1.      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, online. (corresponding)

2.      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, online. (co-first)

3.      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, online. (corresponding)

4.      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, online. (corresponding)

5.      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)

6.      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)

7.      Ding J, Shen CS, Wang ZG, Yang YF, El Fakhri G, Lu J, Liang D, Zheng HR, Zhou Y, Sun T, “Tau-PET abnormality as a biomarker for Alzheimer’s disease staging and early detection: a topological perspective”, Cerebral Cortex, 33(20):10649-10659, 2023. (corresponding)

8.      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, 12:1005924, 2022. (corresponding)

9.      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, 9(63), 2022. (corresponding)

10.   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, 14(785495), 2022. (corresponding)

11.   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, 49, 2994–3004, 2022.

12.   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, 9(62), 2022.

13.   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, 67(3):035012, 2022.

14.   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, 12(1):439-448, 2022.

15.   Sun T, Jacobs R, Pauwels Ruben J, Tijskens E, Fulton Roger R, Nuyts, J. “A motion correction approach for oral and maxillofacial cone-beam CT imaging”, Physics in Medicine & Biology, (66): 125008, 2021.

16.   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, Sep 11, 46(11), 2019.

17.   Petibon Y, Sun T*, Han PK, Ma C, El Fakhri G and 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 Oct 4;64(19):195009 （co-first）.

18.   Sun T, Clackdoyle R, Kim J, Fulton R, and Nuyts J, “Estimation of local data-insufficiency in motion- corrected helical CT,” IEEE Transaction on Radiation and Plasma Medicine Science, vol. 1, no. 4, pp. 346-357, July 2017.

19.   Sun T, Kim JH, Fulton R, and Nuyts J, “An iterative projection-based motion estimation and compensation

scheme for head X-ray CT,” Medical Physics, vol. 43, no. 10, pp. 5705–5716, 2016.

20.   Sun T, Wu TH, Wang SJ, Yang BH, Wu NY, and Mok GSP. “Low dose interpolated average CT for thoracic

PET/CT attenuation correction using an active breathing controller”. Medical Physics, vol.40, pp.102507, 2013.

21.   Sun T and Mok GSP, "Techniques for respiration-induced artifacts reductions in thoracic PET/CT". Quantitative Imaging in Medicine and Surgery, vol. 2, pp. 46-52, 2012. 

1.    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 infered blood input function learned from total-body scan data”, Society of Nuclear Medicine & Molecular Imaging Meeting (SNMMI), Chicago, United States, 2023.

2.     J Ding, C Shen, Z Wang, H Chen, Y Yang, Y Zhou, T Sun, “Tau-PET topological abnormity changes along with Alzheimer;s disease progression”, Society of Nuclear Medicine & Molecular Imaging Meeting (SNMMI), Chicago, United States, 2023.

3.     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 & Molecular Imaging Meeting (SNMMI), Chicago, United States, 2023.

4.    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 infered blood input function learned from total-body scan data”, IEEE International Symposium on Biomedical Imaging (ISBI) , Cartagena, Columbia, 2023.

5.      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 systematic perspective using whole-body PET imaging”, Society of Nuclear Medicine & Molecular Imaging Meeting (SNMMI), Vancouver, Canada, 2022.

6.    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 & Molecular Imaging Meeting (SNMMI), Vancouver, Canada, 2022. 

7.    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 & Molecular Imaging Meeting (SNMMI), Vancouver, Canada, 2022. 

8.    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 & Molecular Imaging Meeting (SNMMI), Vancouver, Canada, 2022.

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

10.     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 & Molecular Imaging Meeting, Washington, DC, 2021.

11.     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 & Molecular Imaging Meeting, Washington, DC, 2021.

12.     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”, 2021 Society of Nuclear Medicine & Molecular Imaging Meeting, Washington, DC, 2021.

13.     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 by Using Dual-ended Readout Detectors”, Society of Nuclear Medicine & Molecular Imaging Meeting, Washington, DC, 2021.

14.     Sun T, Fulton R, "A Bayesian approach for CT perfusion parameters estimation with imperfect measurement", SPIE Medical Imaging Conference, vol.11600, pp.1160007, 2021.

15.     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.

16.     Sun T, Fulton R, "Inferring CT perfusion parameters and their uncertainties using a Bayesian approach", The Fourth International Symposium on Image Computing and Digital Medicine, 2020.

17.     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 & Molecular Imaging Meeting 2020.

18.  Sun T, Petibon Y, Han PK, Ma C, El Fakhri G and Ouyang J, “MR-based motion correction of cardiac PET: Evaluation in Static and Dynamic Imaging of Myocardial Glucose Consumption,” presented at Society of Nuclear Medicine & Molecular Imaging Meeting 2019.

19.  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,” presented at Society of Nuclear Medicine & Molecular Imaging Meeting 2019.