Ge Wang

Clark & Crossan Endowed Chair Professor and Director of Biomedical Imaging Center
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About

Ge Wang is Clark-Crossan Chair Professor and Director of Biomedical Imaging Center, Rensselaer Polytechnic Institute (RPI), New York, USA.  He pioneered the cone-beam spiral CT method in the early 1990s.  There are ~200 million CT scans yearly, with a majority in this scan mode.  He published the first perspective on deep tomographic imaging in 2016, many follow-up papers and patents.  Currently, deep learning-based imaging has become a mainstream in the medical imaging field.  He is Fellow of IEEE, SPIE, AAPM, OSA, AIMBE, AAAS, and NAI, and received the RPI Wiley Distinguished Faculty Award, IEEE R1 Outstanding Teaching Award, IEEE EMBS Career Achievement Award, SPIE Meinel Technology Award, Sigma Xi Chubb Award for Innovation, and IEEE NPSS Hoffman Medical Imaging Scientist Award, and other distinctions.

Education & Training

Ph.D. Electrical and Computer Engineering, University of Buffalo, New York, USA  
M.S. Remote Sensing, University of Chinese Academy of Sciences, Beijing, China  
B.E. Signal Processing, Xidian University, Xi'an, China

Other affililations: Electrical, Computer, and Systems Engineering, Center for Computational Innovations (CCI), Center for Biotechnology and Interdisciplinary Studies

Research

First paper on spiral/helical cone-beam/multi-slice CT (1991). There are over 200-million CT scans annually worldwide, with a majority in the spiral cone-beam/multi-slice mode. Many follow-up papers along this direction, including superiority of spiral fan-beam CT over step-and-shoot CT (1994) and triple-source helical cone-beam reconstruction (2010)

Bioluminescence tomography (2004) for optical preclinical & biological imaging

Interior tomography (2007) solving the interior problem for targeted imaging at low dose, and fast speed

State-of-the-art multi-scale CT facility (2009) covering six orders of magnitude in terms of image resolution & object size

Omni-tomography (2011) for spatiotemporal fusion of tomographic modalities, with simultaneous CT-MRI as an example

Spectrography (2011) for ultrafast & ultrafine tomography from polychromatic radiation scattering (In Focus News in Nature)

Axiomatic bibliometrics (2013) to credit coauthors (PNAS)

Deep imaging perspective (2016) as the 1st roadmap for this emerging field and a basis for the 1st (2018) and 2nd (2021) dedicated IEEE TMI special issues (over the past 5 years, NIH funding to deep imaging has increased by 3.7X, over $1.1B)

IOP textbook on machine learning for tomographic reconstruction (410 pages, 2019)

National Academy of Inventors (2019, inducted for contributions to spiral cone-beam/multi-slice CT)

Deep denoising network (2019) competitive over commercial low-dose CT (Nature Machine Intelligence)

Deep tomographic reconstruction (2020) (Nature Machine Intelligence)

Deep radiomics (2021) predicting heart diseases with low-dose lung CT (Nature Communications)

IOP Book Series Editor "AI in Biomedicine" (2021-)

Best image clustering results in the world with our SPICE network (https://paperswithcode.com (2021-)

Healthcare metaverse (2022) (Nature Machine Intelligence)

Artificial biological intelligence (2023)  (Nature Machine Intelligence)

First place in the AAPM Deep Generative Modeling Grand Challenge (2023)

First CT multimodal multitask foundation model for specialty-oriented generalist medical AI (SOGMAI) (2024)

Bibliometrics: Wang has >600 journal papers (1 in Nature (also reported in Nature as “In Focus News”), 5 in Nature Machine Intelligence, 1 in Nature Communications, 3 in PNAS, 1 in J of Informetrics (reported in Science and Nature), 1 in Phys. Rev. Letters, and >100 in IEEE journals), Google H-index=90, >100 issued and pending patents, >$40M as PI/Contact PI/MPI, and >$39M as Co-PI/Co-I/Mentor.

