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Proceedings Paper

Robust x-ray based material identification using multi-energy sinogram decomposition
Author(s): Yaoshen Yuan; Brian Tracey; Eric Miller
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Paper Abstract

There is growing interest in developing X-ray computed tomography (CT) imaging systems with improved ability to discriminate material types, going beyond the attenuation imaging provided by most current systems. Dual- energy CT (DECT) systems can partially address this problem by estimating Compton and photoelectric (PE) coefficients of the materials being imaged, but DECT is greatly degraded by the presence of metal or other materials with high attenuation. Here we explore the advantages of multi-energy CT (MECT) systems based on photon-counting detectors. The utility of MECT has been demonstrated in medical applications where photon- counting detectors allow for the resolution of absorption K-edges. Our primary concern is aviation security applications where K-edges are rare. We simulate phantoms with differing amounts of metal (high, medium and low attenuation), both for switched-source DECT and for MECT systems, and include a realistic model of detector energy 0 resolution. We extend the DECT sinogram decomposition method of Ying et al. to MECT, allowing estimation of separate Compton and photoelectric sinograms. We furthermore introduce a weighting based on a quadratic approximation to the Poisson likelihood function that deemphasizes energy bins with low signal. Simulation results show that the proposed approach succeeds in estimating material properties even in high-attenuation scenarios where the DECT method fails, improving the signal to noise ratio of reconstructions by over 20 dB for the high-attenuation phantom. Our work demonstrates the potential of using photon counting detectors for stably recovering material properties even when high attenuation is present, thus enabling the development of improved scanning systems.

Paper Details

Date Published: 12 May 2016
PDF: 13 pages
Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470V (12 May 2016); doi: 10.1117/12.2222584
Show Author Affiliations
Yaoshen Yuan, Tufts Univ. (United States)
Brian Tracey, Tufts Univ. (United States)
Eric Miller, Tufts Univ. (United States)


Published in SPIE Proceedings Vol. 9847:
Anomaly Detection and Imaging with X-Rays (ADIX)
Amit Ashok; Mark A. Neifeld; Michael E. Gehm, Editor(s)

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