CORTET：稳健生成可用于模拟的皮质网格
CORTET: Robust generation of simulation-ready tetrahedral meshes of the fetal cerebral cortex
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中文总结 AI 辅助
研究针对人类大脑褶皱差异影响神经发育障碍成像生物标志物寻找的问题，提出CORTET全自动管道，将皮质表面转换为高质量四面体网格，经与通用网格划分器对比及多胎儿受试者验证，实现数值稳定的胎儿大脑形态弹性折叠模拟。
中文摘要 AI 辅助
每个人类大脑的褶皱方式都不同，这种自然变异使得寻找神经发育障碍的成像生物标志物变得复杂。基于物理的模拟有助于确定这种变异性背后的因果机制。然而，每次模拟都必须从大脑内部的坚实四面体网格开始，网格中最差的元素而非平均值决定模拟是否能运行。目前从胎儿MRI构建该网格需要大量人工干预。因此，我们提出了CORTET（皮质四面体网格化）：一个全自动管道，可将三角化的皮质表面转换为求解器就绪的四面体网格，其最差元素质量达到严格的稳定性目标且无需人工修复。通过在相同输入表面上与通用四面体网格划分器进行基准测试，我们分离出管道的贡献与输入几何形状的贡献，并在近200名跨越折叠期的胎儿受试者队列中验证了质量。直接从管道获取的网格维持了对真实胎儿受试者的数值稳定的形态弹性折叠模拟。
英文摘要
Every human brain folds differently, and such natural variation confounds the search for imaging biomarkers of neurodevelopmental disorders. Physics-based simulation can help determine the causal mechanisms that underpin this variability. Yet every simulation must be initiated from a volumetric mesh of the brain's interior, tetrahedral or hexahedral, and it is the worst element in that mesh, not the average, that decides whether the simulation runs at all. Building that mesh from fetal MRI currently requires labour-intensive manual intervention. We therefore present CORTET (CORtical TETrahedral meshing): a fully automated pipeline that converts a triangulated cortical surface into a solver-ready tetrahedral mesh whose worst-element quality meets a strict quality target with no manual repair. By benchmarking against a general-purpose tetrahedral mesher on the same input surfaces, we isolate the pipeline's contribution from that of the input geometry, and we validate quality across a cohort of nearly 200 fetal subjects spanning the folding period. A mesh taken straight from the pipeline sustains a numerically stable morphoelastic folding simulation of a real fetal subject.
