Special Section on Optical Computational Imaging

Computed tomography imaging system design for shape threat detection

[+] Author Affiliations
Ahmad Masoudi, Ratchaneekorn Thamvichai

University of Arizona, Electrical and Computer Engineering Department, 1230 East Speedway Boulevard, Tucson, Arizona 85719, United States

Mark A. Neifeld

University of Arizona, Electrical and Computer Engineering Department, 1230 East Speedway Boulevard, Tucson, Arizona 85719, United States

University of Arizona, College of Optical Sciences, 1630 East University Boulevard, Tucson, Arizona 85721, United States

Opt. Eng. 56(4), 041308 (Dec 08, 2016). doi:10.1117/1.OE.56.4.041308
History: Received August 27, 2016; Accepted November 15, 2016
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Abstract.  In the first part of this work, we present two methods for improving the shape-threat detection performance of x-ray computed tomography. Our work uses a fixed-gantry system employing 25 x-ray sources. We first utilize Kullback–Leibler divergence and Mahalanobis distance to determine the optimal single-source single-exposure measurement. The second method employs gradient search on Bhattacharyya bound on error rate (Pe) to determine an optimal multiplexed measurement that simultaneously utilizes all available sources in a single exposure. With limited total resources of 106 photons, the multiplexed measurement provides a 41.8× reduction in Pe relative to the single-source measurement. In the second part, we consider multiple exposures and develop an adaptive measurement strategy for x-ray threat detection. Using the adaptive strategy, we design the next measurement based on information retrieved from previous measurements. We determine both optimal “next measurement” and stopping criterion to insure a target Pe using sequential hypothesis testing framework. With adaptive single-source measurements, we can reduce Pe by a factor of 40× relative to the measurements employing all sources in sequence. We also observe that there is a trade-off between measurement SNR and number of detectors when we study the performance of systems with reduced detector numbers.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Ahmad Masoudi ; Ratchaneekorn Thamvichai and Mark A. Neifeld
"Computed tomography imaging system design for shape threat detection", Opt. Eng. 56(4), 041308 (Dec 08, 2016). ; http://dx.doi.org/10.1117/1.OE.56.4.041308


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