Graph-based homogenisation for modelling cardiac fibrosis.

Cardiac modelling Eikonal methods Graph-based modelling Homogenisation Numerical upscaling

Journal

Journal of computational physics
ISSN: 0021-9991
Titre abrégé: J Comput Phys
Pays: United States
ID NLM: 9883524

Informations de publication

Date de publication:
15 Jun 2022
Historique:
received: 24 01 2021
revised: 15 02 2022
accepted: 02 03 2022
entrez: 12 8 2022
pubmed: 13 8 2022
medline: 13 8 2022
Statut: ppublish

Résumé

Fibrosis, the excess of extracellular matrix, can affect, and even block, propagation of action potential in cardiac tissue. This can result in deleterious effects on heart function, but the nature and severity of these effects depend strongly on the localisation of fibrosis and its by-products in cardiac tissue, such as collagen scar formation. Computer simulation is an important means of understanding the complex effects of fibrosis on activation patterns in the heart, but concerns of computational cost place restrictions on the spatial resolution of these simulations. In this work, we present a novel numerical homogenisation technique that uses both Eikonal and graph approaches to allow fine-scale heterogeneities in conductivity to be incorporated into a coarser mesh. Homogenisation achieves this by deriving effective conductivity tensors so that a coarser mesh can then be used for numerical simulation. By taking a graph-based approach, our homogenisation technique functions naturally on irregular grids and does not rely upon any assumptions of periodicity, even implicitly. We present results of action potential propagation through fibrotic tissue in two dimensions that show the graph-based homogenisation technique is an accurate and effective way to capture fine-scale domain information on coarser meshes in the context of sharp-fronted travelling waves of activation. As test problems, we consider excitation propagation in tissue with diffuse fibrosis and through a tunnel-like structure designed to test homogenisation, interaction of an excitation wave with a scar region, and functional re-entry.

Identifiants

pubmed: 35959500
doi: 10.1016/j.jcp.2022.111126
pii: S0021-9991(22)00188-7
pmc: PMC9352598
doi:

Types de publication

Journal Article

Langues

eng

Pagination

None

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Megan E Farquhar (ME)

Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.

Kevin Burrage (K)

Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Department of Computer Science, Oxford University, Oxford, United Kingdom.

Rodrigo Weber Dos Santos (R)

Department of Computer Science and Program on Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil.

Alfonso Bueno-Orovio (A)

Department of Computer Science, Oxford University, Oxford, United Kingdom.

Brodie A J Lawson (BAJ)

Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Centre for Data Science, Queensland University of Technology, Brisbane, Australia.

Classifications MeSH