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Investigating the reference domain influence in personalised models of cardiac mechanics: Effect of unloaded geometry on cardiac biomechanics

Research output: Contribution to journalArticlepeer-review

Myrianthi Hadjicharalambous, Christian T. Stoeck, Miriam Weisskopf, Nikola Cesarovic, Eleftherios Ioannou, Vasileios Vavourakis, David A. Nordsletten

Original languageEnglish
Pages (from-to)1579-1597
Number of pages19
JournalBiomechanics and Modeling in Mechanobiology
Volume20
Issue number4
DOIs
Accepted/In press2021
PublishedAug 2021

Bibliographical note

Funding Information: The authors would like to acknowledge funding from the University of Cyprus; V.V. acknowledges UCY’s StartUp Grant and the PostDoctoral Fellowships scheme. D.N. would like to acknowledge funding from Engineering and Physical Sciences Research Council (EP/N011554/1 and EP/R003866/1). CTS would like to acknowledge funding from Swiss national science foundation (PZ00P2_174144). Funding Information: Financial support was received from the University of Cyprus, the Engineering and Physical Sciences Research Council (EP/N011554/1 and EP/R003866/1) and from Swiss national science foundation (PZ00P2_174144). Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

A major concern in personalised models of heart mechanics is the unknown zero-pressure domain, a prerequisite for accurately predicting cardiac biomechanics. As the reference configuration cannot be captured by clinical data, studies often employ in-vivo frames which are unlikely to correspond to unloaded geometries. Alternatively, zero-pressure domain is approximated through inverse methodologies, which, however, entail assumptions pertaining to boundary conditions and material parameters. Both approaches are likely to introduce biases in estimated biomechanical properties; nevertheless, quantification of these effects is unattainable without ground-truth data. In this work, we assess the unloaded state influence on model-derived biomechanics, by employing an in-silico modelling framework relying on experimental data on porcine hearts. In-vivo images are used for model personalisation, while in-situ experiments provide a reliable approximation of the reference domain, creating a unique opportunity for a validation study. Personalised whole-cycle cardiac models are developed which employ different reference domains (image-derived, inversely estimated) and are compared against ground-truth model outcomes. Simulations are conducted with varying boundary conditions, to investigate the effect of data-derived constraints on model accuracy. Attention is given to modelling the influence of the ribcage on the epicardium, due to its close proximity to the heart in the porcine anatomy. Our results find merit in both approaches for dealing with the unknown reference domain, but also demonstrate differences in estimated biomechanical quantities such as material parameters, strains and stresses. Notably, they highlight the importance of a boundary condition accounting for the constraining influence of the ribcage, in forward and inverse biomechanical models.

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