Ge Wang

Ge Wang is a medical imaging scientist focusing on computed tomography (CT) and artificial intelligence (AI) 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 research and teaching on CT and AI-based imaging. He is a Fellow of the 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). Since January 1, 2025, he has been serving as the Editor-in-Chief of the IEEE Transactions on Medical Imaging.

Education Wang earned a B.E. in Signal Processing at Xidian University and an M.S. in Remote Sensing at University of the Chinese Academy of Sciences. He was awarded an M.S. and a Ph.D. in Electrical and Computer Engineering from the University at Buffalo.

To solve the long-object problem, Wang and his collaborators enhanced existing 2D filtered backprojection and Feldkamp–Davis–Kress reconstruction by introducing 3D backprojection along the actual measurement rays from a spiral cone-beam scanning trajectory. This approach, marked the earliest advancement in cone-beam spiral CT. Commercial CT systems adopted and improved the approach proposed by Wang and colleagues. 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, about 200 million medical CT scans are performed annually using the cone-beam spiral scanning mode.

After his cone-beam CT work, 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 imaging-based low-dose CT, few-view, reconstruction, artifact reduction, radiomics, foundation models, and healthcare metaverse. His team also authored the first book on machine learning-based tomographic reconstruction. Collaborating with institutions like General Electric, the Food and Drug Administration, Johns Hopkins University, Yale University, and Harvard University, Wang’s group develops cutting-edge imaging algorithms for clinical and preclinical applications.

Wang’s team and collaborators developed interior tomography theory and algorithms, addressing the long-standing “interior problem”. His team also explored omni-tomography for spatiotemporal fusion of tomographic modalities, including the concept of simultaneous CT-MRI. Additionally, Wang and collaborators 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 800 peer-reviewed papers in journals such as Nature, Nature Machine Intelligence, Nature Communications, and Proceedings of the National Academy of Sciences. Wang holds more than 170 issued and published patents. His research has been consistently funded by the National Institutes of Health, the National Science Foundation and General Electric, with total grants exceeding $40 million.

Honors 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 Optica Fellow “for pioneering contributions to development of bioluminescence tomography”, 2010 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 US 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 an 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 Walston Chubb Award for Innovation, Sigma Xi, 2022 Edward J Hoffman Medical Imaging Scientist Award, 2023. IEEE TRPMS Best Paper Award, 2024 Edith H. Quimby Award for Lifetime Achievement in Medical Physics from the American Association of Physicists in Medicine, 2025 Ranked 42nd worldwide in the field of nuclear medicine and medical imaging in the 2025 citation database compiled by John P. A. Ioannidis and colleagues at Stanford University. Highly Cited Researcher (Clarivate), 2025.