Generating subject-specific, all-hexahedral meshes for finite element analysis continues to be of significant interest in biomechanical research communities. To date, most automated methods "morph" an existing atlas mesh to match with a subject anatomy, which usually result in degradation in mesh quality because of mesh distortion. We present an automated meshing technique that produces satisfactory mesh quality and accuracy without mesh repair. An atlas mesh is first developed using a script. A subject-specific mesh is generated with the same script after transforming the geometry into the atlas space following rigid image registration, and is transformed back into the subject space. By meshing the brain in 11 subjects, we demonstrate that the technique's performance is satisfactory in terms of both mesh quality (99.5% of elements had a scaled Jacobian >0.6 while <0.01% were between 0 and 0.2) and accuracy (average distance between mesh boundary and geometrical surface was 0.07 mm while <1% greater than 0.5mm). The combined computational cost for image registration and meshing was <4 min. Our results suggest that the technique is effective for generating subject-specific, all-hexahedral meshes and that it may be useful for meshing a variety of anatomical structures across different biomechanical research fields.

中文翻译：

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通过图像配准自动生成特定主题的六面体网格

为有限元分析生成特定主题的全六面体网格仍然是生物力学研究界的重要关注点。迄今为止，大多数自动化方法都会“变形”现有的地图集网格以与主体解剖结构相匹配，这通常会由于网格失真而导致网格质量下降。我们提出了一种自动网格划分技术，无需网格修复即可生成令人满意的网格质量和精度。首先使用脚本开发地图集网格。在刚性图像配准后将几何体转换到图集空间后，使用相同的脚本生成特定于主题的网格，然后再转换回主题空间。通过对 11 个受试者的大脑进行网格划分，我们证明该技术在网格质量 (99. 5% 的元素具有缩放雅可比 > 0.6 而 <0.01% 介于 0 和 0.2 之间）和精度（网格边界和几何表面之间的平均距离为 0.07 毫米，而 <1% 大于 0.5 毫米）。图像配准和网格划分的组合计算成本 <4 分钟。我们的结果表明，该技术可有效生成特定于主题的全六面体网格，并且可用于对不同生物力学研究领域的各种解剖结构进行网格划分。