Optimizing convergence rates of alternating minimization reconstruction algorithms for real-time explosive detection applications

Carl Bosch, Soysal Degirmenci, Jason Barlow, Assaf Mesika, David G. Politte, Joseph A. O'Sullivan

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

2 Scopus citations

Abstract

X-ray computed tomography reconstruction for medical, security and industrial applications has evolved through 40 years of experience with rotating gantry scanners using analytic reconstruction techniques such as filtered back projection (FBP). In parallel, research into statistical iterative reconstruction algorithms has evolved to apply to sparse view scanners in nuclear medicine, low data rate scanners in Positron Emission Tomography (PET) [5, 7, 10] and more recently to reduce exposure to ionizing radiation in conventional X-ray CT scanners. Multiple approaches to statistical iterative reconstruction have been developed based primarily on variations of expectation maximization (EM) algorithms. The primary benefit of EM algorithms is the guarantee of convergence that is maintained when iterative corrections are made within the limits of convergent algorithms. The primary disadvantage, however is that strict adherence to correction limits of convergent algorithms extends the number of iterations and ultimate timeline to complete a 3D volumetric reconstruction. Researchers have studied methods to accelerate convergence through more aggressive corrections [1], ordered subsets [1, 3, 4, 9] and spatially variant image updates. In this paper we describe the development of an AM reconstruction algorithm with accelerated convergence for use in a real-time explosive detection application for aviation security. By judiciously applying multiple acceleration techniques and advanced GPU processing architectures, we are able to perform 3D reconstruction of scanned passenger baggage at a rate of 75 slices per second. Analysis of the results on stream of commerce passenger bags demonstrates accelerated convergence by factors of 8 to 15, when comparing images from accelerated and strictly convergent algorithms.

Original languageEnglish
Title of host publicationAnomaly Detection and Imaging with X-Rays (ADIX)
EditorsMichael E. Gehm, Amit Ashok, Mark A. Neifeld
PublisherSPIE
ISBN (Electronic)9781510600881
DOIs
StatePublished - Jan 1 2016
EventAnomaly Detection and Imaging with X-Rays (ADIX) Conference - Baltimore, United States
Duration: Apr 19 2016Apr 20 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9847
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAnomaly Detection and Imaging with X-Rays (ADIX) Conference
CountryUnited States
CityBaltimore
Period04/19/1604/20/16

Keywords

  • Accelerated convergence
  • Computed tomography
  • Convergence
  • Explosive detection
  • Reconstruction
  • Regularization

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    Bosch, C., Degirmenci, S., Barlow, J., Mesika, A., Politte, D. G., & O'Sullivan, J. A. (2016). Optimizing convergence rates of alternating minimization reconstruction algorithms for real-time explosive detection applications. In M. E. Gehm, A. Ashok, & M. A. Neifeld (Eds.), Anomaly Detection and Imaging with X-Rays (ADIX) [98470P] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9847). SPIE. https://doi.org/10.1117/12.2224173