Conference on Modern Challenges in Imaging
In the Footsteps of Allan MacLeod Cormack
On the Fortieth Anniversary of his Nobel Prize

August 5-9, 2019, Tufts University, Medford, Massachusetts

From 1957 to 1995, Allan MacLeod Cormack was a University Professor at Tufts. His pioneering work published in 1963 and 1964 provided the mathematical foundations of computerized tomography (CT) and thereby the first practical method to "see into" an object without physically breaking it open. Along with the engineer Godfrey Newbold Hounsfield, he won the 1979 Nobel Prize in Physiology or Medicine for this work.

40 years later, the international conference on Modern Challenges in Imaging will honor the achievements of Tufts only Nobel Laureate and keep his thriving legacy up by gathering top international researchers in mathematics, engineering, science, and medicine. A broad range of tomographic modalities, mathematics, and applications will be presented to provide an overview of the different aspects and foster new collaborations.

Check out this article about the conference in Tufts Now.

© Picture by Eiríkur Jónsson frá Vorsabæ

  • Guest editors
    • B N Hahn University of Würzburg, Germany
    • E T Quinto Tufts University, USA
    • G Rigaud University of Würzburg, Germany

This special issue honours the achievements of Nobel Laureate Allan Cormack whose pioneering work provided the mathematical foundations of computerized tomography. It will gather top articles on inverse problems in imaging, including a broad range of tomographic modalities, mathematics and applications.

We invite authors to submit original research papers related to modern challenges in imaging with a focus on tomographic and related inverse problems. Papers with focus on mathematical modelling, computational methods, theory, and numerical analysis, and/or applications are equally welcome.

Submission deadline: 31 December 2019.
Simon Arridge University College London United Kingdom
Charles Bouman Purdue University United States
Carl Crawford Csuptwo, LLC United States
Charles Epstein University of Pennsylvania United States
Jeff Fessler University of Michigan United States
Bernadette Hahn University of Würzburg Germany
Per Christian Hansen Technical University of Denmark Denmark
Alexander Katsevich University of Central Florida United States
Misha Kilmer Tufts University United States
Peter Kuchment Texas A&M University United States
Leonid Kunyansky University of Arizona United States
Jennifer Mueller Colorado State University United States
Frank Natterer Münster University Germany
Linh Nguyen University of Idaho United States
Todd Quinto Tufts University United States
Rosemary Renaut Arizona State University United States
Andreas Rieder Karlsruhe Institute of Technology Germany
Gaël Rigaud University of Würzburg Germany
John Schotland University of Michigan United States
Eric de Sturler Virginia Tech United States
Gunther Uhlmann University of Washington and HKUST United States and Hong Kong
Ge Wang Rensselaer Polytechnic Institute United States
Registration phase 1 Before June 10
Registration phase 2 June 10-July 15
Registration phase 3 From July 15
Conference dates August 5-9, 2019
  • Tufts University:
    • Office of the Provost,
    • Dean of Arts and Sciences,
    • Dean of Engineering
    • Electrical and Computer Engineering Department
    • Mathematics Department
    • Physics Department
  • Private Sponsors:
    • Gordon Foundation
    • Jay A. Stein, Chief Technology Officer, Hologic, Inc., Marlboro, Massachusetts
  • Corporate and Government Sponsors:
    • NSF The National Science Foundation (NSF) is a United States government agency that supports fundamental research and education in all the non-medical fields of science and engineering.
    • NIH The National Institutes of Health (NIH) National Institute of Biomedical Imaging and Bioengineering (NIBIB) is a United States government agency that supports biomedical and public health research.
    • Inverse Problems An interdisciplinary journal combining mathematical and experimental papers on inverse problems with numerical and practical approaches to their solution
    • Mobius Imaging A more intelligent, more capable approach to imaging


NSF Logo NIH Logo IP Logo Mobius Logo Tufts Logo