First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Department of Neurology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Comput Methods Programs Biomed. 2024 Nov;256:108372. doi: 10.1016/j.cmpb.2024.108372. Epub 2024 Aug 12.
Establishing accurate one-to-one morphological correspondence between different hippocampal surfaces is a solid foundation for the analysis of AD-induced hippocampal morphological changes. However, owing to the large variations between hippocampal surfaces, exiting registration work either fails to obtain the accurate matching of local and overall morphological features or does not preserve the bijectivity during parametric mapping. For this reason, this study proposes a hybrid-feature based spherical quasi-conformal registration (HSQR) method that can effectively maintain the diffeomorphic property while meeting the hybrid-feature matching constraints in the spherical parameter domain.
The HSQR algorithm is primarily achieved through hippocampal surface hybrid feature extraction and spherical quasi-conformal registration. First, hybrid features for a comprehensive morphological description of the hippocampal surface were established, which included essential anatomical features (landmarks) and mean curvature (intensity) features to ensure the accuracy of surface morphology alignment. Second, spherical parameterization was applied to genus-0 closed surfaces, such as the hippocampus, which maximized the preservation of the original local surface morphology through area-preserving properties. Third, a novel spherical quasi-conformal registration algorithm that can handle large deformations is established. It transforms a 3D spherical parameter domain into a 2D plane parameter domain using iterative local stereo projection to improve the efficiency of the registration algorithm. Subsequently, by controlling the Beltramin coefficient, the hybrid morphological features could be aligned while ensuring bijection before and after registration.
Using a cohort including 161 patients with amyloid-β (Aβ) positive Alzheimer disease (AD), 234 Aβ positive mild cognitive impairment (MCI) and 266 Aβ negative cognitively unimpaired (CU) individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we set up the experiment which indicated that the HSQR-based whole bilateral hippocampal atrophy features demonstrated the stronger statistical power for group morphological differences of CU vs. MCI with q-value: 0.0453 for left hippocampus and 0.0401 for right hippocampus and group morphological differences of AD vs. MCI with q-value: 0.0282 for left hippocampus and 0.0421 for right hippocampus.
Our registration algorithm may provide a solid foundation for the accurate quantification of hippocampal surface morphological changes for the differential diagnosis and tracking of AD.
在不同的海马表面之间建立准确的一对一形态对应关系是分析 AD 引起的海马形态变化的坚实基础。然而，由于海马表面之间存在很大的差异，现有的配准工作要么无法获得局部和整体形态特征的精确匹配，要么在参数映射过程中无法保持双射性。为此，本研究提出了一种基于混合特征的球面拟共形配准（HSQR）方法，该方法可以在满足球面参数域中混合特征匹配约束的同时，有效地保持微分同胚性质。
HSQR 算法主要通过海马表面混合特征提取和球面拟共形配准来实现。首先，建立了用于全面描述海马表面形态的混合特征，包括基本解剖特征（标志点）和平均曲率（强度）特征，以确保表面形态对齐的准确性。其次，对属-0 闭合曲面（如海马体）进行球面参数化，通过保面积性质最大限度地保留原始局部表面形态。然后，建立了一种可以处理大变形的新的球面拟共形配准算法，它使用迭代局部立体投影将 3D 球面参数域转换为 2D 平面参数域，以提高配准算法的效率。随后，通过控制 Beltrami 系数，可以在配准前后对齐混合形态特征，同时确保双射性。
利用来自阿尔茨海默病神经影像学倡议（ADNI）数据库的 161 例淀粉样蛋白-β（Aβ）阳性阿尔茨海默病（AD）患者、234 例 Aβ阳性轻度认知障碍（MCI）患者和 266 例 Aβ阴性认知正常（CU）患者的队列，我们进行了实验，结果表明基于 HSQR 的双侧海马整体萎缩特征在 CU 与 MCI 之间的组形态差异（q 值：左海马为 0.0453，右海马为 0.0401）和 AD 与 MCI 之间的组形态差异（q 值：左海马为 0.0282，右海马为 0.0421）方面具有更强的统计能力。
我们的配准算法可以为准确量化海马表面形态变化提供基础，有助于 AD 的鉴别诊断和跟踪。
