Education/CredentialsPhD: University of Illinois at Urbana-Champaign (Electrical Engineering) Contact Information Email tangj6@ucmail.uc.edu Research InterestsMedical image formation and understanding; Emission tomography imaging; Machine learning integrated image reconstruction and analysis; Radiation dose reduction; Outcome prediction; Diagnostic and prognostic advancement. Peer Reviewed Publications A. Li, M. B. Syed, J. B. Moody, and J. Tang 2026. Deep image prior-incorporated direct PET parametric image reconstruction IEEE Trans. Med. Imaging, 45 6, 2738-2749A. Li, M. B. Syed, M. Naganawa, D. Matuskey, R. E. Carson, and J. Tang 2026. Dose reduction in dynamic PET imaging using self-supervised two-step deep image prior IEEE Trans. Radiat. Plasma. Med. Sci., , A. Li, M. Naganawa, P. Honhar, K. Fontaine, P. Gravel, D. Matuskey, R. E. Carson, and J. Tang 2026. Dose reduction for synaptic density PET imaging in Parkinson’s disease NeuroImage, 331 , 121853C. Han, A. T. Trout, A. Li, J. G. Meier, V. P. V. Alves, J. MacLean, N. Abu Ata, Y. Li, S. E. Sharp, C. G. Anton, J. Tang 2026. Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning EJNMMI Phys., , 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, 245006B. 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 Trans. Med. Imaging, 40 12, 3485 - 3497M. 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 , 104312M. 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 , 233X. Wang, B. Yang, J. B. Moody, and J. Tang 2020. Improved myocardial perfusion PET imaging using artificial neural networks Phys. Med. Biol., 65 14, 145010J. 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 , 1165B. Yang, L. Ying, and J. Tang 2018. Artificial neural network enhanced Bayesian PET image reconstruction IEEE Tran. Med. Imaging, 37 6, 1297X. 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, 175017X. 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
Research InterestsMedical image formation and understanding; Emission tomography imaging; Machine learning integrated image reconstruction and analysis; Radiation dose reduction; Outcome prediction; Diagnostic and prognostic advancement.
Peer Reviewed Publications A. Li, M. B. Syed, J. B. Moody, and J. Tang 2026. Deep image prior-incorporated direct PET parametric image reconstruction IEEE Trans. Med. Imaging, 45 6, 2738-2749A. Li, M. B. Syed, M. Naganawa, D. Matuskey, R. E. Carson, and J. Tang 2026. Dose reduction in dynamic PET imaging using self-supervised two-step deep image prior IEEE Trans. Radiat. Plasma. Med. Sci., , A. Li, M. Naganawa, P. Honhar, K. Fontaine, P. Gravel, D. Matuskey, R. E. Carson, and J. Tang 2026. Dose reduction for synaptic density PET imaging in Parkinson’s disease NeuroImage, 331 , 121853C. Han, A. T. Trout, A. Li, J. G. Meier, V. P. V. Alves, J. MacLean, N. Abu Ata, Y. Li, S. E. Sharp, C. G. Anton, J. Tang 2026. Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning EJNMMI Phys., , 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, 245006B. 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 Trans. Med. Imaging, 40 12, 3485 - 3497M. 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 , 104312M. 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 , 233X. Wang, B. Yang, J. B. Moody, and J. Tang 2020. Improved myocardial perfusion PET imaging using artificial neural networks Phys. Med. Biol., 65 14, 145010J. 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 , 1165B. Yang, L. Ying, and J. Tang 2018. Artificial neural network enhanced Bayesian PET image reconstruction IEEE Tran. Med. Imaging, 37 6, 1297X. 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, 175017X. 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
A. Li, M. B. Syed, J. B. Moody, and J. Tang 2026. Deep image prior-incorporated direct PET parametric image reconstruction IEEE Trans. Med. Imaging, 45 6, 2738-2749A. Li, M. B. Syed, M. Naganawa, D. Matuskey, R. E. Carson, and J. Tang 2026. Dose reduction in dynamic PET imaging using self-supervised two-step deep image prior IEEE Trans. Radiat. Plasma. Med. Sci., , A. Li, M. Naganawa, P. Honhar, K. Fontaine, P. Gravel, D. Matuskey, R. E. Carson, and J. Tang 2026. Dose reduction for synaptic density PET imaging in Parkinson’s disease NeuroImage, 331 , 121853C. Han, A. T. Trout, A. Li, J. G. Meier, V. P. V. Alves, J. MacLean, N. Abu Ata, Y. Li, S. E. Sharp, C. G. Anton, J. Tang 2026. Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning EJNMMI Phys., , 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, 245006B. 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 Trans. Med. Imaging, 40 12, 3485 - 3497M. 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 , 104312M. 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 , 233X. Wang, B. Yang, J. B. Moody, and J. Tang 2020. Improved myocardial perfusion PET imaging using artificial neural networks Phys. Med. Biol., 65 14, 145010J. 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 , 1165B. Yang, L. Ying, and J. Tang 2018. Artificial neural network enhanced Bayesian PET image reconstruction IEEE Tran. Med. Imaging, 37 6, 1297X. 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, 175017X. 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