General

Tao Sun, Associate professor, obtained his PhD degree in Biomedical Sciences from KU Leuven, Belgium in 2018. From 2018 to 2020, he has been a postdoctoral research fellow in Department of Radiology, Massachusetts General Hospital and Harvard Medical School. He joined in Shenzhen Institute of Advanced Technology in Sep. 2020. His research interests include tomographic imaging techniques, including PET and CT reconstruction, processing and quantification. 

Please find more information in colddie.github.io.

Publications

  1. Chen X, Chen L, Yao W, Zuo Q, Lia Y, Liang D, Wang S, Wang M, Sun T. MR-guided graph learning of 18F-florbetapir PET enables accurate and interpretable Alzheimer’s disease staging. Neuroimage. 2025, online. (corresponding)
  2. Chen R, Li Y, Liang D, Liu J, Sun T. PSMA-PET-Derived Distance Features as Biomarkers for Predicting Outcomes in Primary Prostate Cancer Post-Radical Prostatectomy. Cancer Imaging. 2025, online. (corresponding)
  3. Yuan H, Wang F, Chen Y, Pan X, Zhang Q, Hu Y, Sun T, Jiang L. Total-body kinetic modeling and parametric imaging of ¹³N-NH₃ PET in treatment-naïve lung cancer. Molecular Pharmaceutics. 2025, in press. (corresponding)
  4. Sun L, Wu Y, Yang J, Liang J, Li P, Yu X, Meng N, Sun T, Wang M, Chen C. The Brain-White Adipose Tissue Axis May Play a Crucial Role in Diabetes Mellitus: A Metabolic Network Analysis Using Total-Body PET/CT Imaging. European Journal of Nuclear Medicine and Molecular Imaging. 2025, online. (corresponding)
  5. Yao Z, Wang Y, Wu Y, Zhou J, Dang N, Wang M, Liang Y, Sun T. Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis. European Journal of Nuclear Medicine and Molecular Imaging. 2025, online. (corresponding)
  6. Wang Y, Zhou J, Zhang Z, Liang Y, Sun T. Utilizing temporal information to assess metabolic heterogeneity: a study of 18F-FDG dynamic PET as a treatment response biomarker in small cell lung cancer. Quantitative Imaging in Medicine and Surgery. 2025, online. (corresponding)
  7. Sun L, Wu Y, Sun T et al. Influence of diabetes mellitus on metabolic networks in lung cancer patients: an analysis using dynamic total-body PET/CT imaging. European Journal of Nuclear Medicine and Molecular Imaging. 2025, online.
  8. Wu Y, Sun T, Ng Y, Liu J, Zhu X, Cheng Z, Xu B, Meng N, Zhou Y, Wang M. Clinical Implementation of Total-Body PET in China. Journal of Nuclear Medicine. 2024, 65(Suppl 1):64S–71S.
  9. Chen X, Yao W, Zuo Q, Li Y, Liang D, Zheng H, Wang X, Wang S, Sun T. IG-GCN: Empowering e-Health Services for Alzheimer's Disease Prediction. IEEE Transactions on Consumer Electronics. 2024; 70(4). (corresponding)
  10. Xieraili Wumener, Yarong Zhang, Zihan Zang, Fen Du, Xiaoxing Ye, Maoqun Zhang, Ming Liu, Jiuhui Zhao, Sun T, Ying Liang. The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations. BMC Pulmonary Medicine. 2024; 24:227. (corresponding)
  11. Sun T, Chen R, Liu J, Zhou Y. Current progress and future perspectives in total-body positron emission tomography/computed tomography. Part I: Data processing and analysis. iRadiology. 2024; 2(2):173–90. (corresponding)
  12. Chen R, Sun T, Huang G, Zhou Y, Liu J. Current progress and future perspectives in total-body positron emission tomography/computed tomography. Part II: Clinical applications. iRadiology. 2024; 2(3):328–38. (co-first)
  13. Wang Z, Wu Y, Xia Z, Chen X, Li X, Bai Y, Zhou Y, Liang D, Zheng H, Yang Y, Wang S, Wang M, Sun T. Non-invasive quantification of the brain [18F]FDG-PET using inferred blood input function learned from total-body data with physical constraint. IEEE Transactions on Medical Imaging. 2024; 43(7):2563–2573. (corresponding)
  14. Zhao R, Xia Z, Miao K, Lv J, Zhong H, He Y, Gu D, Liu Y, Zeng G, Zhu L, Alexoff D, Kung H, Wang X, Sun T. Determining the optimal pharmacokinetic modelling and simplified quantification method of [18F]AlF-P16-093 for patients with primary prostate cancer (PPCa). European Journal of Nuclear Medicine and Molecular Imaging. 2024; 51:2124–2133. (corresponding)
