Ge Wang

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

Ge Wang is the Clark & Crossan Endowed Chair Professor and the Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute. His interests include medical imaging and artificial intelligence especially deep learning. Wang published the first spiral cone-beam/multi-slice CT method in 1991 and has systematically contributed many papers and patents in this area. Also, his group and collaborators developed interior tomography theory to solve the long-standing interior problem, and initiated research of omni-tomography with simultaneous CT-MRI as an example. In 2016, he published the first perspective on deep learning-based tomographic imaging, lead-edited the first and second IEEE TMI special issues on deep reconstruction, and with his coauthors wrote the first book on deep learning-based tomography and a series of papers on various deep imaging topics. His results were featured in Nature, Science, PNAS, and news media. He received various major societal awards, and is Fellow of IEEE, SPIE, AAPM, OSA, AIMBE, AAAS, and NAI.

Education & Training

University of Buffalo
Ph.D. Electrical and Computer Engineering

University of Chinese Academy of Sciences
M.S. Remote Sensing

Xidian University
B.E. Signal Processing

Other affililations: Electrical, Computer, and Systems Engineering

Research

First paper on spiral/helical cone-beam/multi-slice CT (1991) solving "the long object problem" (longitudinal data truncation). There are ~200-million CT scans annually worldwide, with a majority in the spiral cone-beam/multi-slice mode. Many follow-up papers of ours 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 (transverse data truncation) 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 a basis for the first (2018) and second TMI Special Issues (2021) in this emerging field

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 result in the world with our SPICE network (https://paperswithcode.com; 2021)

Various academic awards, including IEEE EMBS Academic Career Achievement Award (2021) , IEEE Region 1 Outstanding Teaching Award (2021) , and SPIE Aden & Marjorie Meinel Technology Achievement Award (2022)

Data: In addition to many conference/arXiv papers, Wang has >500 journal papers (1 in Nature (also reported in Nature as “In Focus News”), 2 in Nature Machine Intelligence, 1 in Nature Communications, 3 in PNAS, 1 in J of Informetrics (reported in Science and Nature respectively), 1 in Phys. Rev. Letters, and >80 in IEEE journals), Google h-index=79, >100 issued and pending patents, >$40M as PI/Contact PI/MPI, and >$30M as Co-PI/Co-I/Mentor

Stories: AI@NIH ('18), MRI, AI-Plenary, AI-KeynoteCT4Layman, AI@Stanford, AI-Stability, SPIE PlenarySPIE Plenary Interview, Shanghai Tech BME. To watch more talks, please visit his YouTube channel

Other Focus Areas

X-ray computed tomography, Optical molecular tomography, Magnetic resonance imaging, Multimodality imaging, Artificial intelligence (Deep learning), Radiomics

Teaching

Current Courses

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 (in English and Chinese respectively). His TEDEd lesson “How X-rays see through your skin” received more than 1.5M views.

Recognition

Awards & Honors
  1. Giovanni DiChiro Award for Outstanding Scientific Research, Journal of Computer Assisted Tomography, 1997
  2. AAPM/IPEM 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), 1999
  3. Herbert M. Stauffer Award for Outstanding Basic Science Paper in Academic Radiology, Association of University Radiologists, USA, 2005
  4. Dean’s Award for Excellence in Research, College of Engineering, Virginia Tech, 2010
  5. Goldwater Award (Eugene Katsevich as a mathematics undergraduate with Princeton University for a paper from his summer work in Ge Wang’s lab; https://en.wikipedia.org/wiki/Barry_M._Goldwater_Scholarship), 2012
  6. School of Engineering Outstanding Professor Award, Rensselaer Polytechnic Institute, 2018
  7. 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
  8. IEEE Region 1 Outstanding Teaching Award (1 per year) “for development of the first graduate and undergraduate deep learning-based medical imaging courses at Rensselaer Polytechnic Institute.” IEEE Region 1, 2021
  9. 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
  10. SPIE Aden & Marjorie Meinel Technology Achievement Award (1 per year) “for contributions in X-ray and optical molecular tomography, including their coupling for biomedical applications.” SPIE, 2022
  11. 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
  12. Fellow of the Institute of Electrical and Electronics Engineers "for contributions to x-ray tomography"), 2003
  13. Life-time Fellow of the International Society for Optical Engineering “for specific achievements in bioluminescence tomography and x-ray computed tomography”, 2007
  14. Fellow of the Optical Society of America “for pioneering contributions to development of bioluminescence tomography”, 2009
  15. Fellow of the American Association of Physics in Medicine “for contributions to medical physics”, 2012
  16. 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
  17. Life-time Fellow of the National Academy of Inventor “for contributions to spiral/helical cone-beam/multi-slice CT”, 2029

 

Publications

The following is a selection of recent publications in Scopus. Ge Wang has 670 indexed publications in the subjects of Engineering, Physics and Astronomy, and Computer Science.

Wenxiang Cong, Bruno De Man, Ge Wang
Journal of X-ray science and technology
, 30
, 2022
, pp.725-736
.
Wenxiang Cong, Mengzhou Li, Xiaodong Guo, Ge Wang
Journal of the Optical Society of America A: Optics and Image Science, and Vision
, 39
, 2022
, pp.841-846
.
Huidong Xie, Stephanie Thorn, Xiongchao Chen, Bo Zhou, Hui Liu, Zhao Liu, Supum Lee, Ge Wang, Yi Hwa Liu, Albert J. Sinusas, Chi Liu
Journal of Nuclear Cardiology
, 2022
.
Haoyuan Chen, Chen Li, Ge Wang, Xiaoyan Li, Md Mamunur Rahaman, Hongzan Sun, Weiming Hu, Yixin Li, Wanli Liu, Changhao Sun, Shiliang Ai, Marcin Grzegorzek
Pattern Recognition
, 130
, 2022
.
Jason Z. Zhang, Halemane Ganesh, Flavius D. Raslau, Rashmi Nair, Edward Escott, Chi Wang, Ge Wang, Jie Zhang
Physics in Medicine and Biology
, 67
, 2022
.
Chuang Niu, Wenxiang Cong, Feng Lei Fan, Hongming Shan, Mengzhou Li, Jimin Liang, Ge Wang
IEEE Transactions on Radiation and Plasma Medical Sciences
, 6
, 2022
, pp.656-666
.
Christopher Wiedeman, Ge Wang
Patterns
, 3
, 2022
.
Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shaoyu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang
Patterns
, 3
, 2022
.
Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shaoyu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang
Patterns
, 3
, 2022
.
Yutong Chen, Carola Bibiane Schonlieb, Pietro Lio, Tim Leiner, Pier Luigi Dragotti, Ge Wang, Daniel Rueckert, David Firmin, Guang Yang
Proceedings of the IEEE
, 110
, 2022
, pp.224-245
.

View All Scopus Publications

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, and deep radiomics. 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 bibliometricswith 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

Publications, Funding, and Presentations

In addition to conference/arXiv papers, he has more than 500 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 $30 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 and popular science talks on CT in English and Chinese, respectively. His TEDEd lesson, "How X-rays see through your skin," received more than 1.5 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
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