Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network
Objective: The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heteroge...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811924000880 |
| _version_ | 1827288844529041408 |
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| author | Jiehui Jiang Rong Shi Jiaying Lu Min Wang Qi Zhang Shuoyan Zhang Luyao Wang Ian Alberts Axel Rominger Chuantao Zuo Kuangyu Shi |
| author_facet | Jiehui Jiang Rong Shi Jiaying Lu Min Wang Qi Zhang Shuoyan Zhang Luyao Wang Ian Alberts Axel Rominger Chuantao Zuo Kuangyu Shi |
| author_sort | Jiehui Jiang |
| collection | DOAJ |
| description | Objective: The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. Methods: The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. Results: The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796−0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. Interpretation: The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice. |
| first_indexed | 2024-04-24T11:38:34Z |
| format | Article |
| id | doaj.art-9875606e6c7b49c29f62d031d226cef8 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-24T11:38:34Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-9875606e6c7b49c29f62d031d226cef82024-04-10T04:28:41ZengElsevierNeuroImage1095-95722024-05-01291120593Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial networkJiehui Jiang0Rong Shi1Jiaying Lu2Min Wang3Qi Zhang4Shuoyan Zhang5Luyao Wang6Ian Alberts7Axel Rominger8Chuantao Zuo9Kuangyu Shi10Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China; Corresponding authors at: Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.School of Information and Communication Engineering, Shanghai University, Shanghai, ChinaDepartment of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, ChinaInstitute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China; Corresponding authors at: Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.School of Information and Communication Engineering, Shanghai University, Shanghai, ChinaSchool of Information and Communication Engineering, Shanghai University, Shanghai, ChinaInstitute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, ChinaDepartment of Nuclear Medicine, Inselspital, University of Bern, Bern, SwitzerlandDepartment of Nuclear Medicine, Inselspital, University of Bern, Bern, SwitzerlandDepartment of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China; Corresponding author at: Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China.Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Department of Informatics, Technical University of Munich, Munich, GermanyObjective: The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. Methods: The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. Results: The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796−0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. Interpretation: The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.http://www.sciencedirect.com/science/article/pii/S1053811924000880Alzheimer's diseaseGenerative adversarial networkStandardized uptake value ratio18F-flortaucipir tau Positron emission tomograph18F-florzolotautau Positron emission tomography |
| spellingShingle | Jiehui Jiang Rong Shi Jiaying Lu Min Wang Qi Zhang Shuoyan Zhang Luyao Wang Ian Alberts Axel Rominger Chuantao Zuo Kuangyu Shi Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network NeuroImage Alzheimer's disease Generative adversarial network Standardized uptake value ratio 18F-flortaucipir tau Positron emission tomograph 18F-florzolotautau Positron emission tomography |
| title | Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network |
| title_full | Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network |
| title_fullStr | Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network |
| title_full_unstemmed | Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network |
| title_short | Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network |
| title_sort | detection of individual brain tau deposition in alzheimer s disease based on latent feature enhanced generative adversarial network |
| topic | Alzheimer's disease Generative adversarial network Standardized uptake value ratio 18F-flortaucipir tau Positron emission tomograph 18F-florzolotautau Positron emission tomography |
| url | http://www.sciencedirect.com/science/article/pii/S1053811924000880 |
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