Other Focus Areas

CT, Multimodality Imaging, AI, Deep Learning

Teaching

Current Courses
  1. Professor Wang developed the first undergraduate and graduate courses on medical imaging in the deep learning framework and promotes online teaching. He gave numerous talks and seminars internationally, including the 2021 SPIE O+P Plenary on deep imaging and popular science talks on CT and AI. His TEDEd lesson “How X-rays see through your skin” received more than 1.5M views. The book he and his coauthors wrote on "Machine Learning for Tomographic Imaging" is the first in the field and received over 33K downloads in the first three months. More presentations include AI@NIH, MRI, AI-Plenary, AI-KeynoteCT4Layman, AI@Stanford, AI-Stability, SPIE PlenarySPIE Plenary Interview, Shanghai Tech BME, Sigma Xi Chubb Award Speech, IEEE NPSS Medical Imaging Award Speech, YouTube and B-station.

Recognition

Awards & Honors
  1. Fellow of the American Institute for Medical and Biological Engineering “for seminal contributions to the development of single-slice spiral, cone-beam spiral, and micro-CT”, 2002
  2. Fellow of the Institute of Electrical and Electronics Engineers "for contributions to x-ray tomography", 2003
  3. Life-time Fellow of the International Society for Optical Engineering “for specific achievements in bioluminescence tomography and x-ray computed tomography”, 2007
  4. Fellow of the Optical Society of America “for pioneering contributions to development of bioluminescence tomography”, 2009
  5. Fellow of the American Association of Physics in Medicine “for contributions to medical physics”, 2012
  6. Fellow of the American Association for the Advancement of Science “for distinguished contributions to the field of biomedical imaging, particularly for x-ray computed tomography, optical molecular tomography, interior tomography, and multi-modality fusion”, 2014
  7. Life-time Fellow of the National Academy of Inventor “for contributions to spiral/helical cone-beam/multi-slice CT”, 2029
  8. Giovanni DiChiro Award for Outstanding Scientific Research. Journal of Computer Assisted Tomography, 1997
  9. Medical Physics Travel Award. American Association of Physicists in Medicine and Institute of Physics and Engineering in Medicine (1 per year in USA to lecture in Europe for 2-3 weeks to promote interaction between USA and Europe). AAPM/IPEM, 1999
  10. Herbert M. Stauffer Award for Outstanding Basic Science Paper in Academic Radiology. Association of University Radiologists, USA, 2005
  11. Dean’s Award for Excellence in Research. College of Engineering, Virginia Tech, 2010
  12. Goldwater Award (Eugene Katsevich as a mathematics undergraduate with Princeton University for a paper from his summer work in Ge Wang’s lab). Goldwater Education Foundation, 2012
  13. School of Engineering Outstanding Professor Award. Rensselaer Polytechnic Institute, 2018
  14. EEE EMBS Academic Career Achievement Award “for pioneering contributions on cone-beam tomography and deep learning-based tomographic imaging.” IEEE Engineering in Medicine and Biology Society, 2021
  15. Outstanding Teaching Award “for development of the first graduate and undergraduate deep learning-based medical imaging courses at Rensselaer Polytechnic Institute.” IEEE Region 1, 2021
  16. World Artificial Intelligence Conference Youth Outstanding Paper Award “for Shan HM, Padole A, Homayounieh F, Kruger U, Khera RD, Nitiwarangkul C, Kalra MK, Wang G, Nature Machine Intelligence 1:269-276, 2019.” World Artificial Intelligence Conference, 2021
  17. Doctoral Dissertation Award (to Fenglei Fan in recognition of his PhD dissertation entitled “Advanced Neural Networks and Their Interpretation”; Advisor: Ge Wang) as “the best doctoral dissertation in the neural networks, machine learning, and related fields.” International Neural Network Society, 2021
  18. SPIE Aden & Marjorie Meinel Technology Achievement Award “for contributions in X-ray and optical molecular tomography, including their coupling for biomedical applications.” SPIE, 2022
  19. Walston Chubb Award for Innovation “for pioneering contributions to medical imaging and major impacts on research, development, and healthcare, including his cone-beam CT method and AI-based imaging leadership.” Sigma Xi, 2022
  20. William H. Wiley Distinguished Faculty Award "for outstanding teaching, productive research, service to Rensselaer, contributions to professional organizations, and community involvement." Rensselaer Polytechnic Institute, 2023
  21. Edward J. Hoffman Medical Imaging Scientist Award "for pioneering contributions to medical computed tomography, multi-modality imaging, and AI-based tomographic imaging, as well as exemplary mentorship in medical imaging training and education." IEEE NPSS, 2023

