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.

中文翻译：

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使用有限元分析和遗传算法的实时虚拟支架算法的比较和校准

在本文中，我们对虚拟支架展开的两种计算方法进行了比较分析：将一种基于弹簧质量模型的新型快速虚拟支架方法与一系列计算机实验中的详细有限元分析进行了比较。鉴于初步比较的结果，我们提出了一种通过在遗传算法的帮助下校准一组参数来优化快速方法的方法，遗传算法利用有限元分析的结果作为学习参考。作为校准阶段的结果，我们能够显着减少两种方法之间的力测量差异，并通过评估最终设备配置的差异来验证快速支架方法。