Ge Wang (Scientist)
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Ge Wang (Chinese: 王 革; born in 1957) is a medical imaging scientist focusing on computed tomography(CT) and artificial intelligence especially deep learning. He is the Clark & Crossan Chair Professor of Biomedical Engineering and the Director of the Biomedical Imaging Center..[1] at Rensselaer Polytechnic Institute, Troy, New York, USA. He is known for his pioneering work on computed tomography and AI-based imaging. He is Fellow of 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).
Career[edit]
Research[edit]
He pioneered the spiral cone-beam CT method in the early 1990s[2]. His work on spiral cone-beam CT solves "the long object problem" (longitudinal data truncation) and has a major impact [3] on the CT field. Michel Defrise[4] et al. wrote 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..”[5] 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..”[6] For this work, he was inducted to National Academy of Inventors in 2019[7]. He and his collaborators published many papers on cone-beam CT including exact cone-beam reconstruction with a general trajectory[8], a quasi-exact triple-source spiral cone-beam reconstruction[9], and more. Currently, there are ~200 million medical CT scans yearly with a majority in this scanning mode[10]
He presented the first deep tomographic imaging roadmap[11] in 2016. With his collaborators, he published a series of papers in this new area of image reconstruction, including major results on deep denoising[12], deep reconstruction[13], and deep radiomics[14] With his coauthors, he published the first book on machine learning-based tomographic reconstruction[15] in 2019 (IOP Publishing; Top download, >33,000 in 2020), and edited the first[16] and second[17] special issues on this theme for IEEE Transactions on Medical Imaging. In partnership with General Electric, Food and Drug Administration, Harvard University, and other research institutions, his team develops deep imaging algorithms and systems for clinical and preclinical applications.
He and his collaborators developed interior tomography to solve “the interior problem” (transverse data truncation)[18] and omni-tomography[19] for spatiotemporal fusion of tomographic modalities with simultaneous CT-MRI[20] as an example. Also, his team developed bioluminescence tomography for optical molecular imaging[21] and spectrography for ultrafast and ultrafine tomography from polychromatic scattering data[22]. He worked on axiomatic bibliometrics[23]. Also, he developed the first undergraduate and graduate courses on deep medical imaging[24] and distanced online testing technology[25]
In addition to many conference/arXiv papers, he has >550 peer-reviewed papers in Nature Machine Intelligence, Nature Communications, Nature, Proceedings of the National Academy of Sciences of the United States of America, and other well-known journals as well as >100 issued and published patents. He has been continuously funded by National Institutes of Health, National Science Foundation, and industry (>$40 million as Principal Investigator (PI)/Contact PI/Multi-PI, and >$30 million as Co-PI/Co-Investigator/Mentor). He gave many seminars, keynotes and plenaries internationally, including the 2021 SPIE O+P Plenary[26] on deep imaging as well as popular science talks on CT in English[27] and Chinese[28] respectively. His TEDEd lesson “How X-rays see through your skin”[29] received >1.5 million views.
Employment[edit]
- Department of Electrical Engineering, University of the Chinese Academy of Sciences, 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
Honors[edit]
Fellowship[edit]
- 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 (IEEE) "for contributions to x-ray tomography", 2003
- Fellow of the International Society for Optical Engineering (SPIE) “for specific achievements in bioluminescence tomography and x-ray computed tomography”, 2007
- Fellow of the Optical Society of America (Optica) “for pioneering contributions to development of bioluminescence tomography”, 2009
- Fellow of the American Association of Physicists in Medicine (AAPM) “for contributions to medical physics”, 2012
