Shape threat detection via adaptive computed tomography

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

doi:10.1117/12.2223348

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Computed tomography imaging system design for shape threat detection

2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Prðrjcþ Prðrjtþmentioning

confidence: 99%