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Abstract：
Research on the local hippocampal atrophy for early detection of dementia has gained considerable attention. However, accurately quantifying subtle atrophy remains challenging in existing morphological methods due to the lack of consistent biological correspondence with the complex curving regions like the hippocampal head. Thereby, this article presents an innovative axis-referenced morphometric model (ARMM) that follows the anatomical lamellar organization of the hippocampus, which capture its precise and consistent longitudinal curving trajectory. Specifically, we establish an \"axis-referenced coordinate system\" based on a 7 T ex vivo hippocampal atlas following its entire curving longitudinal axis and orthogonal distributed lamellae. We then align individual hippocampi by deforming this template coordinate system to target spaces using boundary-guided diffeomorphic transformation, while ensuring that the lamellar vectors adhere to the constraint of medial-axis geometry. Finally, we measure local thickness and curvatures based on the coordinate system and boundary surface reconstructed from vector tips. The morphometric accuracy is evaluated by comparing reconstructed surfaces with those directly extracted from 7 T and 3 T MRI hippocampi. The results demonstrate that ARMM achieves the best performance, particularly in the curving head, surpassing the state-of-the-art morphological models. Additionally, morphological measurements from ARMM exhibit higher discriminatory power in distinguishing early Alzheimer\'s disease from mild cognitive impairment compared to volume-based measurements. Overall, the ARMM offers a precise morphometric assessment of hippocampal morphology on MR images, and sheds light on discovering potential image markers for neurodegeneration associated with hippocampal impairment.
摘要：
关于早期发现痴呆的局部海马萎缩的研究已经获得了相当多的关注。然而,由于缺乏与海马头部等复杂弯曲区域一致的生物学对应关系，因此在现有的形态学方法中，精确量化细微的萎缩仍然具有挑战性。因此，本文提出了一种创新的轴参考形态测量模型（ARMM），该模型遵循海马的解剖板层组织，捕捉其精确和一致的纵向弯曲轨迹。具体来说,我们建立了一个“轴参考坐标系”，基于一个7T离体海马图谱，遵循其整个弯曲的纵轴和正交分布的薄片。然后，我们通过使用边界引导的亚纯变换将该模板坐标系变形为目标空间来对齐各个海马体，同时确保层状矢量遵守中轴几何形状的约束。最后,我们根据矢量尖端重建的坐标系和边界表面测量局部厚度和曲率。通过将重建的表面与直接从7T和3TMRI海马中提取的表面进行比较来评估形态测量的准确性。结果表明，ARMM实现了最佳性能，特别是在弯曲的头部，超越了最先进的形态学模型。此外,与基于体积的测量相比，ARMM的形态学测量在区分早期阿尔茨海默病和轻度认知障碍方面表现出更高的辨别能力。总的来说,ARMM在MR图像上提供了海马形态的精确形态评估，并为发现与海马损伤相关的神经变性的潜在图像标记物提供了启示。
