Constitutive model for brain tissue under finite compression

Kaveh Laksari, Mehdi Shafieian, Kurosh Darvish

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

While advances in computational models of mechanical phenomena have made it possible to simulate dynamically complex problems in biomechanics, accurate material models for soft tissues, particularly brain tissue, have proven to be very challenging. Most studies in the literature on material properties of brain tissue are performed in shear loading and very few tackle the behavior of brain in compression. In this study, a viscoelastic constitutive model of bovine brain tissue under finite step-and-hold uniaxial compression with 10s -1 ramp rate and 20s hold time has been developed. The assumption of quasi-linear viscoelasticity (QLV) was validated for strain levels of up to 35%. A generalized Rivlin model was used for the isochoric part of the deformation and it was shown that at least three terms (C 10, C 01 and C 11) are needed to accurately capture the material behavior. Furthermore, for the volumetric deformation, a two parameter Ogden model was used and the extent of material incompressibility was studied. The hyperelastic material parameters were determined through extracting and fitting to two isochronous curves (0.06s and 14s) approximating the instantaneous and steady-state elastic responses. Viscoelastic relaxation was characterized at five decay rates (100, 10, 1, 0.1, 0s -1) and the results in compression and their extrapolation to tension were compared against previous models.

Original languageEnglish (US)
Pages (from-to)642-646
Number of pages5
JournalJournal of Biomechanics
Volume45
Issue number4
DOIs
StatePublished - Feb 23 2012
Externally publishedYes

Keywords

  • Brain tissue biomechanics
  • Compression test
  • Finite deformation
  • Isochronous curves
  • Viscoelasticity

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Biomedical Engineering
  • Rehabilitation

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