Improving cardio-mechanic inference by combining in vivo strain data with ex vivo volume–pressure data

Lazarus, A., Gao, H. , Luo, X. and Husmeier, D. (2022) Improving cardio-mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(4), pp. 906-931. (doi: 10.1111/rssc.12560)

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Abstract

Cardio-mechanic models show substantial promise for improving personalised diagnosis and disease risk prediction. However, estimating the constitutive parameters from strains extracted from in vivo cardiac magnetic resonance scans can be challenging. The reason is that circumferential strains, which are comparatively easy to extract, are not sufficiently informative to uniquely estimate all parameters, while longitudinal and radial strains are difficult to extract at high precision. In the present study, we show how cardio-mechanic parameter inference can be improved by incorporating prior knowledge from population-wide ex vivo volume–pressure data. Our work is based on an empirical law known as the Klotz curve. We propose and assess two alternative methodological frameworks for integrating ex vivo data via the Klotz curve into the inference framework, using both a non-empirical and empirical prior distribution.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Luo, Professor Xiaoyu and Gao, Dr Hao and Husmeier, Professor Dirk and Lazarus, Mr Alan
Authors: Lazarus, A., Gao, H., Luo, X., 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:Journal of the Royal Statistical Society: Series C (Applied Statistics)
Publisher:Wiley
ISSN:0035-9254
ISSN (Online):1467-9876
Published Online:22 April 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 71(4):906-931
Publisher Policy:Reproduced under a Creative Commons License

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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
172141EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&S - Mathematics
303231A whole-heart model of multiscale soft tissue mechanics and fluid structureinteraction for clinical applications (Whole-Heart-FSI)Xiaoyu LuoEngineering and Physical Sciences Research Council (EPSRC)EP/S020950/1M&S - Mathematics
305655Inference of cardio-mechanical parameters in real time: moving mathematical modelling into the clinicDirk HusmeierThe Royal Society of Edinburgh (ROYSOCED)62335M&S - Statistics