Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: A critical step towards clinical end-user relevance

Umberto Noè, Alan Lazarus, Hao Gao, Vinny Davies, Benn MacDonald, Kenneth Mangion, Colin Berry, Xiaoyu Luo, Dirk Husmeier*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, we have witnessed substantial advances in the mathematical modelling of the biomechanical processes underlying the dynamics of the cardiac soft-tissue. Gao et al. (Gao et al. 2017 J. R. Soc. Interface 14, 20170203 (doi:10.1098/rsif.2017.0203)) demonstrated that the parameters underlying the biomechanical model have diagnostic value for prognosticating the risk of myocardial infarction. However, the computational costs of parameter estimation are prohibitive when the goal lies in building realtime clinical decision support systems. This is due to the need to repeatedly solve the mathematical equations numerically using finite-element discretization during an iterative optimization routine. The present article presents a method for accelerating the inference of the constitutive parameters by using statistical emulation with Gaussian processes. We demonstrate how the computational costs can be reduced by about three orders of magnitude, with hardly any loss in accuracy, and we assess various alternative techniques in a comparative evaluation study based on simulated data obtained by solving the left ventricular model with the finite-element method, and real magnetic resonance images data for a human volunteer.
Original languageEnglish
Article number20190114
JournalJournal of the Royal Society, Interface
Volume16
Issue number156
Early online date3 Jul 2019
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Constitutive parameters
  • Emulation
  • Finite element discretization
  • Left ventricular mechanics
  • Parameter estimation
  • Simulation

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