Limits...
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.


Reaction forces measure in FE. (a) Reaction forces (RF) calculated in the vessel are represented by a colour map in the 20 mm cylindrical vessel. (b) The RF of the stent vertex , which is in contact with the vessel, is calculated summing up the four RF of the four closest vessel nodes.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4643757&req=5

f000015: Reaction forces measure in FE. (a) Reaction forces (RF) calculated in the vessel are represented by a colour map in the 20 mm cylindrical vessel. (b) The RF of the stent vertex , which is in contact with the vessel, is calculated summing up the four RF of the four closest vessel nodes.

Mentions: The distribution of residual forces in stent vertices is determined in the two different methods, FM and FE. While FM can directly output nodal forces, the residual forces (RF) in the FE method can be calculated only if the structure is constrained in all directions. For this reason, we derived the RF in the stent vertices through the reaction forces measured in each node of the vessel, which is always fully constrained. For each stent vertex in contact with the vessel, it was possible to find the 4 closest nodes of the vessel with the help of a specifically implemented automated method; these 4 nodes belong to the element of the vessel in contact with the vertex of the stent. The sum of these 4 reaction forces is equal and opposite of the RF in the stent’s vertices (see Fig. 3).


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)

Reaction forces measure in FE. (a) Reaction forces (RF) calculated in the vessel are represented by a colour map in the 20 mm cylindrical vessel. (b) The RF of the stent vertex , which is in contact with the vessel, is calculated summing up the four RF of the four closest vessel nodes.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f000015: Reaction forces measure in FE. (a) Reaction forces (RF) calculated in the vessel are represented by a colour map in the 20 mm cylindrical vessel. (b) The RF of the stent vertex , which is in contact with the vessel, is calculated summing up the four RF of the four closest vessel nodes.
Mentions: The distribution of residual forces in stent vertices is determined in the two different methods, FM and FE. While FM can directly output nodal forces, the residual forces (RF) in the FE method can be calculated only if the structure is constrained in all directions. For this reason, we derived the RF in the stent vertices through the reaction forces measured in each node of the vessel, which is always fully constrained. For each stent vertex in contact with the vessel, it was possible to find the 4 closest nodes of the vessel with the help of a specifically implemented automated method; these 4 nodes belong to the element of the vessel in contact with the vertex of the stent. The sum of these 4 reaction forces is equal and opposite of the RF in the stent’s vertices (see Fig. 3).

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.