12 May 2016 Optimizing convergence rates of alternating minimization reconstruction algorithms for real-time explosive detection applications
Author Affiliations +
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carl Bosch, Carl Bosch, Soysal Degirmenci, Soysal Degirmenci, Jason Barlow, Jason Barlow, Assaf Mesika, Assaf Mesika, David G. Politte, David G. Politte, Joseph A. O'Sullivan, Joseph A. O'Sullivan, "Optimizing convergence rates of alternating minimization reconstruction algorithms for real-time explosive detection applications", Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470P (12 May 2016); doi: 10.1117/12.2224173; https://doi.org/10.1117/12.2224173
PROCEEDINGS
17 PAGES


SHARE
Back to Top