Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network
Jiehui Jiang,
Rong Shi,
Jiaying Lu,
Min Wang,
Qi Zhang,
Shuoyan Zhang,
Luyao Wang,
Ian Alberts,
Axel Rominger,
Chuantao Zuo,
Kuangyu Shi
Affiliations
Jiehui Jiang
Institute 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.
Rong Shi
School of Information and Communication Engineering, Shanghai University, Shanghai, China
Jiaying Lu
Department 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
Min Wang
Institute 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.
Qi Zhang
School of Information and Communication Engineering, Shanghai University, Shanghai, China
Shuoyan Zhang
School of Information and Communication Engineering, Shanghai University, Shanghai, China
Luyao Wang
Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
Ian Alberts
Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
Axel Rominger
Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
Chuantao Zuo
Department 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.
Kuangyu Shi
Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
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.
