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Proceedings Paper

Shape threat detection via adaptive computed tomography
Author(s): Ahmad Masoudi; Ratchaneekorn Thamvichai; Mark A. Neifeld
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Paper Abstract

X-ray Computed Tomography (CT) is used widely for screening purposes. Conventional x-ray threat detection systems employ image reconstruction and segmentation algorithms prior to making threat/no-threat decisions. We find that in many cases these pre-processing steps can degrade detection performance. Therefore in this work we will investigate methods that operate directly on the CT measurements. We analyze a fixed-gantry system containing 25 x-ray sources and 2200 photon counting detectors. We present a new method for improving threat detection performance. This new method is a so-called greedy adaptive algorithm which at each time step uses information from previous measurements to design the next measurement. We utilize sequential hypothesis testing (SHT) in order to derive both the optimal "next measurement" and the stopping criterion to insure a target probability of error Pe. We find that selecting the next x-ray source according to such a greedy adaptive algorithm, we can reduce Pe by a factor of 42.4× relative to the conventional measurement sequence employing all 25 sources in sequence.

Paper Details

Date Published: 12 May 2016
PDF: 5 pages
Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470H (12 May 2016); doi: 10.1117/12.2223348
Show Author Affiliations
Ahmad Masoudi, The Univ. of Arizona (United States)
Ratchaneekorn Thamvichai, The Univ. of Arizona (United States)
Mark A. Neifeld, College of Optical Sciences, The Univ. of Arizona (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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