Simulation of oxygen transfer in stented arteries and correlation with in‐stent restenosis

Caputo, Massimo; Chiastra, Claudio; Cianciolo, C.; Cutrì, Elena; Dubini, Gabriele; Gunn, Julian; Keller, Brandis; Migliavacca, Francesco; Zunino, Paolo

doi:10.1002/cnm.2588

Cited by 33 publications

(24 citation statements)

References 46 publications

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“…Additionally, the vessel geometry was modified to reflect the curved shape of in vivo coronary vessels. In these vessels curvature results in a significant flow helicity,7 which in turn affects the local WSS. Since WSS indirectly controls SMC proliferation and is an important parameter in the model,40 the curvature was added to the model geometry.…”

Section: Methodsmentioning

confidence: 99%

Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments

Zun

Narracott

Chiastra

et al. 2019

Cardiovasc Eng Tech

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BackgroundCoronary artery restenosis is an important side effect of percutaneous coronary intervention. Computational models can be used to better understand this process. We report on an approach for validation of an in silico 3D model of in-stent restenosis in porcine coronary arteries and illustrate this approach by comparing the modelling results to in vivo data for 14 and 28 days post-stenting.MethodsThis multiscale model includes single-scale models for stent deployment, blood flow and tissue growth in the stented vessel, including smooth muscle cell (SMC) proliferation and extracellular matrix (ECM) production. The validation procedure uses data from porcine in vivo experiments, by simulating stent deployment using stent geometry obtained from micro computed tomography (micro-CT) of the stented vessel and directly comparing the simulation results of neointimal growth to histological sections taken at the same locations.ResultsMetrics for comparison are per-strut neointimal thickness and per-section neointimal area. The neointimal area predicted by the model demonstrates a good agreement with the detailed experimental data. For 14 days post-stenting the relative neointimal area, averaged over all vessel sections considered, was 20 ± 3% in vivo and 22 ± 4% in silico. For 28 days, the area was 42 ± 3% in vivo and 41 ± 3% in silico.ConclusionsThe approach presented here provides a very detailed, location-specific, validation methodology for in silico restenosis models. The model was able to closely match both histology datasets with a single set of parameters. Good agreement was obtained for both the overall amount of neointima produced and the local distribution. It should be noted that including vessel curvature and ECM production in the model was paramount to obtain a good agreement with the experimental data.Electronic supplementary materialThe online version of this article (10.1007/s13239-019-00431-4) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments

Zun

Narracott

Chiastra

et al. 2019

Cardiovasc Eng Tech

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“…Concerning the diffusivity of oxygen in the blood stream,

D_{v}^{ox}

, the value of Table is significantly larger than the usual diffusivity for oxygen transport in arteries. Indeed, in , the authors and coworkers have used

D_{v}^{ox} ≃ 1 0^{- 5}

cm 2 /s. Diffusion of oxygen was on purpose augmented in the present work in order to compensate for the role of red blood cells and in particular for the additional oxygen available because of the exchange of dissolved gas and hemoglobin‐bound oxygen.…”

Section: Methodsmentioning

confidence: 99%

A computational model of drug delivery through microcirculation to compare different tumor treatments

Cattaneo

Zunino

2014

Numer Methods Biomed Eng

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Starting from the fundamental laws of filtration and transport in biological tissues, we develop a computational model to capture the interplay between blood perfusion, fluid exchange with the interstitial volume, mass transport in the capillary bed, through the capillary walls and into the surrounding tissue. These phenomena are accounted at the microscale level, where capillaries and interstitial volume are viewed as two separate regions. The capillaries are described as a network of vessels carrying blood flow. We apply the model to study drug delivery to tumors. The model can be adapted to compare various treatment options. In particular, we consider delivery using drug bolus injection and nanoparticle injection into the blood stream. The computational approach is suitable for a systematic quantification of the treatment performance, enabling the analysis of interstitial drug concentration levels, metabolization rates and cell surviving fractions. Our study suggests that for the treatment based on bolus injection, the drug dose is not optimally delivered to the tumor interstitial volume. Using nanoparticles as intermediate drug carriers overrides the shortcomings of the previous delivery approach. This work shows that the proposed theoretical and computational framework represents a promising tool to compare the efficacy of different cancer treatments.

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“…This problem has been largely addressed and studied in the literature, see e.g. [12,44] and the references therein for an extensive description.…”

Section: Problem Settingmentioning

confidence: 99%

Multi Space Reduced Basis Preconditioners for Large-Scale Parametrized PDEs

Santo¹,

Deparis²,

Manzoni³

et al. 2018

SIAM J. Sci. Comput.

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In this work we introduce a new two-level preconditioner for the ecient solution of large scale linear systems arising from the discretization of parametrized PDEs. The proposed preconditioner combines in a multiplicative way a reduced basis solver, which plays the role of coarse component, and a "traditional" ne grid preconditioner, such as one-level Additive Schwarz, block Gauss-Seidel or block Jacobi preconditioners. The coarse component is built upon a new Multi Space Reduced Basis (MSRB) method that we introduce for the rst time in this paper, where a reduced basis space is built through the proper orthogonal decomposition (POD) algorithm at each step of the iterative method at hand, like the exible GMRES method. MSRB strategy consists in building reduced basis (RB) spaces that are wellsuited to perform a single iteration, by addressing the error components which have not been treated yet. The Krylov iterations employed to solve the resulting preconditioned system targets small tolerances with a very small iteration count and in a very short time, showing good optimality and scalability properties. Simulations are carried out to evaluate the performance of the proposed preconditioner in dierent large scale computational settings related to parametrized advection diusion equations and compared with the current state of the art algebraic multigrid preconditioners.

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Simulation of oxygen transfer in stented arteries and correlation with in‐stent restenosis

Cited by 33 publications

References 46 publications

Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments

Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments

A computational model of drug delivery through microcirculation to compare different tumor treatments

Multi Space Reduced Basis Preconditioners for Large-Scale Parametrized PDEs

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