Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease
BackgroundThis 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 spac...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1621106/full |
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| author | Fenyang Chen Tiantian Heng Qi Feng Rui Hua Jiaojiao Wu Feng Shi Zhengluan Liao Keyin Qiao Zhiliang Zhang Jianliang Miao |
| author_facet | Fenyang Chen Tiantian Heng Qi Feng Rui Hua Jiaojiao Wu Feng Shi Zhengluan Liao Keyin Qiao Zhiliang Zhang Jianliang Miao |
| author_sort | Fenyang Chen |
| collection | DOAJ |
| description | BackgroundThis 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.MethodsA 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.ResultsVB-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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-aa209224f90c4a0c9590cfec3bc82e37 |
| institution | DOAJ |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-aa209224f90c4a0c9590cfec3bc82e372025-08-20T02:40:30ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-07-011710.3389/fnagi.2025.16211061621106Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s diseaseFenyang Chen0Tiantian Heng1Qi Feng2Rui Hua3Jiaojiao Wu4Feng Shi5Zhengluan Liao6Keyin Qiao7Zhiliang Zhang8Jianliang Miao9Department of Medical Imaging, Section One of Air Force Hangzhou Special Crew Sanatorium of PLA AIR Force, Hangzhou, ChinaAir Force Healthcare Center for Special Services, Hangzhou, ChinaDepartment of Radiology, Hangzhou First People’s Hospital Affiliated of Westlake University School of Medicine, Hangzhou, ChinaDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Mental Health, Zhejiang Provincial People’s Hospital, Hangzhou, ChinaDepartment of Medical Imaging, Section One of Air Force Hangzhou Special Crew Sanatorium of PLA AIR Force, Hangzhou, ChinaDepartment of Radiology, Zhejiang Hospital, Hangzhou, ChinaDepartment of Medical Imaging, Section One of Air Force Hangzhou Special Crew Sanatorium of PLA AIR Force, Hangzhou, ChinaBackgroundThis 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.MethodsA 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.ResultsVB-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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1621106/fullAlzheimer’s diseaseamnestic mild cognitive impairmentglymphatic systemV-shape bottleneck networkenlarged perivascular spacediffusion tensor imaging along perivascular spaces |
| spellingShingle | Fenyang Chen Tiantian Heng Qi Feng Rui Hua Jiaojiao Wu Feng Shi Zhengluan Liao Keyin Qiao Zhiliang Zhang Jianliang Miao Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease Frontiers in Aging Neuroscience Alzheimer’s disease amnestic mild cognitive impairment glymphatic system V-shape bottleneck network enlarged perivascular space diffusion tensor imaging along perivascular spaces |
| title | Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease |
| title_full | Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease |
| title_fullStr | Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease |
| title_full_unstemmed | Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease |
| title_short | Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer’s disease |
| title_sort | quantitative assessment of brain glymphatic imaging features using deep learning based epvs segmentation and dti alps analysis in alzheimer s disease |
| topic | Alzheimer’s disease amnestic mild cognitive impairment glymphatic system V-shape bottleneck network enlarged perivascular space diffusion tensor imaging along perivascular spaces |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1621106/full |
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