V2C-Long：具有时空对应关系的纵向皮层重建
V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence
AI总结：
本文提出首个面向纵向 MRI 的深度学习皮层重建方法 V2C-Long，通过双模板形变网络与网格空间模板聚合建立跨受试者、跨访视的时空对应，显著提升重建准确性、一致性及阿尔茨海默病萎缩检测敏感性。
摘要：
从纵向磁共振成像（MRI）中重建皮层，对于分析人脑形态变化不可或缺。尽管近期基于深度学习的皮层表面重建取得了进展，但纵向数据带来的挑战仍然存在。尤其是高度褶皱的脑表面之间缺乏强时空点对应关系，这阻碍了下游分析，因为如果解剖位置无法精确匹配，局部形态就不能直接比较。为解决该问题，我们提出 V2C-Long，这是首个专门面向纵向 MRI、基于深度学习的皮层重建方法。V2C-Long 在不同受试者和访视之间表现出很强的内在时空对应关系，从而减少了对基于表面的后处理需求。我们通过组合两个深度模板形变网络，并在网格空间中创新性地聚合受试者内模板，在重建过程中直接建立这种对应关系。我们在两项大型神经影像学研究中验证了 V2C-Long，重点考察表面准确性、一致性、泛化能力、重测信度和敏感性。结果表明，与现有方法相比，其在纵向一致性和准确性方面均有显著提升。此外，与纵向 FreeSurfer 相比，我们为阿尔茨海默病中的纵向皮层萎缩提供了更有力的证据。
英文摘要：
Reconstructing the cortex from longitudinal magnetic resonance imaging (MRI) is indispensable for analyzing morphological alterations in the human brain. Despite the recent advancement of cortical surface reconstruction with deep learning, challenges arising from longitudinal data are still persistent. Especially the lack of strong spatiotemporal point correspondence between highly convoluted brain surfaces hinders downstream analyses, as local morphology is not directly comparable if the anatomical location is not matched precisely. To address this issue, we present V2C-Long, the first dedicated deep learning-based cortex reconstruction method for longitudinal MRI. V2C-Long exhibits strong inherent spatiotemporal correspondence across subjects and visits, thereby reducing the need for surface-based post-processing. We establish this correspondence directly during the reconstruction via the composition of two deep template-deformation networks and innovative aggregation of within-subject templates in mesh space. We validate V2C-Long on two large neuroimaging studies, focusing on surface accuracy, consistency, generalization, test-retest reliability, and sensitivity. The results reveal a substantial improvement in longitudinal consistency and accuracy compared to existing methods. In addition, we demonstrate stronger evidence for longitudinal cortical atrophy in Alzheimer's disease than longitudinal FreeSurfer.
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