Fitted hyperelastic parameters for Human brain tissue from reported tension,compression, and shear tests |
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Authors: | Richard Moran,Joshua H. Smith,José J. Garcí a |
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Affiliation: | Escuela de Ingeniería Civil y Geomática. Universidad del Valle. Colombia; Department of Mechanical Engineering. Lafayette College, Easton, PA, USA; Escuela de Ingeniería Civil y Geomática. Universidad del Valle. Colombia |
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Abstract: | The mechanical properties of human brain tissue are the subject of interest because of their use in understanding brain trauma and in developing therapeutic treatments and procedures. To represent the behavior of the tissue, we have developed hyperelastic mechanical models whose parameters are fitted in accordance with experimental test results. However, most studies available in the literature have fitted parameters with data of a single type of loading, such as tension, compression, or shear. Recently, Jin et al. (Journal of Biomechanics 46:2795−2801, 2013) reported data from ex vivo tests of human brain tissue under tension, compression, and shear loading using four strain rates and four different brain regions. However, they do not report parameters of energy functions that can be readily used in finite element simulations. To represent the tissue behavior for the quasi-static loading conditions, we aimed to determine the best fit of the hyperelastic parameters of the hyperfoam, Ogden, and polynomial strain energy functions available in ABAQUS for the low strain rate data, while simultaneously considering all three loading modes. We used an optimization process conducted in MATLAB, calling iteratively three finite element models developed in ABAQUS that represent the three loadings. Results showed a relatively good fit to experimental data in all loading modes using two terms in the energy functions. Values for the shear modulus obtained in this analysis (897−1653 Pa) are in the range of those presented in other studies. These energy-function parameters can be used in brain tissue simulations using finite element models. |
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Keywords: | Hyperelastic model Constitutive equation Fitting parameters Finite element method Inverse method |
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