- Fellow of the American Association for the Advancement of Science (AAAS) “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
- Fellow of the National Academy of Inventors (NAI)[30] “for contributions to spiral/helical cone-beam/multi-slice CT”, 2019
Awards[edit]
- Giovanni DiChiro Award for Outstanding Scientific Research, Journal of Computer Assisted Tomography, 1997
- AAPM/IPEM Medical Physics Travel Award in USA to lecture in Europe for 2-3 weeks), American Association of Physicists in Medicine and Institute of Physics and Engineering in Medicine, 1999
- Herbert M. Stauffer Award for Outstanding Basic Science Paper in Academic Radiology[31], Association of University Radiologists, USA, 2005
- Dean’s Award for Excellence in Research, College of Engineering, Virginia Tech, 2010
- Barry M. Goldwater Scholarship (Eugene Katsevich as a undergraduate with Princeton University for a paper from his summer intern work in Ge Wang’s lab at Virginia Tech), 2012
- School of Engineering Outstanding Professor Award, Rensselaer Polytechnic Institute, 2018
- IEEE 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[32]
- IEEE Region 1 Outstanding Teaching Award “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[33], 2021
- SPIE Aden & Marjorie Meinel Technology Achievement Award “for contributions in X-ray and optical molecular tomography, including their coupling for biomedical applications”, SPIE, 2022
Alma Mater[edit]
- Xidian University, Bachler of Engineering, China
- Graduate School of Academia Sinica (University of the Chinese Academy of Sciences), Master of Science, China
- University of Buffalo, Master of Science, Doctor of Philosophy, USA
News[edit]
- Reich ES: Three-dimensional technique on trial, Nature, In-Focus News, December 14, 2011
- 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
- Dineley J: Tackling the silent crisis in cancer care, for the Nobel Laureate Meeting, August 1, 2018
- Freeman T: Machine learning for tomographic imaging, Jan. 30, 2020
- Wells T: In era of online learning, new testing method aims to reduce cheating, Science Daily, March 1, 2021
- Hamilton R: Ge Wang receives 2021 EMBS Academic Career Achievement Award, June 17, 2021
- Thomas K: Inventing the future at his AI-based X-ray Imaging System lab, July 9, 2021
- Jacques A: Ge Wang – The SPIE Aden & Marjorie Meinel Technology Achievement Award, January 11, 2022
References[edit]
- ↑ "Ge Wang Profile". Rensselaer Polytechnic Institute, Troy, New York, USA.
- ↑ G. Wang; et al. (1993). "A general cone-beam reconstruction algorithm". IEEE Trans Med Imaging. 12 (3): 486–496. doi:10.1109/42.241876. PMID 18218441.
- ↑ G. Wang; et al. (2007). "Approximate and exact cone-beam reconstruction with standard and non-standard spiral scanning". Phys. Med. Biol. 52 (6): R1–R13. doi:10.1088/0031-9155/52/6/R01. PMID 17327647. Unknown parameter
|s2cid=ignored (help) - ↑ "Michel Defrise". Engineering and Technology History Wiki. 21 January 2022.
- ↑ M. Defrise; et al. (2000). "A solution to the long-object problem in helical cone-beam tomography". Phys. Med. Biol. 45 (3): 623–643. Bibcode:2000PMB....45..623D. doi:10.1088/0031-9155/45/3/305. PMID 10730961. Unknown parameter
|s2cid=ignored (help) - ↑ R. J. La Riviere and C. R. Crawford (2021). "From EMI to AI: a brief history of commercial CT reconstruction algorithms". J. Med. Imaging. 8 (5): 052111. doi:10.1117/1.JMI.8.5.052111. PMC 8492478 Check
|pmc=value (help). PMID 34660842 Check|pmid=value (help). - ↑ T. Wells (2019). "Ge Wang Named a Fellow of the National Academy of Inventors". American Institute for Medical and Biological Engineering (AIMBE).
- ↑ Y. B. Ye and G. Wang (2005). "Filtered backprojection formula for exact image reconstruction from cone-beam data along a general scanning curve". Med. Phys. 32 (1): 42–48. Bibcode:2005MedPh..32...42Y. doi:10.1118/1.1828673. PMID 15719953.
- ↑ Y. Lu; et al. (2009). "Fast exact/quasi-exact FBP algorithms for triple-source helical cone-beam CT". IEEE Trans Med Imaging. 29 (3): 756–770. doi:10.1109/TMI.2009.2035617. PMC 2885857. PMID 19923043.
- ↑ C. Stewart. "Number of computer tomography (CT) scanners in selected countries as of 2019". Statista.
