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Proceedings Paper

Towards an x-ray-based coded aperture diffraction system for bulk material identification
Author(s): S. O. Diallo; K. Tadlock; C. Gregory; S. Wolter; J. A. Greenberg; K. Roe
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Paper Abstract

The detection of prohibited items at airport checkpoints, especially energetic materials, by means of x-ray imaging technology, is one of the most important tasks in transportation security. Conventional checkpoint X-ray systems exploit the energy dependence of the material- specific attenuation coefficient to estimate an ‘effective’ atomic number (or Zeff ) and, in some cases, the mass density (ρ) of a target material, which are then used to classify it. While this technology provides high quality imaging capabilities and satisfactory objects discrimination in many security applications, it also has known limitations. For example, differentiating objects with similar Zeff and/or ρ, such as is often the case for many benign organic materials and explosives, can be a challenging task. X-ray Diffraction Tomography (XRDT), using a coded mask (down stream from the sample), provides structural information that can further enhance material discrimination from the unique chemical/molecular signatures. Here, we present experimental data obtained using our research prototype or ‘XRDT’ scanner, built with off-the shelf components. Using two different industrial solvents, one benign (H2O or water) and one prohibited chemical precursor (2-butanone or methyl-ethyl-ketone (MEK)), we have evaluated the detection performance against material type, sample size, beam size, and investigated the effects of background. Within the scope of our study, we find that a satisfactory tomographic reconstruction and reliable bulk material identification can be achieved with the XRDT. These results will help guide the future development of coded aperture based screening technology at security checkpoint.

Paper Details

Date Published: 27 April 2018
PDF: 16 pages
Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 1063209 (27 April 2018); doi: 10.1117/12.2302513
Show Author Affiliations
S. O. Diallo, Smiths Detection Inc. (United States)
K. Tadlock, Smiths Detection Inc. (United States)
C. Gregory, Smiths Detection Inc. (United States)
S. Wolter, Elon Univ. (United States)
J. A. Greenberg, Duke Univ. (United States)
K. Roe, Smiths Detection Inc. (United States)

Published in SPIE Proceedings Vol. 10632:
Anomaly Detection and Imaging with X-Rays (ADIX) III
Amit Ashok; Joel A. Greenberg; Michael E. Gehm; Mark A. Neifeld, Editor(s)

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