Computed tomography imaging system design for shape threat detection

Ahmad Masoudi, Ratchaneekorn Thamvichai, Mark A. Neifeld

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number41308
JournalOptical Engineering
Volume56
Issue number4
DOIs
StatePublished - Apr 1 2017

Keywords

  • Computational imaging
  • Computed tomography
  • Detection
  • X-ray

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

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