Viceconti M, Bellingeri L, Cristofolini L, Toni A
Laboratory for Biomaterials Technology, Rizzoli Orthopaedic Institute, Bologna, Italy.
Med Eng Phys. 1998 Jan;20(1):1-10. doi: 10.1016/s1350-4533(97)00049-0.
The aim of this study was to evaluate comparatively five methods for automating mesh generation (AMG) when used to mesh a human femur. The five AMG methods considered were: mapped mesh, which provides hexahedral elements through a direct mapping of the element onto the geometry; tetra mesh, which generates tetrahedral elements from a solid model of the object geometry; voxel mesh which builds cubic 8-node elements directly from CT images; and hexa mesh that automatically generated hexahedral elements from a surface definition of the femur geometry. The various methods were tested against two reference models: a simplified geometric model and a proximal femur model. The first model was useful to assess the inherent accuracy of the meshes created by the AMG methods, since an analytical solution was available for the elastic problem of the simplified geometric model. The femur model was used to test the AMG methods in a more realistic condition. The femoral geometry was derived from a reference model (the "standardized femur") and the finite element analyses predictions were compared to experimental measurements. All methods were evaluated in terms of human and computer effort needed to carry out the complete analysis, and in terms of accuracy. The comparison demonstrated that each tested method deserves attention and may be the best for specific situations. The mapped AMG method requires a significant human effort but is very accurate and it allows a tight control of the mesh structure. The tetra AMG method requires a solid model of the object to be analysed but is widely available and accurate. The hexa AMG method requires a significant computer effort but can also be used on polygonal models and is very accurate. The voxel AMG method requires a huge number of elements to reach an accuracy comparable to that of the other methods, but it does not require any pre-processing of the CT dataset to extract the geometry and in some cases may be the only viable solution.
本研究的目的是比较用于对人体股骨进行网格划分的五种自动网格生成（AMG）方法。所考虑的五种AMG方法分别是：映射网格，它通过将单元直接映射到几何形状上来提供六面体单元；四面体网格，它从物体几何形状的实体模型生成四面体单元；体素网格，它直接从CT图像构建立方8节点单元；以及六面体网格，它从股骨几何形状的表面定义自动生成六面体单元。针对两个参考模型对各种方法进行了测试：一个简化几何模型和一个股骨近端模型。第一个模型有助于评估AMG方法创建的网格的固有精度，因为对于简化几何模型的弹性问题有解析解。股骨模型用于在更实际的条件下测试AMG方法。股骨几何形状源自一个参考模型（“标准化股骨”），并将有限元分析预测结果与实验测量结果进行比较。所有方法都根据进行完整分析所需的人力和计算机工作量以及精度进行了评估。比较表明，每种测试方法都值得关注，并且在特定情况下可能是最佳的。映射AMG方法需要大量人力，但非常准确，并且可以严格控制网格结构。四面体AMG方法需要待分析物体的实体模型，但广泛可用且准确。六面体AMG方法需要大量计算机工作量，但也可用于多边形模型，并且非常准确。体素AMG方法需要大量单元才能达到与其他方法相当的精度，但它不需要对CT数据集进行任何预处理来提取几何形状，在某些情况下可能是唯一可行的解决方案。