  15. Du F, Wumener X, Zhang Y, Zhang M, Zhao J, Zhou J, Li Y, Huang B, Wu R, Xia Z, Yao Z, Sun T, Liang Y. Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions. EJNMMI Physics. 2024; 11(1):23. (corresponding)
  16. Wu Y, Fu F, Meng N, Wang Z, Li X, Bai Y, Zhou Y, Liang D, Zheng H, Yang Y, Wang M, Sun T. The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions. Cancer Imaging. 2023; 42(2). (corresponding)
  17. Ding J, Shen C, Wang Z, Yang Y, El Fakhri G, Lu J, Liang D, Zheng H, Zhou Y, Sun T. Tau-PET abnormality as a biomarker for Alzheimer’s disease staging and early detection: a topological perspective. Cerebral Cortex. 2023; 33(20):10649–10659. (corresponding)
  18. Wumener X, Zhang Y, Wang Z, Zhang M, Zang Z, Huang B, Liu M, Huang S, Huang Y, Wang P, Liang Y, Sun T. Dynamic FDG-PET imaging for differentiating metastatic from nonmetastatic lymph nodes of lung cancer. Frontiers in Oncology. 2022; 12:1005924. (corresponding)
  19. Wang Z, Wu Y, Li X, Bai Y, Chen H, Ding J, Shen C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, Zhou Y, Wang M, Sun T. Comparison between a dual-time-window protocol and other simplified protocols for dynamic total-body 18F-FDG PET imaging. EJNMMI Physics. 2022; 9:63. (corresponding)
  20. Shen C, Wang Z, Chen Z, Bai Y, Li X, Liang D, Liu X, Zheng H, Wang M, Yang Y, Wang H, Sun T. Identifying the mild Alzheimer's disease with 30-min 11C-PiB PET scan. Frontiers in Aging Neuroscience. 2022; 14:785495. (corresponding)
  21. Sun T, Wang Z, Wu Y, Gu F, Li X, Bai Y, Shen C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, El Fakhri G, Zhou Y, Wang M. Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging. European Journal of Nuclear Medicine and Molecular Imaging. 2022; 49:2994–3004.
  22. Sun T, Wu Y, Wei W, Fu F, Meng N, Chen H, Li X, Bai Y, Wang Z, Ding J, Hu D, Chen C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, Zhou Y, Wang M. Motion correction and its impact on quantification in dynamic total-body 18F-Fluorodeoxyglucose PET. EJNMMI Physics. 2022; 9:62.
  23. Sun T, Wu Y, Bai Y, Wang Z, Shen C, Wang W, Li C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, Wang M. An iterative image-based motion compensation method for dynamic brain PET imaging. Physics in Medicine & Biology. 2022; 67(3):035012.
  24. Sun T, Fulton R, Hu Z, Sutiono C, Liang D, Zheng H. Inferring CT perfusion parameters and their uncertainties using a Bayesian approach. Quantitative Imaging in Medicine and Surgery. 2022; 12(1):439–448.
  25. Sun T, Jacobs R, Pauwels RJ, Tijskens E, Fulton RR, Nuyts J. A motion correction approach for oral and maxillofacial cone-beam CT imaging. Physics in Medicine & Biology. 2021; 66:125008.
  26. Sun T, Petibon Y, Han PK, Ma C, Kim SJW, Alpert NM, El Fakhri G, Ouyang J. Body motion detection and correction using list-mode data for cardiac PET. Medical Physics. 2019; 46(11):Sep 11.
  27. Petibon Y, Sun T, Han PK, Ma C, El Fakhri G, Ouyang J. MR-based motion correction of cardiac PET: Evaluation in Static and Dynamic Imaging of Myocardial Glucose Consumption. Physics in Medicine and Biology. 2019; 64(19):195009. (co-first)
  28. Sun T, Clackdoyle R, Kim J, Fulton R, Nuyts J. Estimation of local data-insufficiency in motion-corrected helical CT. IEEE Transactions on Radiation and Plasma Medical Science. 2017; 1(4):346–357.
  29. Sun T, Kim JH, Fulton R, Nuyts J. An iterative projection-based motion estimation and compensation scheme for head X-ray CT. Medical Physics. 2016; 43(10):5705–5716.
  30. Sun T, Wu TH, Wang SJ, Yang BH, Wu NY, Mok GSP. Low dose interpolated average CT for thoracic PET/CT attenuation correction using an active breathing controller. Medical Physics. 2013; 40:102507.
  31. Sun T, Mok GSP. Techniques for respiration-induced artifacts reductions in thoracic PET/CT. Quantitative Imaging in Medicine and Surgery. 2012; 2:46–52.


