这篇论文提出了MT-GNN，用图网络预测脑结构形状随时间的演化，在ADNI上比现有方法误差低2%以上，而且预测越久优势越明显。
MT-GNN是一种新的纵向脑形态预测模型，通过预测每个顶点的第一基本形式（度量张量）来连续时间建模皮层下结构的形状演化，并利用可微ARAP求解器重建表面。在ADNI数据集的14个皮层下结构上，MT-GNN在所有预测时域上平均顶点误差比时间均值降低2.29%（p=6.1×10^-5），在14/14个结构上均优于基线。相比测地线形状回归DCM（-0.19%）和网格Transformer（TransforMesh，-0.45%；p=1.2×10^-4），MT-GNN的优势随预测时域延长而扩大。该方法支持任意多次历史访问和任意预测时域，实现端到端训练。
Predicting Brain Morphometry with MT-GNN: Mesh Evolution in Continuous Time with Graph-Based Metric Tensor Embeddings
Predicting how a subcortical structure's shape will evolve from a few prior scans could support prognosis and clinical-trial enrichment. Existing longitudinal mesh predictors either extrapolate shape trajectories via high-dimensional embeddings or regress vertex deformations directly. We instead predict the surface's intrinsic geometry in continuous time: a single per-structure graph network predicts the future per-vertex first fundamental form (metric tensor) for an arbitrary causal multiple-visit history and an arbitrary prediction horizon, conditioned on a Fourier encoding of the lead time. The predicted metric is decoded into a surface by a differentiable As-Rigid-As-Possible solver, and the model is trained end-to-end on the rigid-aligned vertex error. Training through the reconstruction keeps the decoded prediction a valid surface and consistently improves it. On 14 subcortical structures from the ADNI dataset, the proposed mesh evolution model (MT-GNN) predicts best among the evaluated methods at every horizon ($-2.29\%$ mean vertex error vs. the temporal mean, $p{=}6.1{\times}10^{-5}$, beating it on 14/14 structures), ahead of geodesic shape regression (DCM, $-0.19\%$) and a mesh transformer (TransforMesh, $-0.45\%$; $p{=}1.2{\times}10^{-4}$), with the lead widening as the horizon grows.