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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 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).

Research work
Wang, a pioneer in the field of medical imaging, made significant contributions to the development of spiral cone-beam computed tomography (CT) during the early 1990s. His work addressed the “long object problem,” which involves longitudinal data truncation in CT scans. This innovation had a profound impact on the CT field.

To overcome the long-object problem, Wang and his collaborators improved upon existing 2D filtered backprojection algorithms by introducing 3D backprojection along the actual measurement rays. Their approach, known as the Wang algorithm, marked a crucial advancement in spiral cone-beam CT. Commercial CT systems widely adopted similar methods based on the Feldkamp–Davis–Kress reconstruction, initially proposed by Wang and colleagues.

In recognition of his contributions, Wang was inducted into the National Academy of Inventors in 2019. 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, approximately 200 million medical CT scans are performed annually using this scanning mode.

Beyond cone-beam CT, Wang ventured into deep tomographic imaging. In 2016, he presented the first roadmap for deep imaging, which led to a series of influential papers on deep denoising, reconstruction, and radiomics. His team also authored the first book on machine learning-based tomographic reconstruction, which garnered significant attention. Collaborating with institutions like General Electric, the Food and Drug Administration, and Harvard University, Wang’s group develops cutting-edge imaging algorithms for clinical and preclinical applications.

Wang’s research extends to interior tomography, addressing the “interior problem” related to transverse data truncation. His team also explored omni-tomography, enabling spatiotemporal fusion of tomographic modalities, including simultaneous CT-MRI. Additionally, they pioneered bioluminescence tomography for optical molecular imaging and developed spectrography techniques for ultrafast and ultrafine tomography using polychromatic scattering data.

His scholarly output includes over 550 peer-reviewed papers in prestigious journals such as Nature Machine Intelligence, Nature Communications, and Proceedings of the National Academy of Sciences. Wang holds more than 100 issued and published patents. His research has been consistently funded by organizations like the National Institutes of Health and the National Science Foundation, with total grants exceeding $40 million.

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 (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) “for contributions to spiral/helical cone-beam/multi-slice CT”, 2019.

Awards

 * Giovanni DiChiro Award for Outstanding Scientific Research, Journal of Computer Assisted Tomography, 1997
 * AAPM/IPEM Medical Physics Travel Award in the 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, 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
 * 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
 * Edward J Hoffman Medical Imaging Scientist Award, 2023.