Conference presentations

  1. X Chen, L Chen, S Wang, M Wang, Sun T, “MRI-informed graph learning on amyloid PET enhances diagnosis and staging of Alzheimer’s disease”, European Association of Nuclear Medicine Annual Congress, Barcelona, Spain, 2025.

  2. X Chen, L Chen, S Wang, M Wang, Sun T, “Low-dose to standard-dose PET reconstruction using cross-slice diffusion transformer”, European Association of Nuclear Medicine Annual Congress, Barcelona, Spain, 2025.

  3. Z Yao, Y Wang, Y Wu, M Wang, Y Liang, Sun T, “Time-series learning for differentiating benign and malignant lung lesions with dynamic [18F]FDG-PET”, European Association of Nuclear Medicine Annual Congress, Barcelona, Spain, 2025.

  4. Z Yao, Y Wang, Y Wu, M Wang, Y Liang, Sun T, “Explainable AI enhances lung cancer diagnosis using dynamic metabolic and perfusion features from [18F]FDG PET/CT”, European Association of Nuclear Medicine Annual Congress, Barcelona, Spain, 2025.

  5. Sun T, Y Wu, M Wang, “Model-free perfusion quantification for dynamic total-body [18F]FDG PET reveals remote blood flow alterations in coronary artery disease”, European Association of Nuclear Medicine Annual Congress, Barcelona, Spain, 2025.

  6. Ruiyue Zhao, Zeheng Xia, Miao Ke, Jie Lv, Huizhen Zhong, Yulu He, Di Gu, Yongda Liu, Guohua Zeng, Lin Zhu, David Alexoff, Hank F. Kung, Xinlu Wang, T Sun, “Determining the simplified quantification method of [18F]AlF-P16-093 for patients with primary prostate cancer”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Toronto, Canada, 2024.

  7. Zeheng Xia, Zhiheng Yao, Yaping Wu, Nan Meng, Yan Bai, Dong Liang, Ye Li, Yongfeng Yang, Meiyun Wang, T Sun, “Comparative between linear least-squares and nonlinear least-squares computation method for regional and voxelized quantitative analysis in total-body dynamic 18F-FDG PET”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Toronto, Canada, 2024.

  8. T Sun, Yaping Wu, Nan Meng, Yan Bai, Dong Liang, Ye Li, Yongfeng Yang, Meiyun Wang, “The biodistribution difference between total-body and conventional whole-body FDG image: does it matter?”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Toronto, Canada, 2024.

  9. Z Wang, S Wang, Y Wu, H Tan, X Chen, Y Yang, D Liang, X Liu, Y Zhou, M Wang, T Sun, “Non-invasive quantification of brain [18F]FDG-PET using inferred blood input function learned from total-body scan data”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Chicago, USA, 2023.

  10. J Ding, C Shen, Z Wang, H Chen, Y Yang, Y Zhou, T Sun, “Tau-PET topological abnormality changes along with Alzheimer’s disease progression”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Chicago, USA, 2023.

  11. Y Wu, F Fu, N Meng, Z Wang, X Li, Y Bai, D Liang, X Liu, Y Yang, Y Zhou, M Wang, T Sun, “Comparison between total-body dynamic and delayed imaging in lesion detection and diagnosis”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Chicago, USA, 2023.

