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Connecting Fractional Anisotropy from Medical Images with Mechanical Anisotropy of a Hyperviscoelastic Fibre-reinforced Constitutive Model for Brain Tissue
KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.ORCID iD: 0000-0002-0569-5118
KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.ORCID iD: 0000-0003-0125-0784
2014 (English)In: Journal of the Royal Society Interface, ISSN 1742-5662, E-ISSN 1742-5689, Vol. 11, no 91, 20130914- p.Article in journal (Refereed) Published
Abstract [en]

Brain tissue modelling has been an active area of research for years. Brain matter does not follow the constitutive relations for common materials and loads applied to the brain turn into stresses and strains depending on tissue local morphology. In this work, a hyperviscoelastic fibre-reinforced anisotropic law is used for computational brain injury prediction. Thanks to a fibrere-inforcement dispersion parameter, this formulation accounts for anisotropic features and heterogeneities of the tissue owing to different axon alignment. The novelty of the work is the correlation of the material mechanical anisotropy with fractional anisotropy (FA) from diffusion tensor images. Finite-element (FE) models are used to investigate the influence of the fibre distribution for different loading conditions. In the case of tensile-compressive loads, the comparison between experiments and simulations highlights the validity of the proposed FA-k correlation. Axon alignment affects the deformation predicted by FE models and, when the strain in the axonal direction is large with respect to the maximum principal strain, decreased maximum deformations are detected. It is concluded that the introduction of fibre dispersion information into the constitutive law of brain tissue affects the biofidelity of the simulations.

Place, publisher, year, edition, pages
2014. Vol. 11, no 91, 20130914- p.
Keyword [en]
Brain Tissue, Constitutive Modelling, Fibre Dispersion, Anisotropy
National Category
Medical Engineering
URN: urn:nbn:se:kth:diva-134228DOI: 10.1098/rsif.2013.0914ISI: 000332384600011ScopusID: 2-s2.0-84891897481OAI: diva2:667586

QC 20140131

Available from: 2013-11-27 Created: 2013-11-20 Last updated: 2014-04-07Bibliographically approved

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Giordano, ChiaraKleiven, Svein
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