CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation

Arthurs, Christopher J.; Khlebnikov, Rostislav; Melville, Alex; Marčan, Marija; Gómez, Alberto; Dillon-Murphy, Desmond; Cuomo, Federica; Vieira, Miguel Silva; Schollenberger, Jonas; Lynch, Sabrina R.; Tossas-Betancourt, Christopher; Iyer, Kritika; Hopper, Sara; Livingston, Elizabeth; Youssefi, Pouya; Noorani, Alia; Ahmed, Sabrina Ben; Nauta, Foeke J.H.; Bakel, Theodorus M. J. van; Ahmed, Yunus; Bakel, Petrus Antonius Jacobus van; Mynard, Jonathan P.; Achille, Paolo Di; Gharahi, Hamidreza; Lau, K C; Filonova, Vasilina; Aguirre, Miquel; Nama, Nitesh; Xiao, Nan; Baek, Seungik; Garikipati, Krishna; Sahni, Onkar; Nordsletten, David; Figueroa, C. Alberto

doi:10.1101/2020.10.14.339960

Cited by 13 publications

(13 citation statements)

References 53 publications

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“…CFD modeling and analysis were performed using the validated open-source framework CRIMSON. 44 3D models were meshed using tetrahedral elements. The incompressible Navier-Stokes equations solved iteratively for velocity and pressure using a stabilized finite-element formulation.…”

Section: In Silico Validation Of Cerebrovascular Relative Pressure Es...mentioning

confidence: 99%

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Marlevi

Schollenberger

Aristova

et al. 2021

Magnetic Resonance in Med

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Purpose Hemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image‐based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image‐based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full‐field virtual work‐energy relative pressure (νWERP) method. Methods Patient‐specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and νWERP performance quantified as a function of spatiotemporal sampling and image noise. Cerebrovascular relative pressures were also derived in 4D Flow MRI from healthy volunteers (n=8), acquired at two spatial resolutions (dx = 1.1 and 0.8 mm). Results The in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm νWERP recovers relative pressures at a mean error of 0.02 ± 0.25 mm Hg, while errors remain higher for RB and UB (mean error of −2.18 ± 1.91 and −2.18 ± 1.87 mm Hg, respectively). The dependence on spatial sampling is also indicated in vivo, albeit with higher correlative dependence between resolutions using νWERP (k = 0.64, R2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB (k = 0.04, R2 = 0.03, and k = 0.07, R2 = 0.07, respectively). Conclusion Image‐based full‐field methods such as νWERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.

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Section: In Silico Validation Of Cerebrovascular Relative Pressure Es...mentioning

confidence: 99%

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Marlevi

Schollenberger

Aristova

et al. 2021

Magnetic Resonance in Med

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“…While this coupling approach was inspired by Womersley's derivation of an analytical solution for axisymmetric flow in an elastic pipe [26,27], we believe this assumption of a fictitious body force is unnecessary. Since its introduction, CMM has been implemented in the open-source blood flow simulation software packages SimVascular [28,29] and CRIMSON [30] and extensively used in clinical applications ranging from interventions for coronary artery disease [31,32,33] and aortic coarctation [34] to single-ventricle physiology [35], and Alagille syndrome [36,37]. It has also been validated against experimental measurements from compliant in vitro phantom models [38,39] and Womersley's analytical solution for axisymmetric flow in a thin, linear elastic pipe subject to an oscillating pressure gradient [40,41].…”

Section: Introductionmentioning

confidence: 99%

A reduced unified continuum formulation for vascular fluid-structure interaction

Lan,

Liu,

Yang

et al. 2021

Preprint

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We recently derived the unified continuum and variational multiscale formulation for fluid-structure interaction (FSI) using the Gibbs free energy as the thermodynamic potential. Restricting our attention to vascular FSI, we now reduce this formulation in arbitrary Lagrangian-Eulerian (ALE) coordinates by adopting three common modeling assumptions for the vascular wall. The resulting semi-discrete formulation, referred to as the reduced unified continuum formulation, achieves monolithic coupling of the FSI system in the Eulerian frame through a simple modification of the fluid boundary integral. While ostensibly similar to the semi-discrete formulation of the coupled momentum method introduced by Figueroa et al., its underlying derivation does not rely on an assumption of a fictitious body force in the elastodynamics sub-problem and therefore represents a direct simplification of the ALE method. Furthermore, uniform temporal discretization of the entire FSI system is performed via the generalized-α scheme. In contrast to the predominant approach yielding only first-order accuracy for pressure, we collocate both pressure and velocity at the intermediate time step to achieve uniform second-order temporal accuracy. In conjunction with quadratic tetrahedral elements, our methodology offers higher-order temporal and spatial accuracy for quantities of clinical interest, including pressure and wall shear stress. Furthermore, without loss of consistency, a segregated predictor multi-corrector algorithm is developed to preserve the same block structure as for the incompressible Navier-Stokes equations in the implicit solver's associated linear system. Block preconditioning of a monolithically coupled FSI system is therefore made possible for the first time. Compared to alternative preconditioners, our three-level nested block preconditioner, which achieves improved representation of the Schur complement, demonstrates robust performance over a wide range of physical parameters. We present verification of our methodology against Womersley's deformable wall theory and additionally develop practical modeling techniques for clinical applications, including tissue prestressing. We conclude with an assessment of our combined FSI technology in two patient-specific cases.

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“…Nodal velocity and pressure data were extracted after periodicity was reached (≥4 cardiac cycles). The modelling and analysis were performed using the validated open-source framework CRIMSON [21]. Further details on setup and model validation can be found in [11].…”

Section: Methodsmentioning

confidence: 99%

Cerebrovascular super-resolution 4D Flow MRI – using deep learning to non-invasively quantify velocity, flow, and relative pressure

Ferdian

Marlevi

Schollenberger

et al. 2021

Preprint

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The development of cerebrovascular disease is tightly coupled to changes in cerebrovascular hemodynamics, with altered flow and relative pressure indicative of the onset, development, and acute manifestation of pathology. Image-based monitoring of cerebrovascular hemodynamics is, however, complicated by the narrow and tortuous vasculature, where accurate output directly depends on sufficient spatial resolution. To address this, we present a method combining dedicated deep learning and state-of-the-art 4D Flow MRI to generate super-resolution full-field images with coupled quantification of relative pressure using a physics-driven image processing approach. The method is trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 12.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s at peak velocity), flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.5 ± 0.1 mL/s at peak flow), and with maintained recovery of relative pressure through the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the method is applied to an in-vivo volunteer cohort, effectively generating data at <0.5mm resolution and showing potential in reducing low-resolution bias in relative pressure estimation. Our approach presents a promising method to non-invasively quantify cerebrovascular hemodynamics, applicable to dedicated clinical cohorts in the future.

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CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation

Cited by 13 publications

References 53 publications

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

A reduced unified continuum formulation for vascular fluid-structure interaction

Cerebrovascular super-resolution 4D Flow MRI – using deep learning to non-invasively quantify velocity, flow, and relative pressure

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