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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.


Crimping and initial positioning of the stent graft with FE method. (a) The stent before crimping.  (in red) is the centreline of the vessel defined by 11 points.  (in yellow) is the centreline of the stent and the coaxial cylinder. (b) After the cylindrical surface has crimped the stent, displacements and rotations are applied to the points of  to make them reach the corresponding points of  and to position the stent inside the vessel (c). (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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f000010: Crimping and initial positioning of the stent graft with FE method. (a) The stent before crimping. (in red) is the centreline of the vessel defined by 11 points. (in yellow) is the centreline of the stent and the coaxial cylinder. (b) After the cylindrical surface has crimped the stent, displacements and rotations are applied to the points of to make them reach the corresponding points of and to position the stent inside the vessel (c). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Mentions: Crimping. The device was crimped to the size of the delivery catheter (8 mm diameter) by applying a radial displacement (15 mm) to a coaxial cylindrical surface. Such cylindrical surface mimicked the presence of the membrane sheath that constrains the stent into the delivery catheter (see Fig. 2(a)). Specific boundary conditions were assigned to the stents, in order to avoid rigid translation during the analysis: the device was constrained in its terminal section in both circumferential and axial direction (see Fig. 2(b)).


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)

Crimping and initial positioning of the stent graft with FE method. (a) The stent before crimping.  (in red) is the centreline of the vessel defined by 11 points.  (in yellow) is the centreline of the stent and the coaxial cylinder. (b) After the cylindrical surface has crimped the stent, displacements and rotations are applied to the points of  to make them reach the corresponding points of  and to position the stent inside the vessel (c). (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

f000010: Crimping and initial positioning of the stent graft with FE method. (a) The stent before crimping. (in red) is the centreline of the vessel defined by 11 points. (in yellow) is the centreline of the stent and the coaxial cylinder. (b) After the cylindrical surface has crimped the stent, displacements and rotations are applied to the points of to make them reach the corresponding points of and to position the stent inside the vessel (c). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Mentions: Crimping. The device was crimped to the size of the delivery catheter (8 mm diameter) by applying a radial displacement (15 mm) to a coaxial cylindrical surface. Such cylindrical surface mimicked the presence of the membrane sheath that constrains the stent into the delivery catheter (see Fig. 2(a)). Specific boundary conditions were assigned to the stents, in order to avoid rigid translation during the analysis: the device was constrained in its terminal section in both circumferential and axial direction (see Fig. 2(b)).

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.