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Proceedings Paper

Sparse view Compton scatter tomography with energy resolved data: experimental and simulation results
Author(s): Abdulla Desmal; Brian H. Tracey; Hamideh Rezaee; Eric L. Miller; Jeffrey R. Schubert; Jeff Denker; Aaron Couture
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Paper Abstract

X-ray inspection systems play a critical role in many non-destructive testing and security applications, with systems typically measuring attenuation during transmission along straight-line paths connecting sources and detectors. Computed tomography (CT) systems can provide higher-quality images than single- or dual-view systems, but the need to measure many projections through the scene increases system complexity and cost. We seek to maximize the image quality of sparse-view (few-view) systems by combining attenuation data with measurements of Compton-scattered photons, that deflect after scattering and arrive at detectors via broken ray paths that provide additional sampling of the scene. The work below presents experimental validation of a singlescatter forward model for Compton-scatter data measured with energy-resolving detectors, and demonstrates a reconstruction algorithm that combines both attenuation and scatter measurements. The results suggest that including Compton-scattered data in the reconstruction process can improve image quality for few-view systems.

Paper Details

Date Published: 1 May 2017
PDF: 9 pages
Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 1018707 (1 May 2017); doi: 10.1117/12.2266688
Show Author Affiliations
Abdulla Desmal, Tufts Univ. (United States)
Brian H. Tracey, Tufts Univ. (United States)
Hamideh Rezaee, Tufts Univ. (United States)
Eric L. Miller, Tufts Univ. (United States)
Jeffrey R. Schubert, American Science and Engineering, Inc. (United States)
Jeff Denker, American Science and Engineering, Inc. (United States)
Aaron Couture, American Science and Engineering, Inc. (United States)

Published in SPIE Proceedings Vol. 10187:
Anomaly Detection and Imaging with X-Rays (ADIX) II
Amit Ashok; Edward D. Franco; Michael E. Gehm; Mark A. Neifeld, Editor(s)

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