Quantitative Assessment of Brain glymphatic Imaging Features Using Deep Learning-Based EPVS Segmentation and DTI-ALPS Analysis in Alzheimer's Disease

Chen Fen Yang¹Tiantian Heng²Qi Feng³Rui Hua⁴Feng Shi⁵Zhengluan Liao⁶Keyin Qiao¹Zhiliang Zhang^7*Jianliang Miao¹

• ¹Department of Medical Imaging, Section One of Air Force Hangzhou Special Crew Sanatorium of PLA AIR Force, Hangzhou 310013, Hangzhou, China
• ²Air Force Healthcare Center for Special Services, Hangzhou, China., Hangzhou, China
• ³Department of Radiology, Affiliated Hangzhou First People's Hospital , School Of Medicine, Westlake University., Hangzhou, China
• ⁴Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, ChinaH, Hangzhou, China
• ⁵Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China, Shanghai, China
• ⁶Department of Mental Health, Zhejiang Provincial People's Hospital, China., Hangzhou, China
• ⁷Department of Radiology, Zhejiang Hospital, Hangzhou , China, Hangzhou, China

Background: 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.Methods: 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.Results: 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 P < 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 aeeseement of glymphatic system alterations, offering potential value for early diagnosis and translation in neurodegenerative diseases.