李红霞

个人信息Personal Information

副教授

博士生导师

硕士生导师

性别:女

毕业院校:大连理工大学

学位:博士

所在单位:机械工程学院

学科:机械制造及其自动化

办公地点:知方楼7128

联系方式:18940953096

电子邮箱:hxli@dlut.edu.cn

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Multi-objective optimization of coronary stent using Kriging surrogate model

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论文类型:期刊论文

发表时间:2021-01-30

发表刊物:BIOMEDICAL ENGINEERING ONLINE

卷号:15

期号:Suppl 2

页面范围:148

ISSN号:1475-925X

关键字:Stent; Dogboning; Radial elastic recoil; Black-box techniques; Kriging surrogate model; Design optimization

摘要:Background: In stent design optimization, the functional relationship between design parameters and design goals is nonlinear, complex, and implicit and the multi-objective design of stents involves a number of potentially conflicting performance criteria. Therefore it is hard and time-consuming to find the optimal design of stent either by experiment or clinic test. Fortunately, computational methods have been developed to the point whereby optimization and simulation tools can be used to systematically design devices in a realistic time-scale. The aim of the present study is to propose an adaptive optimization method of stent design to improve its expansion performance.
   Methods: Multi-objective optimization method based on Kriging surrogate model was proposed to decrease the dogboning effect and the radial elastic recoil of stents to improve stent expansion properties and thus reduce the risk of vascular in-stent reste-nosis injury. Integrating design of experiment methods and Kriging surrogate model were employed to construct the relationship between measures of stent dilation performance and geometric design parameters. Expected improvement, an infilling sampling criterion, was employed to balance local and global search with the aim of finding the global optimal design. A typical diamond-shaped coronary stent-balloon system was taken as an example to test the effectiveness of the optimization method. Finite element method was used to analyze the stent expansion of each design.
   Results: 27 iterations were needed to obtain the optimal solution. The absolute values of the dogboning ratio at 32 and 42 ms were reduced by 94.21 and 89.43%, respectively. The dogboning effect was almost eliminated after optimization. The average of elastic recoil was reduced by 15.17%.
   Conclusion: This article presents FEM based multi-objective optimization method combining with the Kriging surrogate model to decrease both the dogboning effect and radial elastic recoil of stents. The numerical results prove that the proposed optimization method effectively decreased both the dogboning effect and radial elastic recoil of stent. Further investigations containing more design goals and more effective multidisciplinary design optimization method are warranted.