Target Detection Via Adaptive Tomography

Masoudi, Ahmad; Neifeld, Mark A.; Thamvichai, Ratchaneekorn

doi:10.1364/cosi.2015.cw2f.5

Cited by 2 publications

(2 citation statements)

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“…3, we extend the aforementioned nonadaptive single-exposure systems to the adaptive multiexposure systems by applying the sequential hypothesis testing (SHT) algorithm. [13][14][15][16] By using SHT, instead of employing a single measurement with high signal-to-noise ratio (SNR) measurement, we can use a sequence of lower SNR measurements that benefit from accumulated knowledge. Section 4 studies the behavior of both adaptive and nonadaptive systems when the number of photon-counting detectors is reduced.…”

Section: Prðrjcþ Prðrjtþmentioning

confidence: 99%

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Computed tomography imaging system design for shape threat detection

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 (P e ) to determine an optimal multiplexed measurement that simultaneously utilizes all available sources in a single exposure. With limited total resources of 10 6 photons, the multiplexed measurement provides a 41.8× reduction in P e 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 P e using sequential hypothesis testing framework. With adaptive single-source measurements, we can reduce P e 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.

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Section: Prðrjcþ Prðrjtþmentioning

confidence: 99%

“…(15). Even though Bhattacharyya distance does not have a closed analytical form between two GMMs, it has been shown that the classification between two GMMs can be upper-bounded by Eq.…”

Section: Multiplex Solutionmentioning

confidence: 99%