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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.


Resulting configurations after deployment in curved vessel geometries. The columns correspond to (a) final device configuration inside the vessel obtained with FE, (b) FE reaction forces, (c) final configuration inside the vessel obtained with FM and (d) FM final configuration displayed in red overlaid with the FE result in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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f000055: Resulting configurations after deployment in curved vessel geometries. The columns correspond to (a) final device configuration inside the vessel obtained with FE, (b) FE reaction forces, (c) final configuration inside the vessel obtained with FM and (d) FM final configuration displayed in red overlaid with the FE result in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Mentions: The detailed results of all six simulations are reported in Fig. 8, Fig. 9, which illustrate the final deployed configurations for both numerical methods. The visual comparison enables the qualitative assessment of the differences of the two methods by means of overlaying the final deployed configurations of the devices (last column FM+FE). The visual comparison showed an overall good agreement in terms of device positioning. However, in the cases where the curvature is higher (i.e., curved C-shaped vessel and curved W-shaped vessel), it is possible to observe a larger difference in the arrangement of the extremities of the stent. Importantly, it is possible to observe that such differences seem to be associated with a different grade of vessel curvature rather than with a different opening size.


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)

Resulting configurations after deployment in curved vessel geometries. The columns correspond to (a) final device configuration inside the vessel obtained with FE, (b) FE reaction forces, (c) final configuration inside the vessel obtained with FM and (d) FM final configuration displayed in red overlaid with the FE result in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f000055: Resulting configurations after deployment in curved vessel geometries. The columns correspond to (a) final device configuration inside the vessel obtained with FE, (b) FE reaction forces, (c) final configuration inside the vessel obtained with FM and (d) FM final configuration displayed in red overlaid with the FE result in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Mentions: The detailed results of all six simulations are reported in Fig. 8, Fig. 9, which illustrate the final deployed configurations for both numerical methods. The visual comparison enables the qualitative assessment of the differences of the two methods by means of overlaying the final deployed configurations of the devices (last column FM+FE). The visual comparison showed an overall good agreement in terms of device positioning. However, in the cases where the curvature is higher (i.e., curved C-shaped vessel and curved W-shaped vessel), it is possible to observe a larger difference in the arrangement of the extremities of the stent. Importantly, it is possible to observe that such differences seem to be associated with a different grade of vessel curvature rather than with a different opening size.

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.