Spranger K, Capelli C, Bosi G M, Schievano S, Ventikos Y
Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK ; Department of Mechanical Engineering, University College London, UK.
UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children, London, UK.
Comput Methods Appl Mech Eng. 2015 Aug 15;293:462-480. doi: 10.1016/j.cma.2015.03.022.
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 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.
在本文中，我们对两种用于虚拟支架展开的计算方法进行了比较分析：一种基于弹簧-质点模型的新型快速虚拟支架置入方法，在一系列实验中与详细的有限元分析进行了比较。根据初始比较结果，我们提出了一种通过借助遗传算法校准一组参数来优化快速方法的途径，该遗传算法将有限元分析的结果用作学习参考。经过校准阶段，我们能够大幅降低两种方法之间的力测量差异，并通过评估最终装置配置的差异来验证快速支架置入方法。
