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Advanced Multi-objective Design Analysis to Identify Ideal Stent Design

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Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting (MLMECH 2019, CVII-STENT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11794))

Abstract

Coronary stents are the preferred option for treating diseased coronary arteries. When implanted, the stent is deployed at the lesion to restore the lumen area by scaffolding the vessel open. However, stents still have a relatively high failure rate of 2–8%, with many patients requiring further interventions due to restenosis and stent thrombosis. It is known that blood flow disruption caused by the presence of a stent can trigger signalling pathways that accelerate restenosis or trigger thrombus formation. This work aims to find design variable values that minimize these adverse flow conditions using Multi-Objective optimisation.

This project was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.

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Author information

Authors and Affiliations

  1. UNSW Syndey, Sydney, 2023, Australia

    Ramtin Gharleghi, Heidi Wright, Somesh Khullar, Jinbo Liu & Susann Beier

  2. UNSW Canberra, Canberra, 2023, Australia

    Tapabrata Ray

Authors
  1. Ramtin Gharleghi
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  2. Heidi Wright
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  3. Somesh Khullar
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  4. Jinbo Liu
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  5. Tapabrata Ray
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  6. Susann Beier
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Corresponding author

Correspondence to Susann Beier .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Hongen Liao

  2. University of Barcelona, Barcelona, Spain

    Simone Balocco

  3. Tsinghua University, Beijing, China

    Guijin Wang

  4. Chinese Academy of Sciences, Beijing, China

    Feng Zhang

  5. Tsinghua University, Beijing, China

    Yongpan Liu

  6. Tsinghua University, Beijing, China

    Zijian Ding

  7. École de Technologie Supérieure, Montreal, QC, Canada

    Luc Duong

  8. University of Calgary, Calgary, AB, Canada

    Renzo Phellan

  9. Technical University of Munich, Munich, Germany

    Guillaume Zahnd

  10. Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany

    Katharina Breininger

  11. Technical University of Munich, Munich, Germany

    Shadi Albarqouni

  12. University College London, London, UK

    Stefano Moriconi

  13. Imperial College London, London, UK

    Su-Lin Lee

  14. Technical University of Munich, Munich, Germany

    Stefanie Demirci

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Gharleghi, R., Wright, H., Khullar, S., Liu, J., Ray, T., Beier, S. (2019). Advanced Multi-objective Design Analysis to Identify Ideal Stent Design. In: Liao, H., et al. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. MLMECH CVII-STENT 2019 2019. Lecture Notes in Computer Science(), vol 11794. Springer, Cham. https://doi.org/10.1007/978-3-030-33327-0_23

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  • DOI: https://doi.org/10.1007/978-3-030-33327-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33326-3

  • Online ISBN: 978-3-030-33327-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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