Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China.
Hum Brain Mapp. 2024 Jul 15;45(10):e26715. doi: 10.1002/hbm.26715.
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.
针对痴呆症早期检测的局部海马体萎缩研究受到了广泛关注。然而，由于缺乏与海马头部等复杂弯曲区域的一致生物学对应关系，现有的形态学方法在准确量化细微萎缩方面仍然具有挑战性。因此，本文提出了一种创新的基于轴的形态计量模型（ARMM），该模型遵循海马体的解剖层状组织，捕捉其精确和一致的纵向弯曲轨迹。具体来说，我们基于整个弯曲的纵向轴和正交分布的层状结构，在 7T 离体海马体图谱的基础上建立了一个“基于轴的坐标系”。然后，我们使用边界引导的变形将模板坐标系变形到目标空间，从而对齐个体的海马体，同时确保层状向量符合中轴几何的约束。最后，我们基于坐标系统和从向量尖端重建的边界表面来测量局部厚度和曲率。通过将重建表面与直接从 7T 和 3T MRI 海马体中提取的表面进行比较，评估形态计量的准确性。结果表明，ARMM 实现了最佳性能，特别是在弯曲的头部，优于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具有更高的区分能力。总体而言，ARMM 提供了一种在 MR 图像上对海马体形态进行精确形态计量评估的方法，为发现与海马体损伤相关的神经退行性疾病的潜在图像标志物提供了新的思路。
