Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

Borowska, A. , Gao, H. , Lazarus, A. and Husmeier, D. (2022) Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle. International Journal for Numerical Methods in Biomedical Engineering, 38(5), e3593. (doi: 10.1002/cnm.3593) (PMID:35302293)

[img] Text
267161.pdf - Published Version
Available under License Creative Commons Attribution.

7MB

Abstract

We consider parameter inference in cardio-mechanic models of the left ventricle, in particular the one based on the Holtzapfel-Ogden (HO) constitutive law, using clinical in vivo data. The equations underlying these models do not admit closed form solutions and hence need to be solved numerically. These numerical procedures are computationally expensive making computational run times associated with numerical optimisation or sampling excessive for the uptake of the models in the clinical practice. To address this issue, we adopt the framework of Bayesian optimisation (BO), which is an efficient statistical technique of global optimisation. BO seeks the optimum of an unknown black-box function by sequentially training a statistical surrogate-model and using it to select the next query point by leveraging the associated exploration-exploitation trade-off. To guarantee that the estimates based on the in vivo data are realistic also for high-pressures, unobservable in vivo, we include a penalty term based on a previously published empirical law developed using ex vivo data. Two case studies based on real data demonstrate that the proposed BO procedure outperforms the state-of-the-art inference algorithm for the HO law.

Item Type:Articles
Additional Information:This work was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), grant numbers EP/T017899/1and EP/R018634/1.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gao, Dr Hao and Husmeier, Professor Dirk and Borowska, Dr Agnieszka and Lazarus, Dr Alan
Authors: Borowska, A., Gao, H., Lazarus, A., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics
College of Science and Engineering > School of Mathematics and Statistics > Mathematics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:International Journal for Numerical Methods in Biomedical Engineering
Publisher:Wiley
ISSN:2040-7939
ISSN (Online):2040-7947
Published Online:18 March 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in International Journal for Numerical Methods in Biomedical Engineering 38(5): e3593
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
308255The SofTMech Statistical Emulation and Translation HubDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/T017899/1M&S - Statistics
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science