Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation

Davies, V. , Noè, U. , Lazarus, A., Gao, H. , Macdonald, B. , Berry, C. , Luo, X. and Husmeier, D. (2019) Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(5), pp. 1555-1576. (doi:10.1111/rssc.12374) (PMID:31762497) (PMCID:PMC6856984)

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Abstract

A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic. We compare and contrast two emulation strategies: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Berry, Professor Colin and Gao, Dr Hao and Davies, Dr Vinny and Lazarus, Mr Alan and Luo, Professor Xiaoyu and Noe, Mr Umberto and Macdonald, Dr Benn and Husmeier, Professor Dirk
Authors: Davies, V., Noè, U., Lazarus, A., Gao, H., Macdonald, B., Berry, C., Luo, X., and Husmeier, D.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cardiovascular and Medical Sciences
College of Science and Engineering > School of Computing Science
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:20 September 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 68(5):1555-1576
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
662681"First steps towards modelling myocardial infarction (a computed MI Physiome): A case-control study of novel biomechanical parameters in acute MI survivors with left ventricular dysfunction."Colin BerryBritish Heart Foundation (BHF)PG/14/64/31043RI CARDIOVASCULAR & MEDICAL SCIENCES
694461EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&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

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