User:Gwshenyang/sandbox

Ge Wang (Chinese: 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 at Rensselaer Polytechnic Institute, Troy, New York, USA. He is widely known for his pioneering work on spiral cone-beam CT and deep learning-based tomographic reconstruction. He is Fellow of AIMBE, IEEE, SPIE, OSA, AAPM, AAAS, and National Academy of Inventors.

Most Impactful Work – Spiral Cone-beam CT

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 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.” 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 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 spiral 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 reconstruction, including major results on deep denoising, deep reconstruction, and deep radiomics. With his coauthors, he published the first book on machine learning based tomographic reconstruction in 2019 (IOP Top Download, >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 Fellowship

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 & Teaching Awards

Giovanni DiChiro Award for Outstanding Scientific Research, Journal of Computer Assisted Tomography, 1997
AAPM/IPEM Medical Physics Travel Award, American Association of Physicists in Medicine and Institute of Physics and Engineering in Medicine in USA to lecture in Europe for 2-3 weeks), 1999
Herbert M. Stauffer Award for Outstanding Basic Science Paper in Academic Radiology, Association of University Radiologists, USA, 2005
Dean’s Award for Excellence in Research, College of Engineering, Virginia Tech, 2010
Goldwater Award (Eugene Katsevich as a undergraduate with Princeton University for a paper from his summer intern work in Ge Wang’s lab), 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
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, 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

Publications, Funding, & Presentations

In addition to conference/arXiv papers, he has >500 peer-reviewed papers in PNAS, Nature Machine Intelligence, Nature Communications, Nature, and other well-known journals as well as >100 issued and published patents.
He has been continuously well-funded by NIH, NSF, and industry (>$40 millions as PI/Contact PI/MPI, and >$30 millions 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 >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

External links

Category:Computed tomography (CT) Category:magnetic resonance imaging (MRI) Category:optical molecular tomograph Category:multimodality imaging Category:artificial intelligence Category:machine learning Category:deep learning