Detecting material anomalies in baggage requires a high-throughput X-ray measurement system that can reliably inform the user/classifier of pertinent material characteristics. We have developed a comprehensive high-fidelity simulation framework capable of modeling a multi-energy X-ray fixed gantry computed tomography transmission system. Our end-to-end simulation framework includes experimentally validated models of sources and detectors, as well as virtual bags to emulate the X-ray measurements generated by the fixed gantry X-ray CT system. This simulation capability enables us to conduct exploratory system trade-off studies around the current fixed gantry system, in terms of the source detector geometry, detector energy resolution and other relevant system parameters to assess their impact on the threat detection performance. Using scalable information-theoretic metrics, evaluated on simulated system data, we are able to provide quantitative performance bounds on the performance of the candidate system designs. In this work, we will report results of our initial system design trade-off studies focused on detector energy resolution and energy partitioning and how they impact the threat detection performance.
Simulations of x-ray scanners have the potential to aid in the design and understanding of system performance. We have previously shown the usefulness of a high-throughput simulation framework in pursuit of information theoretic analysis of x-ray systems employed for aviation security. While conclusions drawn from these studies were able to inform design decisions, they were limited to generic system geometries and na¨ıve interpretations of detector responses. In collaboration with the SureScan Corporation, we have since expanded our analysis efforts to include their real world system geometry and detector response. To this extent, we present our work to simulate the SureScan x1000 scanner, a fixed-gantry spectral CT system for checked baggage. Our simulations are validated in terms of system geometry and spectral response. We show how high fidelity simulations are used with SureScan reconstruction software to analyze virtual baggage. The close match between simulated and real world measurements means that simulation can be a powerful tool in system development. Moreover, the close match allows simulation to be a straightforward avenue for producing large labeled datasets needed in machine learning approaches to automatic threat recognition (ATR).
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.
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