Clark and Crossan Endowed Chair Professor
Ge Wang is a world leading imaging scientist and biomedical engineer whose contributions have enduring major impacts on radiology, preclinical imaging, and micro-tomography, especially for x-ray computed tomography (CT) and bioluminescence tomography. His work crosses traditional disciplinary boundaries involving multiple modalities and multi-scales with key results in applied mathematics, medical physics, instrumentation, and applications. He received decades of continuous federal grant support including academic-industrial partnership and bioengineering partnership awards. His main collaborating sites include General Electric Global Research Center, Wake Forest, Stanford, Harvard, and Yale Medical Schools.
Ge Wang’s most significant publication is “A general cone-beam reconstruction algorithm” in IEEE Transactions on Medical Imaging (Wang et al. 1993), which has been highly cited. This paper introduced the concept of spiral cone-beam CT scanning to solve the “long object” problem (longitudinal data truncation). Spiral cone-beam/multi-slice CT has become a standard imaging modality and cornerstone of modern radiology. The de facto standard algorithm for CT, filtered backprojection, has been extended to reconstruct volumetric images with spiral scanning. A majority of his >400 peer-reviewed journal papers and >30 patents focus on cone-beam spiral CT to address imaging performance and radiation dose issues. These contributed to widespread clinical acceptance of spiral cone-beam/multi-slice CT. Currently, there are >85 million CT scans yearly in USA alone, with a majority in the spiral cone-beam/multi-slice mode.
Bioluminescence tomography was developed since 2004 to allow in vivo 3D localization of bioluminescent probes for preclinical molecular imaging, largely based on pioneering work by Ge Wang. The algorithms and methods were first introduced and later refined by Ge Wang, collaborators and other peers. This was based in part on micro-CT reconstruction.
Ge Wang’s group introduced interior tomography in 2007 as a solution to the long-standing “interior problem” (transverse data truncation), targeting exact reconstruction of an internal region from truncated data, with major benefits such as low-dose/ultra-fast CT. His team enhanced interior tomography via compressed sensing in a highly cited 2009 paper, challenging the conventional wisdom that there is no unique solution to the interior problem. He pioneered general interior tomography and omni-tomography for the grand fusion of all relevant modalities (“all-in-one”) to acquire datasets simultaneously (“all-at-once”); for example for simultaneous CT-MRI.
Also, Ge Wang performed micro-tomography research to enable cone-beam image reconstruction of either elongated or planar samples. At Virginia Tech, he built a world-class multi-scale CT facility covering sample size and image resolution over six orders of magnitude and applied to a variety of topics demanding nano-/micro-imaging capabilities. In service to his former institution, Virginia Tech, Ge Wang led the multi-scale CT facility openly available to colleagues and industry.
Ge Wang’s leadership to the profession includes peer review and editorial duties for NIH, NSF, DOE, and many journals. He served as lead guest editor for four IEEE Transactions on Medical Imaging special issues. He delivered numerous plenary/keynote/invited presentations at imaging conferences and major universities. He was Co-Chair of the 2008 International Conference on Biomedical Inverse Problems, General Chair of the 2014 IEEE International Symposium on Biomedical Imaging, Technology/CT Category Chair of the 2014 World Molecular Imaging Congress, and will be Conference Chair for the 2017 Fully Three-Dimensional Image Reconstruction Conference, and Chair for the 2018 SPIE Conference on Developments in X-ray Tomography. He received multiple academic awards and peer recognitions, such as the 1997 Giovanni DiChiro Award for Outstanding Scientific Research from Journal of Computer Assisted Tomography, and the 2004 Herbert M. Stauffer Award for Outstanding Basic Science Paper in Academic Radiology, Association of University Radiologists, USA. He has been elected as Fellow of AIMBE (2002), Fellow of IEEE (2003), Fellow of SPIE (2007), Fellow of OSA (2009), Fellow of AAPM (2012), and Fellow of AAAS (2014).
