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Zhang, Miao; Qian, Xiao-Hang; Hu, Jialin; Zhang, Yaoyu; Lin, Xiaozhu; Hai, Wangxi; Shi, Kuangyu; Jiang, Xufeng; Li, Yao; Tang, Hui-Dong; Li, Biao (2024). Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer's disease. European journal of nuclear medicine and molecular imaging, 51(2), pp. 455-467. Springer 10.1007/s00259-023-06446-3
Zhou, Bo; Xie, Huidong; Liu, Qiong; Chen, Xiongchao; Guo, Xueqi; Feng, Zhicheng; Hou, Jun; Zhou, S Kevin; Li, Biao; Rominger, Axel; Shi, Kuangyu; Duncan, James S; Liu, Chi (2023). FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising. Medical image analysis, 90(102993), p. 102993. Elsevier 10.1016/j.media.2023.102993
Zhang, Min; Quan, Weiwei; Zhu, Tianqi; Feng, Shuo; Huang, Xinyun; Meng, Hongping; Du, Run; Zhu, Zhengbin; Qu, Xuezheng; Li, Ping; Cui, Yuke; Shi, Kuangyu; Yan, Xiaoxiang; Zhang, Ruiyan; Li, Biao (2023). [68Ga]Ga-DOTA-FAPI-04 PET/MR in patients with acute myocardial infarction: potential role of predicting left ventricular remodeling. European journal of nuclear medicine and molecular imaging, 50(3), pp. 839-848. Springer 10.1007/s00259-022-06015-0
Guo, Rui; Xue, Song; Hu, Jiaxi; Sari, Hasan; Mingels, Clemens; Zeimpekis, Konstantinos; Prenosil, George; Wang, Yue; Zhang, Yu; Viscione, Marco; Sznitman, Raphael; Rominger, Axel; Li, Biao; Shi, Kuangyu (2022). Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction. Nature Communications, 13(1), p. 5882. Springer Nature 10.1038/s41467-022-33562-9
Xue, Song; Guo, Rui; Bohn, Karl Peter; Matzke, Jared; Viscione, Marco; Alberts, Ian; Meng, Hongping; Sun, Chenwei; Zhang, Miao; Zhang, Min; Sznitman, Raphael; El Fakhri, Georges; Rominger, Axel; Li, Biao; Shi, Kuangyu (2022). A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET. European journal of nuclear medicine and molecular imaging, 49(6), pp. 1843-1856. Springer-Verlag 10.1007/s00259-021-05644-1
Xue, Song; Bohn, Karl Peter; Guo, Rui; Sari, Hasan; Viscione, Marco; Rominger, Axel; Li, Biao; Shi, Kuangyu (2021). Development of a deep learning method for CT-free correction for an ultra-long axial field of view PET scanner. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021, pp. 4120-4122. IEEE 10.1109/EMBC46164.2021.9630590
Guo, Rui; Hu, Xiaobin; Song, Haoming; Xu, Pengpeng; Xu, Haoping; Rominger, Axel; Lin, Xiaozhu; Menze, Bjoern; Li, Biao; Shi, Kuangyu (2021). Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. European journal of nuclear medicine and molecular imaging, 48(10), pp. 3151-3161. Springer 10.1007/s00259-021-05232-3
Hu, Xiaobin; Guo, Rui; Chen, Jieneng; Li, Hongwei; Waldmannstetter, Diana; Zhao, Yu; Li, Biao; Shi, Kuangyu; Menze, Bjoern (2020). Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images. IEEE journal of biomedical and health informatics, 24(9), pp. 2599-2608. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2020.2972694
Guo, Rui; Xu, Pengpeng; Cheng, Shu; Lin, Mu; Zhong, Huijuan; Li, Weixia; Huang, Hengye; Ouyang, Bingsheng; Yi, Hongmei; Chen, Jiayi; Lin, Xiaozhu; Shi, Kuangyu; Zhao, Weili; Li, Biao (2020). Comparison of Nasopharyngeal MR, 18 F-FDG PET/CT, and 18 F-FDG PET/MR for Local Detection of Natural Killer/T-Cell Lymphoma, Nasal Type. Frontiers in oncology, 10, p. 576409. Frontiers Research Foundation 10.3389/fonc.2020.576409
