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Updated: Mar 8, 2026

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

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Published on: December 6, 2024

Multi-objective optimization of coronary stent using Kriging surrogate model.

Hongxia Li¹, Junfeng Gu², Minjie Wang¹

¹School of Mechanical Engineering, Dalian University of Technology, Dalian, 116023, Liaoning, China.

Biomedical Engineering Online

|February 4, 2017

Summary

This study introduces an adaptive optimization method to improve stent expansion. The novel approach significantly reduces the dogboning effect and elastic recoil, enhancing stent performance and patient outcomes.

Keywords:

Black-box techniques Design optimization Dogboning Kriging surrogate model Radial elastic recoil Stent

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Ferromagnetic Bare Metal Stent for Endothelial Cell Capture and Retention

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Published on: September 18, 2015

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Ferromagnetic Bare Metal Stent for Endothelial Cell Capture and Retention

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Published on: September 18, 2015

Area of Science:

Biomedical Engineering
Computational Mechanics
Medical Device Design

Background:

Stent design optimization is complex due to nonlinear relationships between parameters and goals.
Conflicting performance criteria make traditional optimization methods time-consuming and challenging.
Computational methods offer a viable solution for systematic and efficient stent design.

Purpose of the Study:

To propose an adaptive optimization method for stent design.
To enhance stent expansion performance.
To reduce the risk of vascular in-stent restenosis injury.

Main Methods:

A multi-objective optimization method using a Kriging surrogate model was developed.
Design of experiment methods and Kriging were integrated to model stent dilation performance.
Expected improvement criterion was used for global optimal design search.
Finite element method (FEM) analyzed stent expansion.

Main Results:

The optimization method achieved optimal solutions in 27 iterations.
Dogboning ratio was reduced by over 89%, nearly eliminating the effect.
Average elastic recoil decreased by 15.17%.

Conclusions:

FEM-based multi-objective optimization with a Kriging surrogate model effectively reduces stent dogboning and elastic recoil.
The proposed method demonstrates significant improvements in stent expansion properties.
Further research is warranted for more design goals and advanced optimization methods.