Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model

@article{Ye2021NonintrusiveAS,
  title={Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model},
  author={Dongwei Ye and Anna Nikishova and Lourens E. Veen and Pavel S. Zun and Alfons G. Hoekstra},
  journal={Reliab. Eng. Syst. Saf.},
  year={2021},
  volume={214},
  pages={107734}
}

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References

SHOWING 1-10 OF 74 REFERENCES
Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis
TLDR
It is concluded that the semi-intrusive metamodelling method is reliable and efficient, and can be applied to such complex models as the in-stent restenosis ISR2D model.
Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis
TLDR
The quasi-Monte Carlo UQ and the Sobol sensitivity analysis are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.
Semi-intrusive uncertainty propagation for multiscale models
Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments
TLDR
An approach for validation of an in silico 3D model of in-stent restenosis in porcine coronary arteries is reported on and good agreement was obtained for both the overall amount of neointima produced and the local distribution.
A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data
TLDR
A fully-coupled 3D multiscale model of in-stent restenosis, with blood flow simulations coupled to smooth muscle cell proliferation, and results of numerical simulations performed are described and reported.
Multi-scale simulations of the dynamics of in-stent restenosis: impact of stent deployment and design
TLDR
Simulation results suggest that the growth of the restenotic lesion is strongly dependent on the stent strut cross-sectional profile, and a strong correlation between strut thickness and the rate of smooth muscle cell proliferation has been observed.
A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
TLDR
This study marks, to the authors' knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.
Modelling the Effect of a Functional Endothelium on the Development of In-Stent Restenosis
TLDR
The data indicate a positive correlation between the neointimal growths and strut deployment depths in the presence of a functional endothelium, in qualitative agreement with in-vivo data.
The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery
  • D. Evans, P. Lawford, A. Hoekstra
  • Computer Science
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2008
TLDR
This paper focuses on methodology, introduces the concept of the CxA and demonstrates its use in the generation of a multiscale model of the physical and biological processes implicated in a challenging and clinically relevant problem, namely coronary artery in-stent restenosis.
...
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2
3
4
5
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