Publications

The following is a selection of recent publications in Scopus. Ge Wang has 743 indexed publications in the subjects of Medicine, Computer Science, Engineering.

Yuxuan Liang, Chuang Niu, Pingkun Yan, Ge Wang
Visual Computing for Industry, Biomedicine, and Art
, 7
, 2024
.
Indranil Guha, Syed Ahmed Nadeem, Xiaoliu Zhang, Paul A. DiCamillo, Steven M. Levy, Ge Wang, Punam K. Saha
Medical Physics
, 51
, 2024
, pp.4258-4270
.
Yongshun Xu, Shuo Han, Dayang Wang, Ge Wang, Jonathan S. Maltz, Hengyong Yu
Physics in Medicine and Biology
, 69
, 2024
.
Md Sayed Tanveer, Christopher Wiedeman, Mengzhou Li, Yongyi Shi, Bruno De Man, Jonathan S. Maltz, Ge Wang
Journal of X-ray science and technology
, 32
, 2024
, pp.173-205
.
Jiajin Zhang, Hanqing Chao, Giridhar Dasegowda, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Radiology: Artificial Intelligence
, 6
, 2024
.
Ge Wang
BME Frontiers
, 5
, 2024
.
Alexandre Bousse, Venkata Sai Sundar Kandarpa, Simon Rit, Alessandro Perelli, Mengzhou Li, Guobao Wang, Jian Zhou, Ge Wang
IEEE Transactions on Radiation and Plasma Medical Sciences
, 8
, 2024
, pp.113-137
.
Arjun Krishna, Shanmukha Yenneti, Ge Wang, Klaus Mueller
Medical Physics
, 51
, 2024
, pp.3464-3479
.
Razi Mahmood, Ge Wang, Mannudeep Kalra, Pingkun Yan
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
, 14349 LNCS
, 2024
, pp.214-223
.
Christopher Wiedeman, Peter Lorraine, Ge Wang, Richard Do, Amber Simpson, Jacob Peoples, Bruno De Man
Visual Computing for Industry, Biomedicine, and Art
, 7
, 2024
.

View All Scopus Publications

News

Ge Wang for Wikipedia

Ge Wang’s Biography for Wikipedia

Ge Wang (Chinese name 王革; born in 1957) is a medical imaging scientist focusing on computed tomography (CT), multimodality imaging, and artificial intelligence especially deep learning. He is the Clark & Crossan Chair Professor of Biomedical Engineering and the Director of the Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA. His URLs include Profile; Lab, Google Scholar, YouTube, and B Station.

