Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

有限元头部 (FE) 模型是研究头部损伤和开发保护系统的重要数值工具。生成具有一致六面体网格的解剖学准确和特定主题的头部模型仍然是一个重大挑战。本研究的重点是展示两项开发工作：首先，具有符合六面体网格的解剖学详细的 FE 头部模型，在大脑和脑脊液之间具有光滑的界面，嵌入白质 (WM) 纤维束；其次，通过集成Demons和Dramms可变形配准算法的新分层图像配准管道生成主题特定头部模型的变形方法。通过将模型预测与脑-颅骨相对运动、脑应变、和颅内压。为了证明头部模型和管道的适用性，生成了六个颅内体积和形状变化很大的特定主题的头部模型，并结合了特定主题的 WM 纤维束。计算颅、脑面罩、局部大脑区域和侧脑室的 DICE 相似系数以评估个性化准确性，证明了管道在生成详细的特定主题头部模型方面的效率，无需进一步网格修复即可实现令人满意的元素质量。然后对六个头部模型进行相同的震荡载荷，以研究大脑应变对大脑和 WM 纤维形态的受试者间变异性的敏感性。p  < 0.001)，以及它们的位置在受试者之间也有所不同，这表明需要进一步研究特定于受试者的模型的重要性。本研究中开发的技术可能有助于更好地评估个体脑损伤和未来个性化头部保护系统的开发。本研究还包含研究界可能认为有用的一般方面：使用接近或处于损伤水平的实验性脑应变进行头部模型验证；分层图像配准管道可用于变形其他头部模型，例如平滑体素模型。