Multi-objective optimisation of material properties and strut geometry for poly(L-lactic acid) coronary stents using response surface methodology

Ross W. Blair, Nicholas J. Dunne, Alex B. Lennon, Gary H. Menary

Research output: Contribution to journalArticle

1 Citation (Scopus)
101 Downloads (Pure)

Abstract

Coronary stents for treating atherosclerosis are traditionally manufactured from metallic alloys. However, metal stents permanently reside in the body and may trigger undesirable immunological responses. Bioresorbable polymer stents can provide a temporary scaffold that resorbs once the artery heals but are mechanically inferior, requiring thicker struts for equivalent radial support, which may increase thrombosis risk. This study addresses the challenge of designing mechanically effective but sufficiently thin poly(L-lactic acid) stents through a computational approach that optimises material properties and stent geometry. Forty parametric stent designs were generated: cross-sectional area (post-dilation), foreshortening, stent-to-artery ratio and radial collapse pressure were evaluated computationally using finite element analysis. Response surface methodology was used to identify performance trade-offs by formulating relationships between design parameters and response variables. Multi-objective optimisation was used to identify suitable stent designs from approximated Pareto fronts and an optimal design is proposed that offers comparable performance to designs in clinical practice. In summary, a computational framework has been developed that has potential application in the design of high stiffness, thin strut polymeric stents.

Original languageEnglish
Article numbere0218768
Number of pages21
JournalPLoS ONE
Volume14
Issue number8
DOIs
Publication statusPublished - 26 Aug 2019

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  • Student Theses

    A combined experimental and computational framework to optimise processing and design of poly(L-lactic acid) bioresorbable stents

    Author: Blair, R., Dec 2019

    Supervisor: Menary, G. (Supervisor), Lennon, A. (Supervisor) & Dunne, N. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

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