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Identification of the visco-hyperelastic properties of brain white matter based on the combination of inverse method and experiment

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Abstract

To fully understand the brain injury mechanism and develop effective protective approaches, an accurate constitutive model of brain tissue is firstly required. Generally, the brain tissue is regarded as a kind of viscoelastic material and is simply used in the simulation of brain injury. In fact, the brain tissue has the behavior of the visco-hyperelastic property. Therefore, this paper presents an effective computational inverse method to determine the material parameters of visco-hyperelastic constitutive model of brain white matter through compression experiments. First, with the help of 3D hand scanner, 3D geometries of brain white matter specimens are obtained to make it possible to establish the accurate simulation models of the specific specimens. Then, the global sensitivity analysis is adopted to evaluate the importance of the material parameters and further determine the parameters which may be identified. Subsequently, based on the genetic algorithm, the optimal material parameters of brain white matter can be identified by minimizing the match error between the experimental and simulated responses. Finally, by comparing the experiment and simulation results on the other specific specimen, and the simulation results with the material parameters from the references, respectively, the accuracy and reliability of the constitutive model parameters of brain white matter are demonstrated.

The main flowchart of the computational inverse technique for determining the material parameters of specimen-specific on brain white matter. Generalization: Combining the computational inverse method and unconfined uniaxial compression experiment of the specific specimen, an effective identification method is presented to accurately determine the hyperelastic and viscoelastic parameters of brain white matter in this paper.

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Funding

This work is supported by the National Science Foundation of China (Grant Nos. 51621004 and 11572115) and an independent research project of the State Key Laboratory of Reliability and Intelligence Electrical Equipment, Hebei University of Technology (EERIZZ2018001).

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Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300401, People’s Republic of China

    Qiming Liu & Xu Han

  2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People’s Republic of China

    Qiming Liu, Jie Liu, Xu Han, Lixiong Cao & Kezhen Shan

  3. Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Fengjiao Guan

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  1. Qiming Liu
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Correspondence to Jie Liu or Xu Han.

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Liu, Q., Liu, J., Guan, F. et al. Identification of the visco-hyperelastic properties of brain white matter based on the combination of inverse method and experiment . Med Biol Eng Comput 57, 1109–1120 (2019). https://doi.org/10.1007/s11517-018-1944-7

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