Ge Wang (scientist)

Ge Wang
王革
Alma materXidian University (B.E.)
University of the Chinese Academy of Sciences (M.S.)
University at Buffalo (M.S., PhD)
Known forContributions to cone-beam computed tomography and deep learning–based medical imaging
Scientific career
FieldsBiomedical engineering, medical imaging, computed tomography, artificial intelligence
InstitutionsRensselaer Polytechnic Institute
Websitefaculty.rpi.edu/ge-wang

Ge Wang (Chinese: 王 革) is a medical imaging scientist focusing on computed tomography (CT) and artificial intelligence (AI) especially deep learning. He is the Clark & Crossan Chair Professor of Biomedical Engineering and the Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute, Troy, New York, USA.[1] He is known for his research and teaching on CT and AI-based imaging. He is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), Institute of Electrical and Electronics Engineers (IEEE), International Society for Optics and Photonics (SPIE), Optical Society of America (OSA/Optica), American Association of Physicists in Medicine (AAPM), American Association for the Advancement of Science (AAAS), and National Academy of Inventors (NAI). Since January 1, 2025, he has been serving as the Editor-in-Chief of the IEEE Transactions on Medical Imaging.[2]

Education

Wang earned a B.E. in Signal Processing at Xidian University and an M.S. in Remote Sensing at University of the Chinese Academy of Sciences. He was awarded an M.S. and a Ph.D. in Electrical and Computer Engineering from the University at Buffalo.[1]

Research work

Wang, a pioneer in the field of medical imaging, made contributions that initiated the development of the spiral cone-beam computed tomography (CT) during the early 1990s. His work addressed the “long object problem,” which involves longitudinal data truncation in cone-beam CT scans.[3]

To solve the long-object problem, Wang and his collaborators enhanced existing 2D filtered backprojection and Feldkamp–Davis–Kress reconstruction by introducing 3D backprojection along the actual measurement rays from a spiral cone-beam scanning trajectory. This approach, marked the earliest advancement in cone-beam spiral CT. Commercial CT systems adopted and improved the approach proposed by Wang and colleagues.[3]

In recognition of his contributions, Wang was inducted into the National Academy of Inventors in 2019.[4] His research output includes numerous papers on cone-beam CT, covering topics such as exact cone-beam reconstruction with a general trajectory and quasi-exact triple-source spiral cone-beam reconstruction. Notably, about 200 million medical CT scans are performed annually using the cone-beam spiral scanning mode.[5]

After his cone-beam CT work, Wang ventured into deep tomographic imaging.[6] In 2016, he presented the first roadmap for deep imaging, which led to a series of influential papers on deep imaging-based low-dose CT, few-view, reconstruction, artifact reduction, radiomics, foundation models, and healthcare metaverse. His team also authored the first book on machine learning-based tomographic reconstruction. Collaborating with institutions like General Electric, the Food and Drug Administration, Johns Hopkins University, Yale University, and Harvard University, Wang’s group develops cutting-edge imaging algorithms for clinical and preclinical applications.

Wang’s team and collaborators developed interior tomography theory and algorithms, addressing the long-standing “interior problem”. His team also explored omni-tomography for spatiotemporal fusion of tomographic modalities, including the concept of simultaneous CT-MRI. Additionally, Wang and collaborators pioneered bioluminescence tomography for optical molecular imaging and developed spectrography techniques for ultrafast and ultrafine tomography using polychromatic scattering data.[7]

His scholarly output includes over 800 peer-reviewed papers in journals such as Nature, Nature Machine Intelligence, Nature Communications, and Proceedings of the National Academy of Sciences. Wang holds more than 170 issued and published patents. His research has been consistently funded by the National Institutes of Health, the National Science Foundation and General Electric, with total grants exceeding $40 million.[8][9]

Honors

Fellowship

Awards

References

  1. ^ a b "Ge Wang Profile". Rensselaer Polytechnic Institute, Troy, New York, USA.
  2. ^ "Professor Ge Wang Appointed as the Next Editor-in-Chief of IEEE Transactions on Medical Imaging". IEEE TMI.
  3. ^ a b "Inventing the future". Spie.org.
  4. ^ "2019 NAI Fellows Commemorative Book by National Academy of Inventors - Issuu". issuu.com. 2020-03-16. Retrieved 2024-09-12.
  5. ^ "Ulrich Bonse's lasting influence through X-ray interference". Spie.org.
  6. ^ Freeman, Tami (30 January 2020). "Machine learning for tomographic imaging". Physics World.
  7. ^ "Biomedical Imaging Expert Ge Wang Joins Rensselaer". Newswise.
  8. ^ "Proposed next generation nano-computed tomography system will enhance nanoscale research". Vt.edu.
  9. ^ "New patented technology for improving cardiac CTs receives NIH support". EurekAlert!.
  10. ^ "Ge Wang, Ph.D. COF-1049". AIMBE.
  11. ^ "Ge Wang". IEEE.
  12. ^ "Prof. Ge Wang". SPIE.
  13. ^ "2010 Fellows". Optica.
  14. ^ "AAPM History and Heritage - Fellows". AAPM.
  15. ^ "Ge Wang elected as AAAS Fellow | Biomedical Engineering". Rensselaer Polytechnic Institute.
  16. ^ "COMPLETE LIST OF CURRENT NAI FELLOWS" (PDF). National Academy of Inventors.
  17. ^ Elster, Allen D. (March–April 1998). "The "Giovanni Di Chiro Awards" for Outstanding Scientific Research Published in the Journal of Computer Assisted Tomography, 1997". Journal of Computer Assisted Tomography. 22 (2): 9. doi:10.1097/00004728-199803000-00002.
  18. ^ "AAPM AAPM/IPEM Travel Award (TA) Recipients". AAPM.
  19. ^ "1999 AAPM Award Winners". Medical Physics. 26 (10): 2206–2217. October 1999. Bibcode:1999MedPh..26.2206.. doi:10.1002/j.2473-4209.1999.tb00836.x.
  20. ^ "Ge Wang named Samuel Reynolds Pritchard Professor of Engineering". Virginia Tech Magazine.
  21. ^ "Excellence in Research". Virginia Tech.
  22. ^ "Academic Career Achievement Award". Archived from the original on 20 June 2021.
  23. ^ "Meet the 2022 SPIE Award Recipients". Spie.org.
  24. ^ "The Walston Chubb Award for Innovation". Sigma Xi.
  25. ^ "Ge Wang selected for 2023 Edward J Hoffman Medical Imaging Scientist Award | Biomedical Engineering". Rensselaer Polytechnic Institute.
  26. ^ "Ge Wang Awarded AAPM Edith H. Quimby Lifetime Achievement Award | Biomedical Engineering". RPI. Retrieved 5 May 2025.
  27. ^ Ioannidis, John P. A. (19 September 2025). "August 2025 data-update for "Updated science-wide author databases of standardized citation indicators"". Elsevier Data Repository. 8. doi:10.17632/btchxktzyw.8.
  28. ^ "RPI Faculty Among World's Most-Cited Researchers". Rensselaer Polytechnic Institute.
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