  12. Z Wang, S Wang, Y Wu, H Tan, X Chen, Y Yang, D Liang, X Liu, Y Zhou, M Wang, T Sun, “Non-invasive quantification of brain [18F]FDG-PET using inferred blood input function learned from total-body scan data”, IEEE International Symposium on Biomedical Imaging, Cartagena, Colombia, 2023.

  13. Sun T, Wang Z, Wu Y, Gu F, Li X, Bai Y, Shen C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, El Fakhri G, Zhou Y, Wang M, “Identifying the individual metabolic abnormalities from a systemic perspective using whole-body PET imaging”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Vancouver, Canada, 2022.

  14. Wang Z, Wu Y, Shen C, Chen H, Ding J, Gu F, Li X, Bai Y, Hu Z, Liang D, Liu X, Zheng H, Yang Y, Zhou Y, Wang M, Sun T, “Comparison between dual-time-window protocol with other simplified quantifications in dynamic total-body 18F-FDG PET imaging”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Vancouver, Canada, 2022.

  15. Shen C, Ding J, Chen H, Gu F, Wang Z, Hu Z, Liang D, Liu X, Zheng H, Yang Y, Zhou Y, Wang M, Sun T, “Longitudinal changes of amyloid network during the progression of Alzheimer’s disease”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Vancouver, Canada, 2022.

  16. Wumener X, Sun T, Liang Y, “Parametric dynamic 18F-FDG PET/CT imaging for differentiating positive lymph nodes in lung cancer”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Vancouver, Canada, 2022.

  17. Sun T, Wang Y, Hu Z, Li C, Yang Y, “Tracer kinetics driven motion correction in dynamic PET imaging”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Washington, DC, 2021.

  18. Sun T, Wang Y, Hu Z, Yang Y, Li C, “Peak-Clearance-Rate as index for detection of Alzheimer’s Disease using 11C-PiB PET imaging”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Washington, DC, 2021.

  19. Sang Z, Kuang Z, Lee J, Wang X, Ren N, Wu S, Gao D, Zeng T, Liu Z, Sun T, Hu Z, Li Y, Yang Y, “MRI Compatibility Measurements of SIAT aPET”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Washington, DC, 2021.

  20. Chen Z, Li C, Sun T, WANG Y, Liang D, Liu X, Yang Y, Zheng H, Hu Z, Li K, Cheng Z, Cui X, Duan Y, “Iterative method for the calculation of parametric images from dynamic brain PET images”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Washington, DC, 2021.

  21. Kuang Z, Sang Z, Wang X, Gao D, Ren N, Wu S, Zeng T, Niu M, Cong L, Liu Z, Sun T, Hu Z, Du J, Yang Y, “Design and development of an MRI-compatible human brain PET scanner using dual-ended readout detectors”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, Washington, DC, 2021.

  22. Sun T, Fulton R, “A Bayesian approach for CT perfusion parameter estimation with imperfect measurement”, SPIE Medical Imaging Conference, vol.11600, pp.1160007, 2021.

  23. Sun T, “Peak-Decay-Rate as index for detection of Alzheimer’s Disease using 11C-PiB PET imaging”, SPIE Medical Imaging Conference, vol.11595, pp.1159508, 2021.

  24. Sun T, Fulton R, “Inferring CT perfusion parameters and their uncertainties using a Bayesian approach”, International Symposium on Image Computing and Digital Medicine, 2020.

  25. Sun T, Becker JA, Lois C, Johnson K, El Fakhri G, Ouyang J, “Time-of-flight list-mode based motion correction for 18F-MK6240 PET imaging”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, 2020.

  26. Sun T, Petibon Y, Han PK, Ma C, El Fakhri G, Ouyang J, “MR-based motion correction of cardiac PET: Evaluation in static and dynamic imaging of myocardial glucose consumption”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, 2019.

  27. Kim SJW, Pelletier-Galarneau M, Petibon Y, Sun T, Martineau P, Normandin M, El Fakhri G, Alpert NM, “Measurement of myocardial membrane potential by primed constant infusion”, Society of Nuclear Medicine and Molecular Imaging Annual Meeting, 2019.