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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.


Related in: MedlinePlus

First row: deployment of the stent graft in a bent cylinder with FM. Results after (a) crimping and initial positioning along the centreline, (b) after 20, (c) 30, (d) 100, (e) 200 iterations of FM method. Second row: placement of a stent graft over a dissection entry. Result after (f) 1, (g) 20, (h) 50, (i) 80, (j) 150 iterations of FM expansion.
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f000045: First row: deployment of the stent graft in a bent cylinder with FM. Results after (a) crimping and initial positioning along the centreline, (b) after 20, (c) 30, (d) 100, (e) 200 iterations of FM method. Second row: placement of a stent graft over a dissection entry. Result after (f) 1, (g) 20, (h) 50, (i) 80, (j) 150 iterations of FM expansion.

Mentions: To assess the differences between the FM and FE methods, the stent graft configurations obtained with both methods were compared in six different deployment scenarios. Both methods are able to realistically capture the self-expansion of the stent graft until it reaches equilibrium in contact with the arterial wall. The process of expansion is visualised in Fig. 7 which displays two examples of different complexity. In order to mimic clinical practice, the deployment of the device was either initiated at the centre and progressed to both extremities (first row) or was started at the one end and progressed towards the other extremity (second row), as common for this type of stent grafts. In the case of FM, the final configuration was obtained after approximately 200 iterations of the virtual deployment algorithm and required around 20 s. The device was in a reasonably good opposition to the vessel wall and covered the initial tear completely. In both FM and FE simulations, the stent graft successfully covered the virtual vasculature lesion. These results confirmed the possibility to use both the techniques also in case of complex anatomical cases.


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)

First row: deployment of the stent graft in a bent cylinder with FM. Results after (a) crimping and initial positioning along the centreline, (b) after 20, (c) 30, (d) 100, (e) 200 iterations of FM method. Second row: placement of a stent graft over a dissection entry. Result after (f) 1, (g) 20, (h) 50, (i) 80, (j) 150 iterations of FM expansion.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f000045: First row: deployment of the stent graft in a bent cylinder with FM. Results after (a) crimping and initial positioning along the centreline, (b) after 20, (c) 30, (d) 100, (e) 200 iterations of FM method. Second row: placement of a stent graft over a dissection entry. Result after (f) 1, (g) 20, (h) 50, (i) 80, (j) 150 iterations of FM expansion.
Mentions: To assess the differences between the FM and FE methods, the stent graft configurations obtained with both methods were compared in six different deployment scenarios. Both methods are able to realistically capture the self-expansion of the stent graft until it reaches equilibrium in contact with the arterial wall. The process of expansion is visualised in Fig. 7 which displays two examples of different complexity. In order to mimic clinical practice, the deployment of the device was either initiated at the centre and progressed to both extremities (first row) or was started at the one end and progressed towards the other extremity (second row), as common for this type of stent grafts. In the case of FM, the final configuration was obtained after approximately 200 iterations of the virtual deployment algorithm and required around 20 s. The device was in a reasonably good opposition to the vessel wall and covered the initial tear completely. In both FM and FE simulations, the stent graft successfully covered the virtual vasculature lesion. These results confirmed the possibility to use both the techniques also in case of complex anatomical cases.

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.


Related in: MedlinePlus