Zhang Jie, Shi Jie, Stonnington Cynthia, Li Qingyang, Gutman Boris A, Chen Kewei, Reiman Eric M, Caselli Richard J, Thompson Paul M, Ye Jieping, Wang Yalin
School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
Dept. of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ.
Med Image Comput Comput Assist Interv. 2016 Oct;9900:326-334. doi: 10.1007/978-3-319-46720-7_38. Epub 2016 Oct 2.
Mild Cognitive Impairment (MCI) is a transitional stage between normal age-related cognitive decline and Alzheimer's disease (AD). Here we introduce a hyperbolic space sparse coding method to predict impending decline of MCI patients to dementia using surface measures of ventricular enlargement. First, we compute diffeomorphic mappings between ventricular surfaces using a canonical hyperbolic parameter space with consistent boundary conditions and surface tensor-based morphometry is computed to measure local surface deformations. Second, ring-shaped patches of TBM features are selected according to the geometric structure of the hyperbolic parameter space to initialize a dictionary. Sparse coding is then applied on the patch features to learn sparse codes and update the dictionary. Finally, we adopt max-pooling to reduce the feature dimensions and apply Adaboost to predict AD in MCI patients ( = 133) from the Alzheimer's Disease Neuroimaging Initiative baseline dataset. Our work achieved an accuracy rate of 96.7% and outperformed some other morphometry measures. The hyperbolic space sparse coding method may offer a more sensitive tool to study AD and its early symptom.
轻度认知障碍（MCI）是正常的年龄相关性认知衰退与阿尔茨海默病（AD）之间的过渡阶段。在此，我们引入一种双曲空间稀疏编码方法，利用脑室扩大的表面测量来预测MCI患者即将出现的向痴呆症的衰退。首先，我们使用具有一致边界条件的规范双曲参数空间计算脑室表面之间的微分同胚映射，并计算基于表面张量的形态测量以测量局部表面变形。其次，根据双曲参数空间的几何结构选择TBM特征的环形补丁来初始化一个字典。然后将稀疏编码应用于补丁特征以学习稀疏代码并更新字典。最后，我们采用最大池化来降低特征维度，并应用Adaboost从阿尔茨海默病神经影像倡议基线数据集中预测MCI患者（n = 133）的AD。我们的工作实现了96.7%的准确率，并且优于其他一些形态测量方法。双曲空间稀疏编码方法可能为研究AD及其早期症状提供一个更敏感的工具。
