A toolbox for generating scalable mitral valve morphometric models

Oliveira, Diana C. de; Espino, Daniel M.; Deorsola, Luca; Mynard, Jonathan P.; Rajagopal, Vijay; Buchan, Keith; Dawson, Dana; Shepherd, Duncan E. T.

doi:10.1016/j.compbiomed.2021.104628

Cited by 2 publications

(1 citation statement)

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“…FEA has been used to facilitate the evaluation of experimental conditions, where analysis of the mechanics cannot be obtained either reliably or in a cost-effective manner. For example, toolboxes that generate models for FEA of the mitral heart valve [ 15 ] or the lumbar spine [ 16 ] enable the effect of geometric variables to be evaluated. While techniques to tackle anatomical variability and commercial software for structural optimization, e.g.…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm optimization framework for the characterization of hyper-viscoelastic materials: application to human articular cartilage

Allen,

Cox,

Jones

et al. 2024

R. Soc. Open Sci.

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This study aims to develop an automated framework for the characterization of materials which are both hyper-elastic and viscoelastic. This has been evaluated using human articular cartilage (AC). AC (26 tissue samples from 5 femoral heads) underwent dynamic mechanical analysis with a frequency sweep from 1 to 90 Hz. The conversion from a frequency- to time-domain hyper-viscoelastic material model was approximated using a modular framework design where finite element analysis was automated, and a genetic algorithm and interior point technique were employed to solve and optimize the material approximations. Three orders of approximation for the Prony series were evaluated at N = 1, 3 and 5 for 20 and 50 iterations of a genetic cycle. This was repeated for 30 simulations of six combinations of the above all with randomly generated initialization points. There was a difference between N = 1 and N = 3/5 of approximately ~5% in terms of the error estimated. During unloading the opposite was seen with a 10% error difference between N = 5 and 1. A reduction of ~1% parameter error was found when the number of generations increased from 20 to 50. In conclusion, the framework has proved effective in characterizing human AC.

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Section: Introductionmentioning

confidence: 99%