Chen Fenyang, Heng Tiantian, Feng Qi, Hua Rui, Wu Jiaojiao, Shi Feng, Liao Zhengluan, Qiao Keyin, Zhang Zhiliang, Miao Jianliang
Department of Medical Imaging, Section One of Air Force Hangzhou Special Crew Sanatorium of PLA AIR Force, Hangzhou, China.
Air Force Healthcare Center for Special Services, Hangzhou, China.
Front Aging Neurosci. 2025 Jul 16;17:1621106. doi: 10.3389/fnagi.2025.1621106. eCollection 2025.
This study aimed to quantitatively evaluate brain glymphatic imaging features in patients with Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI), and normal controls (NC) by applying a deep learning-based method for the automated segmentation of enlarged perivascular space (EPVS) and diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) indices.
A total of 89 patients with AD, 24 aMCI, and 32 NCs were included. EPVS were automatically segmented from T1WI and T2WI images using a VB-Net-based model. Quantitative metrics, including total EPVS volume, number, and regional volume fractions were extracted, and segmentation performance was evaluated using the Dice similarity coefficient. Bilateral ALPS indices were also calculated. Group comparisons were conducted for all imaging metrics, and correlations with cognitive scores were analyzed.
VB-Net segmentation model demonstrated high accuracy, with mean Dice coefficients exceeding 0.90. Compared to the NC group, both AD and aMCI groups exhibited significantly increased EPVS volume, number, along with reduced ALPS indices (all < 0.05). Partial correlation analysis revealed strong associations between ALPS and EPVS metrics and cognitive performance. The combined imaging features showed good discriminative performance among diagnostic groups.
The integration of deep learning-based EPVS segmentation and DTI-ALPS analysis enables multidimensional assessment of glymphatic system alterations, offering potential value for early diagnosis and translation in neurodegenerative diseases.
本研究旨在通过应用基于深度学习的方法对扩大的血管周围间隙（EPVS）进行自动分割以及沿血管周围间隙的扩散张量成像分析（DTI-ALPS）指数，定量评估阿尔茨海默病（AD）、遗忘型轻度认知障碍（aMCI）患者和正常对照（NC）的脑类淋巴系统成像特征。
共纳入89例AD患者、24例aMCI患者和32例NC。使用基于VB-Net的模型从T1WI和T2WI图像中自动分割EPVS。提取包括EPVS总体积、数量和区域体积分数在内的定量指标，并使用Dice相似系数评估分割性能。还计算双侧ALPS指数。对所有成像指标进行组间比较，并分析与认知评分的相关性。
VB-Net分割模型显示出高准确性，平均Dice系数超过0.90。与NC组相比，AD组和aMCI组的EPVS体积和数量均显著增加，同时ALPS指数降低（均P<0.05）。偏相关分析显示ALPS和EPVS指标与认知表现之间存在强关联。联合成像特征在诊断组之间显示出良好的鉴别性能。
基于深度学习的EPVS分割和DTI-ALPS分析的整合能够对类淋巴系统改变进行多维度评估，为神经退行性疾病的早期诊断和转化提供潜在价值。
