Connecting fractional anisotropy from medical images with mechanical anisotropy of a hyperviscoelastic fibre-reinforced constitutive model for brain tissue
Connecting fractional anisotropy from medical images with mechanical anisotropy of a hyperviscoelastic fibre-reinforced constitutive model for brain tissue
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DOI：
10.1098/rsif.2013.0914
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发表时间：
2014-02-06
影响因子：
3.9
通讯作者：
Kleiven, Svein
中科院分区：
文献类型：
--
作者：
Giordano, Chiara;Kleiven, Svein
脑组织建模多年来一直是一个活跃的研究领域。脑物质不遵循普通材料的本构关系，并且施加到脑的载荷取决于组织局部形态而转化为应力和应变。在这项工作中，一个超粘弹性纤维增强各向异性法律用于计算脑损伤预测。由于纤维增强分散参数，该配方占各向异性的功能和异质性的组织由于不同的轴突对齐。这项工作的新奇之处是材料的机械各向异性与分数各向异性（FA）从扩散张量图像的相关性。有限元模型被用来研究不同加载条件下纤维分布的影响。在拉压载荷的情况下，实验和模拟之间的比较突出了所提出的FA-k相关性的有效性。轴突对齐影响FE模型预测的变形，当轴突方向的应变相对于最大主应变较大时，检测到最大变形减小。它的结论是，纤维分散信息的脑组织的本构关系的引入影响模拟的逼真度。
Brain tissue modelling has been an active area of research for years. Brain matter does not follow the constitutive relations for common materials and loads applied to the brain turn into stresses and strains depending on tissue local morphology. In this work, a hyperviscoelastic fibre-reinforced anisotropic law is used for computational brain injury prediction. Thanks to a fibre-reinforcement dispersion parameter, this formulation accounts for anisotropic features and heterogeneities of the tissue owing to different axon alignment. The novelty of the work is the correlation of the material mechanical anisotropy with fractional anisotropy (FA) from diffusion tensor images. Finite-element (FE) models are used to investigate the influence of the fibre distribution for different loading conditions. In the case of tensile-compressive loads, the comparison between experiments and simulations highlights the validity of the proposed FA-k correlation. Axon alignment affects the deformation predicted by FE models and, when the strain in the axonal direction is large with respect to the maximum principal strain, decreased maximum deformations are detected. It is concluded that the introduction of fibre dispersion information into the constitutive law of brain tissue affects the biofidelity of the simulations.