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.
Differentiating material anomalies requires a measurement system that can reliably inform the user/classifier of pertinent material characteristics. In past work, we have developed a simulation framework capable of making simulated x-ray transmission and scatter measurements of virtual baggage. Using this simulated data, we have demonstrated how an information-theoretic approach to x-ray system design and analysis provides insight into system performance. Moreover, we have shown how performance limits relate to architectural variations in source fluence, view number, spectral resolution, spatial resolution, etc. However, our previous investigations did not include material variability in the description of the materials which make up the virtual baggage. One would expect the material variability to dramatically affect the results of the information-theoretic metric, and thus we now include it in our analysis. Previously, material information was captured as energy-dependent mean attenuation values. Because of this, material differentiation can always become easier with an improvement in SNR. When there is no variation to obscure class differences, improvements in SNR will indefinitely improve performance. Therefore, we saw a monotonic increase of the metric with source fluence. However there is inherent variability in materials from chemical impurities, texturing, or macroscopic variation. When this variability is accounted for, we better understand system performance limits at higher SNR as well as better represent the distributions of material characteristics. We will report on the analysis of real world system geometries and the fundamental limits of performance limits after incorporating these material variability improvements.
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.
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