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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.


Evolution of the cost function throughout 100 generations (abscissa is scaled logarithmically). Red, green and blue lines represent the evolution of nodal error, force error and the overall cost function, respectively. (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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f000065: Evolution of the cost function throughout 100 generations (abscissa is scaled logarithmically). Red, green and blue lines represent the evolution of nodal error, force error and the overall cost function, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Mentions: With the aim of reducing the differences between FM and FE and modifying the stiffness settings, we have performed multiple runs of the GA resulting in similar outcomes. As a rule, the convergence was achieved after 50 generations. The results of one typical run are reported here in detail. The very first, randomly assigned, population and the final population are captured in Table 2, together with the corresponding values of the cost function. The cost function fell from the initial best value of 275.6908 to 49.4124 during the simulation, which is displayed in Fig. 11. All individuals that were sampled during the evolution process can be seen in Fig. 12, which displays them as red crosses on the cost space (a) and as red circles on the nodal (b) and force (c) differences spaces. The evolution of the fittest individuals from each generation are plotted as a black line. The convergence towards the most optimal exemplar can be noticed by means of the increase in the concentration of red crosses and circles in the lowest interval of both parameters. In the course of the evolution, the fittest candidate emerges, defined by the pair [0.0688, 0.0874] with the , and . Note that was defined as the additional stiffness weighting that would be added to the background stiffness weight (see Eq. (3)). Thus, the overall struts stiffness weight is the sum of the two values (), whereas the weight for the background stiffness remains 0.0874.


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)

Evolution of the cost function throughout 100 generations (abscissa is scaled logarithmically). Red, green and blue lines represent the evolution of nodal error, force error and the overall cost function, respectively. (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

f000065: Evolution of the cost function throughout 100 generations (abscissa is scaled logarithmically). Red, green and blue lines represent the evolution of nodal error, force error and the overall cost function, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Mentions: With the aim of reducing the differences between FM and FE and modifying the stiffness settings, we have performed multiple runs of the GA resulting in similar outcomes. As a rule, the convergence was achieved after 50 generations. The results of one typical run are reported here in detail. The very first, randomly assigned, population and the final population are captured in Table 2, together with the corresponding values of the cost function. The cost function fell from the initial best value of 275.6908 to 49.4124 during the simulation, which is displayed in Fig. 11. All individuals that were sampled during the evolution process can be seen in Fig. 12, which displays them as red crosses on the cost space (a) and as red circles on the nodal (b) and force (c) differences spaces. The evolution of the fittest individuals from each generation are plotted as a black line. The convergence towards the most optimal exemplar can be noticed by means of the increase in the concentration of red crosses and circles in the lowest interval of both parameters. In the course of the evolution, the fittest candidate emerges, defined by the pair [0.0688, 0.0874] with the , and . Note that was defined as the additional stiffness weighting that would be added to the background stiffness weight (see Eq. (3)). Thus, the overall struts stiffness weight is the sum of the two values (), whereas the weight for the background stiffness remains 0.0874.

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.