Jing Tang

Jing Tang

Associate Professor


PhD: University of Illinois at Urbana-Champaign (Electrical Engineering)

Research and Practice Interests

Medical image formation and understanding; Emission tomography imaging; Machine learning integrated image reconstruction and analysis; Radiation dose reduction; Outcome prediction; Diagnostic and prognostic advancement. 

Research Support

Grant: #R03EB028070 Investigators: Jing Tang, 2019 -2022 NIH, “Dose Reduction in Pediatric/Adolescent Brain PET Imaging through Artificial Neural Networks”, Role: PI, $244,403, Active

Grant: #1454552 Investigators: Jing Tang, 2015 -2021 NSF, "CAREER: Next Generation Positron Emission Tomography Integrated with Magnetic Resonance Imaging", Role: PI, $526,178, Completed

Grant: #1228091 Investigators: Jing Tang, 2012 -2015 NSF, "BRIGE: Magnetic Resonance Imaging Assisted Dynamic Positron Emission Tomography Imaging", Role: PI, $174,648, Completed


Peer Reviewed Publications

B. Yang, X. Wang, A. Li, J. B. Moody, and J. Tang (2021. ) Dictionary learning constrained direct parametric reconstruction in dynamic PET myocardial perfusion imaging. IEEE Tran. Med. Imaging, , 40 (12 ) , 3485 - 3497 More Information

M. P. Adams, A. Rahmim, and J. Tang (2021. ) Improved motor outcome prediction in Parkinson’s disease applying deep learning to DaTscan SPECT images. Comput. Biol. Med., , 132 , 104312 More Information

X. Wang, B. Yang, J. B. Moody, and J. Tang (2020. ) Improved myocardial perfusion PET imaging using artificial neural networks. Phys. Med. Biol., , 65 (14 ) , 145010 More Information

M. R. Salmanpour, M. Shamsaei, A. Saberi, I. S. Klyuzhin, J. Tang, V. Sossi, A. Rahmim (2020. ) Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease. Phys. Medica, , 69 , 233 -240More Information

J. Tang, B. Yang, M. P. Adams, N. N. Shenkov, I. S. Klyuzhin, S. Fotouhi, E. Davoodi-Bojd, L. Lu, H. Soltanian-Zadeh, V. Sossi, and A. Rahmim (2019. ) Artificial neural network-based prediction of outcome in Parkinson’s disease patients using DaTscan SPECT imaging features. Mol. Imaging, Biol., , 21 , 1165 -1173More Information

X. Wang, B. Yang, M. P. Adams, X. Gao, N. A. Karakatsanis, and J. Tang (2018. ) Improved myocardial perfusion PET imaging with MRI assisted reconstruction incorporating multi-resolution joint entropy. Phys. Med. Biol., , 63 (17 ) ,175017 More Information

B. Yang, L. Ying, and J. Tang (2018. ) Artificial neural network enhanced Bayesian PET image reconstruction. IEEE Tran. Med. Imaging, , 37 (6 ) ,1297 -1309More Information

X. Wang, A. Rahmin, and J. Tang (2017. ) MRI assisted dual motion correction for myocardial perfusion defect detection in PET imaging. Med. Phys., , 44 (9 ) , 4536 - 4547 More Information