Career Achievements

Most Impactful Work – Spiral Cone-beam CT

Spiral Cone Beam CT
Spiral cone-beam CT, with simultaneous gantry rotation, data acquisition and patient translation

He pioneered the spiral cone-beam CT method in 1991. His work on spiral cone-beam CT solves "the long object problem" (longitudinal data truncation) and has a major impact on the CT field.  Defrise et alwrote that “to solve the long-object problem, a first level of improvement with respect to the 2D filtered backprojection algorithms was obtained by backprojecting the data in 3D, along the actual measurement rays. The prototype of this approach is the algorithm of Wang et al.” La Riviere and Crawford wrote that “most commercial systems used approximate methods based on extending the Feldkamp–Davis–Kress reconstruction to helical cone-beam scanning trajectories initially formulated by Wang et al.” For this work, he was inducted to the National Academy of Inventors in 2019. He and his collaborators published many papers on spiral cone-beam CT, including  exact cone-beam reconstruction with a general trajectory, a quasi-exact triple-source cone-beam reconstruction, and more. Currently, there are ~200 million medical CT scans yearly with a majority in this scanning mode.

AI-empowered Breakthrough – Deep Tomographic Imaging

In 2016, he presented the first roadmap on deep tomographic imaging. With his collaborators he published a series of papers in this new area of image reconstructionincluding major results on deep denoisingdeep reconstruction, deep radiomics, and healthcare metaverse. With his coauthors, he published the first book on machine learning-based tomographic reconstruction in 2019 (IOP Top Download, more than 33,000 in 2020), and edited two special issues on this theme for IEEE Transactions on Medical Imaging. In partnership with General Electric, FDA and other leading institutions, his team develops deep imaging algorithms and systems for clinical and preclinical applications.

Other Innovations

He and his collaborators developed interior tomography to solve “the interior problem” (transverse data truncation), and omni-tomography (for spatiotemporal fusion of tomographic modalities, with  simultaneous CT-MRI as an example. Also, his team developed bioluminescence tomography for optical molecular imaging and spectrography for ultrafast and ultrafine tomography from polychromatic scattering data. He worked on axiomatic bibliometrics, with results reported in Nature, Science, and news media. Also, he developed the first undergraduate and graduate courses on deep medical imaging and distanced online testing technology.

Societal Fellowships

  • Fellow of the American Institute for Medical and Biological Engineering (AIMBE) “for seminal contributions to the development of single-slice spiral, cone-beam spiral, and micro-CT”, 2002
  • Fellow of the Institute of Electrical and Electronics Engineers "for contributions to x-ray tomography", 2003
  • Life-time Fellow of the International Society for Optical Engineering “for specific achievements in bioluminescence tomography and x-ray computed tomography”, 2007
  • Fellow of the Optical Society of America “for pioneering contributions to development of bioluminescence tomography”, 2009
  • Fellow of the American Association of Physics in Medicine “for contributions to medical physics”, 2012
  • Fellow of the American Association for the Advancement of Science “for distinguished contributions to the field of biomedical imaging, particularly for x-ray computed tomography, optical molecular tomography, interior tomography, and multi-modality fusion”, 2014
  • Life-time Fellow of the National Academy of Inventor “for contributions to spiral/helical cone-beam/multi-slice CT”, 2019

