Shape threat detection via adaptive computed tomography

Ahmad Masoudi, Ratchaneekorn Thamvichai, Mark A Neifeld

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish (US)
Title of host publicationAnomaly Detection and Imaging with X-Rays (ADIX)
PublisherSPIE
Volume9847
ISBN (Electronic)9781510600881
DOIs
StatePublished - 2016
EventAnomaly Detection and Imaging with X-Rays (ADIX) Conference - Baltimore, United States
Duration: Apr 19 2016Apr 20 2016

Other

OtherAnomaly Detection and Imaging with X-Rays (ADIX) Conference
CountryUnited States
CityBaltimore
Period4/19/164/20/16

Fingerprint

Computed Tomography
Tomography
tomography
X rays
x ray sources
Greedy Algorithm
Adaptive algorithms
Adaptive Algorithm
Sequential Testing
X-ray Tomography
gantry cranes
Photon Counting
Stopping Criterion
Information use
Image Reconstruction
preprocessing
Hypothesis Testing
image reconstruction
Image reconstruction
Image segmentation

Keywords

  • Adaptive Imaging System
  • Computational Imaging System
  • Computed Tomography

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Masoudi, A., Thamvichai, R., & Neifeld, M. A. (2016). Shape threat detection via adaptive computed tomography. In Anomaly Detection and Imaging with X-Rays (ADIX) (Vol. 9847). [98470H] SPIE. https://doi.org/10.1117/12.2223348

Shape threat detection via adaptive computed tomography. / Masoudi, Ahmad; Thamvichai, Ratchaneekorn; Neifeld, Mark A.

Anomaly Detection and Imaging with X-Rays (ADIX). Vol. 9847 SPIE, 2016. 98470H.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Masoudi, A, Thamvichai, R & Neifeld, MA 2016, Shape threat detection via adaptive computed tomography. in Anomaly Detection and Imaging with X-Rays (ADIX). vol. 9847, 98470H, SPIE, Anomaly Detection and Imaging with X-Rays (ADIX) Conference, Baltimore, United States, 4/19/16. https://doi.org/10.1117/12.2223348
Masoudi A, Thamvichai R, Neifeld MA. Shape threat detection via adaptive computed tomography. In Anomaly Detection and Imaging with X-Rays (ADIX). Vol. 9847. SPIE. 2016. 98470H https://doi.org/10.1117/12.2223348
Masoudi, Ahmad ; Thamvichai, Ratchaneekorn ; Neifeld, Mark A. / Shape threat detection via adaptive computed tomography. Anomaly Detection and Imaging with X-Rays (ADIX). Vol. 9847 SPIE, 2016.
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