Measurement of the hyperelastic properties of ex vivo brain tissue slices
Measurement of the hyperelastic properties of ex vivo brain tissue slices
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DOI：
10.1016/j.jbiomech.2011.01.019
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发表时间：
2011-04-07
影响因子：
2.4
通讯作者：
Samani, A.
中科院分区：
文献类型：
--
作者：
Kaster, T.;Sack, I.;Samani, A.
脑组织的弹性和超弹性性质是医学研究界感兴趣的，因为有几个应用程序，其中这些性质的准确表征对于准确的结果至关重要。线性响应适用于脑弹性成像，而非线性响应则适用于手术模拟程序。由于灰色和白色物质之间的生物学差异，预期它们的机械特性存在差异是合理的。这项工作的目标是表征大脑灰质和白色物质的弹性和超弹性特性。在这种方法中，这些组织的力-位移数据是从25个不同的大脑样本使用压痕装置。这些数据进行了处理与反问题算法，使用有限元法作为正问题求解器。得到了常用的Polynomial、Yeoh、Arruda-Boyce和Ogden模型对应的杨氏模量和超弹性参数。表征灰色和白色物质的线性和非线性力学行为的参数被发现是显着不同的。白色物质和灰质的杨氏模量分别为1787 +/- 186和1195 +/- 157 Pa。在超弹性模型中，由于其精度高、参数少和计算时间短，Yeoh模型被认为是最合适的。由于线性和非线性组织反应之间的显着差异，我们得出结论，将这些差异纳入脑生物力学模型是必要的，以提高准确性。(C)2011爱思唯尔有限公司保留所有权利。
The elastic and hyperelastic properties of brain tissue are of interest to the medical research community as there are several applications where accurate characterization of these properties is crucial for an accurate outcome. The linear response is applicable to brain elastography, while the non-linear response is of interest for surgical simulation programs. Because of the biological differences between gray and white matter, it is reasonable to expect a difference in their mechanical properties. The goal of this work is to characterize the elastic and hyperelastic properties of the brain gray and white matter. In this method, force-displacement data of these tissues were acquired from 25 different brain samples using an indentation apparatus. These data were processed with an inverse problem algorithm using finite element method as the forward problem solver. Young's modulus and the hyperelastic parameters corresponding to the commonly used Polynomial, Yeoh, Arruda-Boyce, and Ogden models were obtained. The parameters characterizing the linear and non-linear mechanical behavior of gray and white matters were found to be significantly different. Young's modulus was 1787 +/- 186 and 1195 +/- 157 Pa for white matter and gray matter, respectively. Among hyperelastic models, due to its accuracy, fewer parameters and shorter computational time requirements, Yeoh model was found to be the most suitable. Due to the significant differences between the linear and non-linear tissue response, we conclude that incorporating these differences into brain biomechanical models is necessary to increase accuracy. (C) 2011 Elsevier Ltd. All rights reserved.