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.
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.
Med Biol Eng Comput. 2019 May;57(5):1109-1120. doi: 10.1007/s11517-018-1944-7. Epub 2019 Jan 11.
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. Graphical abstract 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.
为了充分了解脑损伤机制并开发有效的保护方法，首先需要一个准确的脑组织本构模型。通常，脑组织被视为一种粘弹性材料，并在脑损伤模拟中简单使用。事实上，脑组织具有粘超弹性特性。因此，本文提出了一种有效的计算反演方法，通过压缩实验确定脑白质粘超弹性本构模型的材料参数。首先，借助 3D 手工扫描仪，获取脑白质标本的 3D 几何形状，从而有可能建立特定标本的精确模拟模型。然后，采用全局灵敏度分析来评估材料参数的重要性，并进一步确定可能识别的参数。随后，基于遗传算法，通过最小化实验和模拟响应之间的匹配误差，确定脑白质的最优材料参数。最后，通过比较特定标本上的实验和模拟结果，以及与参考文献中的材料参数的模拟结果，分别验证了脑白质本构模型参数的准确性和可靠性。
用于确定特定脑白质试样材料参数的计算反演技术的主要流程图。概括：本文结合计算反演方法和特定标本的无约束单轴压缩实验，提出了一种有效的识别方法，可准确确定脑白质的超弹性和粘弹性参数。
