Ganpule S, Daphalapurkar N P, Cetingul M Pirtini, Ramesh K T
Indian Institute of Technology Roorkee, Roorkee, India, 247667.
Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, 21218.
Shock Waves. 2018 Jan;28(1):127-139. doi: 10.1007/s00193-017-0791-z. Epub 2017 Dec 18.
Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury (TBI) and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well-characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2 mm voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber-bundles for modeling white-matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured deformation in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.
诸如爆炸导致的创伤性脑损伤是一种复杂的损伤，伴有广泛的症状和残疾。脑生物力学的计算模型有望阐明创伤性脑损伤（TBI）的力学原理并开发预防装置。然而，模型要具有预测性就需要可靠的材料参数。不幸的是，人类脑组织的特性难以测量，尤其是脑组织的体积模量尚未得到很好的表征。因此，在脑生物力学的计算模型中使用了广泛的体积模量值，其数值差异跨度高达三个数量级。然而，这些变化对计算预测的敏感性尚不清楚。在这项工作中，我们研究了三维计算人体头部模型对各种体积模量值的敏感性。根据体素分辨率为2毫米的T1加权MRI图像构建了一个特定个体的人体头部模型。扩散张量成像提供了轴突纤维束的空间分布和方向数据，用于对白质各向异性进行建模。利用人类志愿者的无损伤全场脑变形来评估模拟预测。比较结果表明，吉帕量级的体积模量值与在人类大脑中实验测量的变形最为吻合。此外，损伤载荷模拟表明，吉帕量级的体积模量值在预测损伤区域和损伤程度方面与临床发现最为匹配。
