Draft:Ge Wang (scientist)

Ge Wang (Chinese: 王 革; born in 1957) is a medical imaging scientist focusing on computed tomography(CT), artificial intelligence (AI), and deep learning. He is Clark & Crossan Chair Professor of Biomedical Engineering and Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute, Troy, New York, USA. Wang is known for his pioneering work on CT and AI-based imaging.

Researcher
In the early 1990s, Wang introduced the spiral cone-beam CT method. For this work, he was inducted into the National Academy of Inventors in 2019.

Michel Defrise et al. wrote that "to solve the long-object problem, the 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."

In 2016, Wang presented the deep learning-based tomographic imaging roadmap. Wang co-authored a book on deep learning-based tomographic reconstruction in 2019 with IOP Publishing.

In partnership with General Electric, Food and Drug Administration, and Harvard University, Wang’s team develops deep imaging algorithms and systems for clinical and preclinical applications. In 2017, he was the coordinator of the first Deep Reconstruction Workshop.

Wang and his collaborators developed interior tomography to solve the interior problem and proposed omnitomography for the spatiotemporal fusion of tomographic datasets, with simultaneous CT-MRI as an example. Moreover, his team developed bioluminescence tomography for optical molecular imaging and proposed spectrography for ultrafast and ultrafine tomography from polychromatic scattering data. Wang worked on axiomatic bibliometrics, developed the first undergraduate and graduate courses on deep medical imaging, and a distanced online testing technology.

Employment
Wang joined the Department of Electrical & Computer Engineering, University of Chinese Academy of Sciences as an Instructor (1984 - 1986) and became promoted to Assistant Professor (1986 - 1988). He was Research Assistant (1988 - 1989) at the Department of Geography & Environmental Studies, Tasmania University, Hobart, Australia, and a Research Assistant (1989 - 1992) at the Department of Electrical & Computer Engineering, State University of New York at Buffalo, USA.

Later, he was also an Adjunct Professor at the Department of Biomedical Engineering, Department of Mathematics, Department of Electrical and Computer Engineering, and Department of Civil Engineering at the University of Iowa, Iowa City. From 1997 to 2006, Wang was the director of the CT Lab. From 2004 to 2006, he was the director of the Center for X-ray & Optical Tomography at the University of Iowa.

From 2006 to 2012, Wang worked as a Pritchard Professor at the College of Engineering, Virginia Tech, Blacksburg, USA. Also, he served as Adjunct Professor (2006 - 2012) at the Department of Mathematics and Department of Electrical & Computer Engineering, Virginia Tech, VA, and an Adjunct Professor (2008 - 2012) with Wake Forest Institute of Regenerative Medicine, Wake Forest University, USA.

Since 2013, Wang has been Clark & Crossan Endowed Chair Professor at the Department of Biomedical Engineering and Department of Electrical, Computer and Systems Engineering and Director of the Biomedical Imaging Center, School of Engineering, Center for Biotechnology and Multidisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.

Recognitions and Awards
Wang is a Fellow of the National Academy of Inventors (NAI) for contributions to spiral/helical cone-beam/multi-slice CT (2019).


 * Herbert M. Stauffer Award, Outstanding Basic Science Paper in Academic Radiology, Association of University Radiologists, USA, 2005


 * IEEE EMBS Academic Career Achievement Award 2021
 * IEEE Region 1 Outstanding Teaching Award 2021
 * World Artificial Intelligence Conference Youth Outstanding Paper Award 2021
 * Doctoral Dissertation Award by International Neural Network Society 2021
 * SPIE Aden & Marjorie Meinel Technology Achievement Award 2022
 * Sigma Xi Walston Chubb Award for Innovation 2022
 * 2023 William H. Wiley Distinguished Faculty Award. Rensselaer Polytechnic Institute, 2023

Book

 * Machine Learning for Tomographic Imaging

Selected publications

 * Wang G, Lin TH, Cheng PC, Shinozaki DM: A general cone-beam reconstruction algorithm. IEEE Trans. Med. Imaging 12:486-496, 1993.


 * Wang G, Li Y, Jiang M: Uniqueness theorems in bioluminescence tomography. Med. Phys. 31:2289-2299, 2004.
 * Yu H, Wang G: Compressive sensing based interior tomography. Phys. Med. Biol. 54:2791-2805, 2009.
 * Ye YB, Yu HY, Wang G: Gel'fand-Graev'ss reconstruction formula in the 3D real space. Medical Physics, 38(S1): S69-S75, 2011.
 * Wang G, Yu H, Cong W, Katsevich A: Non-uniqueness and instability of '' Ankylograph". Nature 480:E2–E3, Nov. 30, 2011.
 * Katsevich G, Katsevich A, Wang G: Stability of the interior problem with polynomial attenuation in the region of interest. Inverse Problems 28:065022, 2012.
 * Stallings J, Vance E, Yang JS, Vannier MW, Liang J, Pang L, Dai L, Ye I, Wang G: Determining scientific impact using a collaboration index. PNAS 110:9680-9685, 2013.
 * Wang G, Kalra M, Murugan V, Xi Y, Gjesteby L, Getzin M, Yang QS, Cong WX, Vannier MW: Simultaneous CT-MRI: Next chapter of multi-modality imaging. Med. Phys. 42:5879-5889, 2015.
 * Wang G, Perspective on deep imaging. IEEE Access, DOI:10.1109/ACCESS.2016.2624938, 2016.
 * Shan HM, Padole A, Homayounieh F, Kruger U, Khera RD, Nitiwarangkul C, Kalra MK, Wang G: Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nature Machine Intelligence, June 2019.
 * Wang G, Ye JC, De Man B: Deep learning for tomographic image reconstruction. Nature Machine Intelligence, DOI:10.1038/s42256-020-00273-z, 2020.
 * Chao HQ, Shan HM, Homayounieh F, Singh R, Khera RD, Guo HT, Su T, Wang G, Kalra MK, Yan PK: Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nature Communications 12:2963 (10 pages), 2021.
 * Wang G, Badal A, Jia X, Maltz JS, Mueller K, Myers KJ, Niu C, Vannier MW, Yan PK, Yu Z, Zeng RP: Development of Metaverse for Intelligent Healthcare. Nature Machine Intelligence 4:922-929.
 * Wang G, Goldwag J, Wang G: DishBrain plays Pong and promises more. Nature MI, 2023