Download Curriculum Vitae

Dr. Yang's professional research career started in the department of radiation oncology at Stanford University and transferred to the radiology department at UCSF. Throughout his professional research career, Dr. Yang has worked extensively in developing novel and clinically applicable radionuclide (PET and SPECT) imaging techniques. At Stanford, he demonstrated the proof-of-principle for PET-guided lung tumor tracking, supported by an initial research grant from RefleXion Medical that commercialized the world first emission-guided radiotherapy system recently. At UCSF, he developed quantitative PET imaging techniques readily applicable to daily clinics and extensively experienced quantitative data analysis using PET/MR data based on collaborations with GE Healthcare, focusing on attenuation correction and motion management. 

In January 2022, Dr. Yang started a faculty position at UT Southwestern (UTSW). He is very excited about this move which provides much more independence and additional resources to conduct translational research. His  particular research interest is, but not limited to, developing practical and efficient solutions for the unmet needs of radionuclide imaging systems, overcoming their current limitations caused by long data acquisition, slow data processing and low spatial resolution. His experience and expertise in oncological application of PET combined with CT and MRI have proven a strong asset to perform data acquisition and preprocessing for deep learning (DL) technology development. For the first time, he developed the key enabling technology of deriving attenuation and scatter corrected PET images directly from noncorrected PET images using a DL algorithm; furthermore, he demonstrated the potential of the DL algorithm for transforming noncorrected SPECT images to attenuation corrected SPECT images in the image space, as an important application for myocardial perfusion imaging (MPI) in stand-alone SPECT systems not combined with a CT.


Research Interest

  • Deep learning


Featured Publications LegendFeatured Publications

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.
Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, Gullberg GT, Seo Y, J Nucl Med 2021 11 62 11 1645-1652
CT-less Direct Correction of Attenuation and Scatter in the Image Space Using Deep Learning for Whole-Body FDG PET: Potential Benefits and Pitfalls.
Yang J, Sohn JH, Behr SC, Gullberg GT, Seo Y, Radiol Artif Intell 2021 Mar 3 2 e200137
Direct Image-Based Attenuation Correction using Conditional Generative Adversarial Network for SPECT Myocardial Perfusion Imaging.
Torkaman M, Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, Gullberg GT, Seo Y, Proc SPIE Int Soc Opt Eng 2021 Feb 11600
Data management and network architecture effect on performance variability in direct attenuation correction via deep learning for cardiac SPECT: A feasibility study
Torkaman M, Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, Gullberg GT, Seo Y IEEE Trans Radiat Plasma Med Sci. 2021
Time of flight PET reconstruction using nonuniform update for regional recovery uniformity.
Kim K, Kim D, Yang J, El Fakhri G, Seo Y, Fessler JA, Li Q Med Phys 2019 Feb 46 2 649-664
Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning.
Wiesinger F, Bylund M, Yang J, Kaushik S, Shanbhag D, Ahn S, Jonsson JH, Lundman JA, Hope T, Nyholm T, Larson P, Cozzini C, Magn Reson Med 2018 10 80 4 1440-1451

Honors & Awards

  • Alavi–Mandell Award
    Journal of Nuclear Medicine (2019)
  • Full Fellowship for MS/PhD
    Kwanjeong Educational Foundation (KEF) (2007-2013)
  • Highest honors
    Yonsei University (2007)

Professional Associations/Affiliations

  • UTSW Biomedical Engineering (2022-2024)