- ↑ G. Wang (2016). "A Perspective on Deep Imaging". IEEE Access. 4: 8914–8924. doi:10.1109/ACCESS.2016.2624938. Unknown parameter
|s2cid=ignored (help) - ↑ H. M. Shan; et al. (2019). "Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction". Nature Machine Intelligence. 1 (6): 269–276. doi:10.1038/s42256-019-0057-9. PMC 7687920 Check
|pmc=value (help). PMID 33244514 Check|pmid=value (help). - ↑ G. Wang; et al. (2020). "Deep learning for tomographic image reconstruction". Nature Machine Intelligence. 2 (12): 737–748. doi:10.1038/s42256-020-00273-z. Unknown parameter
|s2cid=ignored (help) - ↑ H. Q. Chao; et al. (2021). "Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography". Nature Communications. 12 (1): 2963 (10 pages). arXiv:2008.06997. Bibcode:2021NatCo..12.2963C. doi:10.1038/s41467-021-23235-4. PMC 8137697 Check
|pmc=value (help). PMID 34017001 Check|pmid=value (help). - ↑ G. Wang; et al. (2019). Machine Learning for Tomographic Imaging. IOP Publishing. Bibcode:2019mlti.book.....W. Search this book on
- ↑ G. Wang; et al. (2018). "Image Reconstruction is a New Frontier of Machine Learning". IEEE Trans. Med. Imaging. 37 (6): 1289–1296. doi:10.1109/TMI.2018.2833635. PMID 29870359. Unknown parameter
|s2cid=ignored (help) - ↑ G. Wang; et al. (2021). "Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow — Editorial for the 2nd Special Issue "Machine Learning for Image Reconstruction"". IEEE Trans. Med. Imaging. 40 (11): 2956–2964. doi:10.1109/TMI.2021.3115547. Unknown parameter
|s2cid=ignored (help) - ↑ G. Wang; et al. (2013). "The meaning of interior tomography". Phys. Med. Biol. 58 (16): R161–R186. arXiv:1304.7823. Bibcode:2013PMB....58R.161W. doi:10.1088/0031-9155/58/16/R161. PMC 3775479. PMID 23912256.
- ↑ G. Wang; et al. (2012). "Towards Omni-Tomography — Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography". PLOS ONE. 7 (6): e39700. Bibcode:2012PLoSO...739700W. doi:10.1371/journal.pone.0039700. PMC 3387257. PMID 22768108.
- ↑ G. Wang; et al. (2015). "Vision 20/20: Simultaneous CT-MRI--Next chapter of multimodality imaging". Med. Phys. 42 (10): 5879–5889. Bibcode:2015MedPh..42.5879W. doi:10.1118/1.4929559. PMID 26429262.
- ↑ G. Wang; et al. (2006). "In vivo mouse studies with bioluminescence tomography". Opt. Express. 14 (17): 7801–7809. Bibcode:2006OExpr..14.7801W. doi:10.1364/oe.14.007801. PMID 19529149.
- ↑ G. Wang; et al. (2011). "Non-uniqueness and instability of "ankylography"". Nature. 30 (7375): E2–E3. Bibcode:2011Natur.480E...2W. doi:10.1038/nature10635. PMID 22129733. Unknown parameter
|s2cid=ignored (help) - ↑ J. Stallings; et al. (2013). "Determining scientific impact using a collaboration index". Proc. Natl. Acad. Sci. USA. 110 (24): 9680–9685. Bibcode:2013PNAS..110.9680S. doi:10.1073/pnas.1220184110. PMC 3683734. PMID 23720314.
- ↑ C. Wiedeman; et al. (2020). "Innovating the Medical Imaging Course". Technology & Innovation. 21 (4): 1–11. doi:10.21300/21.4.2020.5. Unknown parameter
|s2cid=ignored (help) - ↑ M. Z. Li; et al. (2021). "Optimized collusion prevention for online exams during social distancing". NJP Science of Learning. 5 (1): 5. Bibcode:2021npjSL...6....5L. doi:10.1038/s41539-020-00083-3. PMC 7921656 Check
|pmc=value (help). PMID 33649355 Check|pmid=value (help). - ↑ Thomas, Karen (July 9, 2021). "Inventing the future". SPIE.
- ↑ Wang, Ge (November 11, 2020). "Computed Tomography - Scanning into the Future". YouTube.
- ↑ Wang, Ge (December 2, 2021). "CT – Ideas & Impacts". YouTube.
- ↑ Wang, Ge (June 22, 2015). "How X-rays see through your skin". TEDEd.
- ↑ "About the NAI Fellows".
- ↑ "Academic Radiology". Association of University Radiologists (AUR).
- ↑ "Academic Career Achievement Award".
- ↑ "World Artificial Intelligence Conference". WAIC Scientific Committee.
External Links[edit]
- Ge Wang Profile
- Lab
- Google Scholar
- YouTube
- B Station
- Research.com (In the computer science field, his 2021 rank is #459 internationally and #270 in USA)
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