Guo, L, Vardakis, JC, Chou, D et al. (1 more author) (2020) A multiple-network poroelastic model for biological systems and application to subject-specific modelling of cerebral fluid transport. International Journal of Engineering Science, 147. 103204. ISSN 0020-7225
Abstract
Biological tissue can be viewed as porous, permeable and deformable media infiltrated by fluids, such as blood and interstitial fluid. A finite element model has been developed based on the multiple-network poroelastic theory to investigate transport phenomenon in such biological systems. The governing equations and boundary conditions are adapted for the cerebral environment as an example. The numerical model is verified against analytical solutions of classical consolidation problems and validated using experimental data of infusion tests. It is then applied to three-dimensional subject-specific modelling of brain, including anatomically realistic geometry, personalised permeability map and arterial blood supply to the brain. Numerical results of smoking and non-smoking subjects show hypoperfusion in the brains of smoking subjects, which also demonstrate that the numerical model is capable of capturing spatio-temporal fluid transport in biological systems across different scales.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/ ) |
Keywords: | Poroelasticity; Multiple fluids; Finite element method; Transport phenomenon; Subject-specific modelling; Brain |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Jul 2020 12:47 |
Last Modified: | 02 Jul 2020 12:47 |
Status: | Published |
Publisher: | Elsevier BV |
Identification Number: | 10.1016/j.ijengsci.2019.103204 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162618 |