, block-diagonal, incomplete factorization, multigrid, and resistance based methods. Solver performance and matrix characteristics (diagonal dominance, symmetry, sparsity, bandwidth and spectral properties) are first examined for an idealized cylindrical geometry with physiologic boundary conditions and then successively tested on several patientspecific anatomies representative of realistic cardiovascular simulation problems. Incomplete factorization preconditioners provide the best performance and results in terms of both strong and weak scalability. The BIPN method was found to outperform other methods in patientspecific models with rigid walls. In models with deformable walls, BIPN was outperformed by BICG with diagonal and Incomplete LU preconditioners.

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“…ALE is a realistic and computationally intensive approach to solve the Navier-Stokes equations, the equations for solid mechanics, and the mesh motion equations in a monolithic fashion. We take advantage of recent developments in the SimVascular open-source software package, 37 namely the svFSI flow solver which provides an implementation of ALE FSI. The aim of the study is 3-fold.…”

Section: Introductionmentioning

confidence: 99%

The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls

Seo

Schiavazzi

Kahn

et al. 2020

Numer Methods Biomed Eng

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Cardiovascular simulations are increasingly used for noninvasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an Arbitrary‐Lagrangian‐Eulerian framework for fluid‐structure interaction, with a prescribed inlet pressure and open‐loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra‐coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi Monte Carlo sampling, stochastic collocation, and multi‐wavelet stochastic expansion. Variabilities in the quantities of interest, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. From the simulation results, we conclude that the multi‐wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.

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A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package

Cited by 95 publications

References 77 publications

Fractional Flow Reserve Evaluated as Metric of Coronary Stenosis — A Mathematical Model Study

Fractional Flow Reserve Evaluated as Metric of Coronary Stenosis — A Mathematical Model Study

Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels

The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls

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