Education
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: #1R01HL170245 - 01A1 Investigators: Jing Tang & Albert Sinusas (MPI), 2024 -2029 NIH, "Novel Methodologies to Improve Coronary Artery Disease Diagnostics with Dynamic PET", Role: PI, $3,380,095, Active
Grant: #R03EB028070 Investigators: Jing Tang, 2019 -2023 NIH, “Dose Reduction in Pediatric/Adolescent Brain PET Imaging through Artificial Neural Networks”, Role: PI, $244,403, Completed
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
Publications
Peer Reviewed Publications
A. Li, B. Yang, M. Naganawa, K. Fontaine, T. Toyonaga, R. E. Carson and J. Tang (2023). Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks. Phys. Med. Biol., 68 (24) , 245006More Information
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 - 3497More 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, 104312More 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) , 145010More 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) , 175017More 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 - 4547More Information
Post Graduate Training and Education
Postdoctoral Fellow, Radiology, Johns Hopkins University, , Baltimore, MD