X-ray imaging for security screening is a challenging application that requires simultaneous satisfaction of seemingly incompatible constraints, including low cost, high throughput, and reliable detection of threats. We take a principled computational imaging approach to system design. Mathematical models of the underlying physics and a Huber-class penalty function yield a penalized maximum-likelihood problem. We extend our iterative algorithm for computing linear attenuation coefficients to use multiple energy bins in the SureScan x1000, which has an unconventional, fixed-source geometry. The goal is to maintain the spatial resolution of the single-energy reconstruction while providing information for material characterization used for detection of threats.
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 , 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.
Three-dimensional image reconstruction for scanning baggage in security applications is becoming
important. Compared to medical x-ray imaging, security imaging systems must be designed for a
greater variety of objects. There is a lot of variation in attenuation and nearly every bag scanned
has metal present, potentially yielding significant artifacts. Statistical iterative reconstruction
algorithms are known to reduce metal artifacts and yield quantitatively more accurate estimates of
attenuation than linear methods.
For iterative image reconstruction algorithms to be deployed at security checkpoints, the images
must be quantitatively accurate and the convergence speed must be increased dramatically. There are
many approaches for increasing convergence; two approaches are described in detail in this paper.
The first approach includes a scheduled change in the number of ordered subsets over iterations and
a reformulation of convergent ordered subsets that was originally proposed by Ahn, Fessler et. al.1
The second approach is based on varying the multiplication factor in front of the additive step in
the alternating minimization (AM) algorithm, resulting in
more aggressive updates in iterations. Each approach is implemented on real data from a SureScanTM
x 1000 Explosive Detection System∗ and compared to straightforward implementations of the
algorithm of O’Sullivan and Benac2 with a Huber-type edge-preserving penalty, originally proposed