BE, 1978-1982, Dept. of ECE, Xidian Univ., P. R. China (the best radar signal processing specialty in China)
MS, 1982-1985, Institute of Remote Sensing Applications, Graduate School of Academia Sinica (the first graduate school in China); Thesis: Texture analysis of satellite images
MS, 1989-1991, Dept. of ECE, State Univ. of New York at Buffalo; Thesis: Preliminary studies on cone-beam reconstruction
PhD, 1989-1992, Dept. of ECE, State Univ. of New York at Buffalo; Dissertation: Cone-beam X-ray Microtomography (UMI# 9317362) (Advisors: Cheng PC, Lin TH)
- Wang G, Lin TH, Cheng PC, Shinozaki DM: A general cone-beam reconstruction algorithm. IEEE Trans. Med. Imaging 12:486-496, 1993. It is a moderate expansion of the SPIE paper: Wang G, Lin TH, Cheng PC, Shinozaki DM, Kim HG: Scanning cone-beam reconstruction algorithms for x-ray microtomography. Proc. SPIE 1556:99-112, July 1991. These are the first publications on spiral/helical multi-slice/cone-beam CT image reconstruction. Spiral/helical multi-slice/cone-beam CT methods are widely used in medical CT scanners. Over 100-million multi-slice/cone-beam scans are annually performed worldwide.
- Wang G, Vannier MW: Longitudinal resolution in volumetric X-ray CT – Analytical comparison between conventional and helical CT. Med. Phys. 21:429-433, 1994. It is the first paper on superiority of spiral fan-beam CT (a simpler/special case of spiral cone-beam CT) over conventional slice-based CT. This work has facilitated widespread use of spiral fan-beam CT as the first step towards spiral cone-beam CT, and was remarked by an international CT authority Dr. Kalender in an editorial as “the state of the art in spiral CT” (Radiology 197:578-580, 1995).
- Jiang M, Wang G: Convergence studies on iterative algorithms for image reconstruction. IEEE Trans. Med. Imaging 22:569-579, 2003. It proves the convergence of the simultaneous algebraic reconstruction technique (SART), which is a most popular iterative algorithm widely used in many fields.
- Wang G, Li Y, Jiang M: Uniqueness theorems in bioluminescence tomography. Med. Phys. 31:2289-2299, 2004. It is the first paper on bioluminescence tomography, proposes the overall concept, describes the theoretical foundation, was selected for the Virtual Journal of Biological Physics Research, and reviewed in Nature Biotechnology (http://www.nature.com/nbt/journal/v23/n3/pdf/nbt1074.pdf).
- Yu H, Wang G: Compressive sensing based interior tomography. Phys. Med. Biol. 54:2791-2805, 2009. It is the first paper on interior tomography to replace classic local tomography and lambda tomography under a general condition; also see the PNAS Letter: Wang G, Yu H: Can interior tomography outperform lambda tomography? (http://www.pnas.org/content/107/22/E92.full). This solves the long-standing “interior problem” (theoretically exact interior reconstruction from local data), and is now the most-cited research paper in Phys. Med. Biol. since 2009.
- Wang G, Yu H, Cong W, Katsevich A: Non-uniqueness and instability of ‘Ankylography’. Nature 480:E2–E3, Nov. 30, 2011. It disproves ‘Ankylography’ and suggests spectrography for 3D reconstruction from one or few spectral coherent views. This was also reported in Nature as “In Focus News” (http://www.nature.com/polopoly_fs/1.9645!/import/pdf/480303a.pdf).
- Wang G, Zhang J, Gao H, Weir V, Yu HY, Cong WX, Xu XC, Shen HO, Bennett J, Furth M, Wang Y, Vannier MW: Towards Omni-Tomography. PLoS ONE 7(6): e39700. doi:10.1371/journal.pone.0039700, 2012. It defines omni-tomography for grand fusion of biomedical tomographic modalities (“all-in-one”) to acquire different types of data simultaneously (“all-at-once”) (The follow-up work was widely reported to translate the initial toy system to an engineering design, such as http://medicalphysicsweb.org/cws/article/opinion/51026, http://spie.org/x94063.xml).
- Wang G, HY Yu: The meaning of interior tomography. Phys. Med. Biol. 58:R161–R186, 2013; also the PMB Editor's Choice on http://medicalphysicsweb.org, 08/05/13. It reviews the state-of-the-art in the area of interior tomography.
- 278. Wang G, Perspective on deep imaging. IEEE Access, DOI: 10.1109/ACCESS.2016.2624938, 2016; https://ieeexplore.ieee.org/document/7733110/
- 285. Wang G, Ye JC, Mueller K, Fessler JA: Image Reconstruction Is a New Frontier of Machine Learning — Editorial for the Special Issue “Machine Learning for Image Reconstruction”. IEEE Trans. Medical Imaging 37:1298- 1296, 2018; https://ieee-tmi.org/fast-facts/featured-article.asp?id=30&title=Special-Issue-on-Machine-Learning-for-Image-Reconstruction,-June-2018