In our prior work, we had employed a fixed photo-absorption, coherent, and incoherent cross-section material model to derive a shot-noise limited description of the X-ray measurements in check-point or a checked baggage threat-detection systems. Using this measurement model, we developed an information-theoretic metric, which provides an upper-bound on the performance of a threat-detection system. However, the fixed cross-section material model does not incorporate material variability arising from inherent variations in its composition and density. In this work, we develop a multi-energy model of material variability based on composition and density variations and combine it with the shot-noise photon detection process to derive a new X-ray measurement model. We derive a computationally scalable analytic approximation of an information-theoretic metric, i.e. Cauchy-Schwarz mutual information, based on this material variability model to quantify the upper-bound on the performance of the threat-detection task. We demonstrate the effect of material variations on the performance bounds of X-ray transmission-based threat detection systems as a function of detector energy resolution and source fluence.
Ahmad Masoudi, Jay Voris, David Coccarelli, Joel Greenberg, Michael Gehm, and Amit Ashok, "X-ray measurement model and information-theoretic system metric incorporating material variability (Conference Presentation)," Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 106320H (Presented at SPIE Defense + Security: April 18, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2307242.5783257721001.
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