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Comparison and calibration of a real-time virtual stenting algorithm using Finite Element Analysis and Genetic Algorithms.

Spranger K, Capelli C, Bosi GM, Schievano S, Ventikos Y - Comput Methods Appl Mech Eng (2015)

Bottom Line: In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring-mass model, is compared with detailed finite element analysis in a sequence of in silico experiments.Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference.As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK ; Department of Mechanical Engineering, University College London, UK.

ABSTRACT

In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring-mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.

No MeSH data available.


(a) The convergence of the FE model in the free expansion experiment is achieved when the stent is fully deployed and the ratio kinetic energy/internal energy is below 0.05.
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f000035: (a) The convergence of the FE model in the free expansion experiment is achieved when the stent is fully deployed and the ratio kinetic energy/internal energy is below 0.05.


Comparison and calibration of a real-time virtual stenting algorithm using Finite Element Analysis and Genetic Algorithms.

Spranger K, Capelli C, Bosi GM, Schievano S, Ventikos Y - Comput Methods Appl Mech Eng (2015)

(a) The convergence of the FE model in the free expansion experiment is achieved when the stent is fully deployed and the ratio kinetic energy/internal energy is below 0.05.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643757&req=5

f000035: (a) The convergence of the FE model in the free expansion experiment is achieved when the stent is fully deployed and the ratio kinetic energy/internal energy is below 0.05.
Bottom Line: In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring-mass model, is compared with detailed finite element analysis in a sequence of in silico experiments.Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference.As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK ; Department of Mechanical Engineering, University College London, UK.

ABSTRACT

In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring-mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.

No MeSH data available.