Surrogate-based multi-objective design optimization of a coronary stent: Altering geometry toward improved biomechanical performance,International Journal for Numerical Methods in Biomedical Engineering

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Surrogate-based multi-objective design optimization of a coronary stent: Altering geometry toward improved biomechanical performance
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2021-03-10 , DOI: 10.1002/cnm.3453
Nelson S Ribeiro ₁ , João Folgado ₁ , Hélder C Rodrigues ₁

Affiliation

The main objective of this study was to solve a multi-objective optimization on a representative coronary stent platform with the goal of finding new geometric designs with improved biomechanical performance. The following set of metrics, calculated via finite element models, was used to quantify stent performance: vessel injury, radial recoil, bending resistance, longitudinal resistance, radial strength and prolapse index. The multi-objective optimization problem was solved with the aid of surrogate-based algorithms; for comparison and validation purposes, four surrogate-based multi-objective optimization algorithms (EI_hv-EGO, P_hv-EGO, ParEGO and SMS-EGO) with a limited sample budget were employed and their results compared. The quality of the non-dominated solution sets outputted by each algorithm was assessed against four quality indicators: hypervolume, R2, epsilon and generational distance. Results showed that P_hv-EGO was the algorithm that exhibited the best performance in overall terms. Afterwards, the highest quality Pareto front was chosen for an in-depth analysis of the optimization results. The amount of correlation and conflict was quantified for each pair of objective functions. Next, through cluster analysis, one was able to identify families of solutions with similar performance behavior and to discuss the nature of the existent trade-offs between objectives, and the trends between design parameters and solutions in a biomechanical perspective. In the end, a constrained-based design selection was performed with the goal of finding solutions in the Pareto front with equal or better performance in all objectives against a baseline design.

中文翻译：

基于替代物的冠状动脉支架多目标设计优化：改变几何形状以提高生物力学性能

本研究的主要目的是解决代表性冠状动脉支架平台上的多目标优化问题，目的是寻找具有改进生物力学性能的新几何设计。以下一组通过有限元模型计算的指标用于量化支架性能：血管损伤、径向反冲、弯曲阻力、纵向阻力、径向强度和脱垂指数。借助基于代理的算法解决了多目标优化问题；为了比较和验证的目的，四种基于代理的多目标优化算法（EI _hv -EGO，P _hv-EGO、ParEGO 和 SMS-EGO）样本预算有限，并比较了它们的结果。每种算法输出的非支配解集的质量根据四个质量指标进行评估：超体积、R 2、ε 和世代距离。结果表明，P _hv-EGO 是总体表现最好的算法。然后，选择最高质量的帕累托前沿对优化结果进行深入分析。对每对目标函数的相关性和冲突量进行了量化。接下来，通过聚类分析，人们能够识别具有相似性能行为的解决方案系列，并从生物力学的角度讨论目标之间现有权衡的性质，以及设计参数和解决方案之间的趋势。最后，执行了基于约束的设计选择，目标是在帕累托前沿中找到与基线设计相比在所有目标中具有相同或更好性能的解决方案。

更新日期：2021-03-10

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