Selected Research and Teaching Awards

  • Fellow of the Institute of Electrical and Electronics Engineers "for contributions to x-ray tomography", 2003
  • Giovanni DiChiro Award for Outstanding Scientific Research, Journal of Computer Assisted Tomography, 1997
  • APPM/IPEM Medical Physics Travel Award, American Association of Physicists in Medicine and Institute of Physics and Engineering in Medicine (one person per year) in USA to lecture in Europe for two to three weeks), 1999
  • Herbert M. Stauffer Award for Outstanding Basic Science Paper in Academic Radiology, Association for University Radiologists, USA, 2005
  • Dean's Award for Excellence in Research, College of Engineering, Virginia Tech, 2010
  • Goldwater Award; Eugene Katsevich as an undergraduate with Princeton University for a paper from his summer intern work in Ge EWang's lab), 2012
  • School of Engineering Outstanding Professor Award (one person per year), Rensselaer Polytechnic Institute, 2018
  • IEEE EMBS Academic Career Achievement Award (one person per year) "for pioneering contributions on cone-beam tomography and deep learning-based tomographic imaging," IEEE Engineering in Medicine and Biology Society, 2021
  • IEEE Region 1 Outstanding Teaching Award (one person per year) "for development of the first graduate and undergraduate deep learning-based medical imaging courses at Rensselaer Polytechnic Institute," IEEE, 2021
  • World Artificial Intelligence Conference Youth Outstanding Paper Award "for Shan HM, Padole A, Homayounieh F, Kruger U, Khera RD, Nitiwarangkul C, Kalra MK, Wang G, Nature Machine Intelligence 1:269-276, 2019," World Artificial Intelligence Conference, 2021
  • SPIE Aden & Marjorie Meinel Technology Achievement Award (one person per year) "for contributions in X-ray and optical molecular tomography, including their coupling for biomedical applications," SPIE, 2022
  • Sigma Xi Walston Chubb Award for Innovation “for pioneering contributions to medical imaging and major impacts on research, development, and healthcare, including his cone-beam CT method and AI-based imaging leadership.” Sigma Xi, 2022
  • William H. Wiley Distinguished Faculty Award (for outstanding teaching, productive research, service to Rensselaer, contributions to professional organizations, and community involvement). Rensselaer Polytechnic Institute, 2023
  • Edward J. Hoffman Medical Imaging Scientist Award "for pioneering contributions to medical computed tomography, multi-modality imaging, and AI-based tomographic imaging, as well as exemplary mentorship in medical imaging training and education." IEEE NPSS, 2023

Publications, Funding, and Presentations

In addition to conference/arXiv papers, he has more than 600 peer-reviewed papers in PNAS, Nature Machine Intelligence, Nature Communications, Nature, and other well-known journals, as well as over 100 issued and pending patents. He has been continuously well-funded by NIH, NSF, and industry (more than $40 million as PI/Contact PI/MPI, and more than $39 million as Co-PI/Co-I/Mentor). He gave numerous seminars, keynotes and plenaries internationally, including the 2021 SPIE O+P Plenary on deep imaging. His TEDEd lesson, "How X-rays see through your skin," received more than 1.8 million views.

Employment

  • Department of Electrical Engineering, Graduate School of Academia Sinica, China;
  • Mallinckrodt Institute of Radiology, Washington University in St. Louis, USA;
  • Department of Radiology, University of Iowa, USA;
  • School of Biomedical Engineering and Sciences, Virginia Tech and Wake Forest University, USA;
  • Department of Biomedical Engineering, Rensselaer Polytechnic Institute, USA

Alma Mater

  • Xidian University, BE, China;
  • Graduate School of Academia Sinica, MS, China;
  • University of Buffalo, MS, PhD, USA

References

  1. Reich ES: Three-dimensional technique on trial, Nature, In-Focus News, December 14, 2011
  2. Wang G., Liu F., Liu FL, Cao GH, Gao H, Vannier MW: Design proposed for a combined MRI/computed-tomography scanner. SPIE Newsroom: 10.1117/2.1201305.004860, 2013
  3. Dineley J: Tackling the silent crisis in cancer care, for the Nobel Laureate Meeting, August 1, 2018
  4. Freeman T: Machine learning for tomographic imaging, Jan. 30, 2020
  5. Wells T: In era of online learning, new testing method aims to reduce cheating, Science Daily, March 1, 2021
  6. Hamilton R: Ge Wang receives 2021 EMBS Academic Career Achievement Award, June 17, 2021
  7. Thomas K. Inventing the future at his AI-based X-ray Imaging System lab, July 9, 2021
  8. Jacques A: Ge Wang – The SPIE Aden & Marjorie Meinel Technology Achievement Award, January 11, 2022
  9. Wang G: Future of Medical Imaging, Nov. 4, 2022
  10. Malatino K: Artificial Biological Intelligence Could Play a Key Role in the Future, June 2, 2023
  11. Malatino K: RPI Professor Is Honored for Medical Imaging Research, Contributes to American Scientist. Oct. 2, 2023
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