Mao Haojie, Gao Haitao, Cao Libo, Genthikatti Vinay Veeranna, Yang King H
Bioengineering Center, Wayne State University, Detroit, MI, USA.
Comput Methods Biomech Biomed Engin. 2013;16(3):271-9. doi: 10.1080/10255842.2011.617005. Epub 2011 Dec 8.
The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations.
有限元（FE）方法是研究脑损伤的有力工具，而脑损伤仍是一个关键的健康问题。特定受试者/患者的有限元脑模型有潜力在计算机辅助手术期间准确预测特定受试者/患者的脑反应，或设计特定受试者的头盔以预防脑损伤。不幸的是，为包含复杂颅内表面和不同内部结构的大脑开发高质量六面体有限元网格所需的工作量令人望而却步。本研究展示了使用多块技术为成人和小儿脑（3岁）开发全六面体有限元脑模型的高效网格划分过程。此外，使用块技术可以轻松调整网格密度。这一优势有助于进行网格收敛研究，并通过平衡可用计算能力和预测精度，为选择合适的脑网格密度提供更多自由度。建议在生物医学领域采用多块网格划分方法来高效开发三维全六面体高质量模型，以提高数值模拟的接受度和应用。
