Structured illumination for compressive x-ray diffraction tomography

Joel A. Greenberg, David J. Brady

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Coherent x-ray scatter (also know as x-ray diffraction) has long been used to non-destructively investigate the molecular structure of materials for industrial, medical, security, and fundamental purposes. Unfortunately, molecular tomography based on coherent scatter typically requires long scan times and/or large incident fluxes, which has limited the practical applicability of such schemes. One can overcome the conventional challenges by employing compressive sensing theory to optimize the information obtained per incident photon. We accomplish this in two primary ways: we use a coded aperture to structure the incident illumination and realize massive measurement parallelization and use photon-counting, energy-sensitive detection to recover maximal information from each detected photon. We motivate and discuss here the general imaging principles, investigate different coding and sampling strategies, and provide results from theoretical studies for our structured illumination scheme. We find that this approach promises real-time molecular tomography of bulk objects without a loss in imaging performance.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Computational Imaging XII
ISBN (Print)9780819499370
StatePublished - 2014
Externally publishedYes
EventComputational Imaging XII - San Francisco, CA, France
Duration: Feb 5 2014Feb 6 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceComputational Imaging XII
CitySan Francisco, CA


  • Coded aperture
  • X-ray diffraction imaging
  • X-ray tomography

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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