![]() ![]() We therefore focus our attention on the resolution of the diffraction pattern, rather than the spatial resolution, unless explicitly stated otherwise.Īn example of good resolution obtained in a real airport security setting is described in Ref. Small quantities of threats, or a multitude of different tiny threats within the same suitcase, is beyond the scope of this research. There is also the consideration of spatial resolution, but that is less important for security applications where the goal is to detect the most egregious threats like a suitcase loaded with pounds of an explosive or a drug. A reasonable trade-off between resolution and flux is therefore key for an economically viable scanner. The downside of collimation is the loss of photon flux 1, which then requires long measurement times to compensate. The resolution can be increased with tight collimation, both spatially and in terms of X-ray energy spectrum. The ability to identify a material depends on the resolution of the reconstructed diffraction pattern. This is advantageous for security screenings, since many threats are in fact crystals, crystalline powders, or semi-crystalline compound materials (crystal methamphetamine, cocaine and common explosives like TNT and RDX). XRD is especially well-suited for identifying crystals, as their periodic structure gives rise to very sharp diffraction peaks. It is very sensitive to the spatial arrangement of atoms, which is highly distinct across thousands of different materials. A much more specific material fingerprint can be measured using X-ray diffraction (XRD). Unfortunately for security applications, the density and atomic number of threat materials (drugs, explosives) can be very similar to that of harmless metals, ceramics, and plastics. With multi-energy CT, we can also infer the average composition (effective atomic number) in 3D. After performing this measurement from multiple angles, it is possible to mathematically reconstruct the 3D density of the object. X-ray computed tomography (CT) is based on the measurement of X-ray transmission across a large region of interest (ROI), for example a suitcase in airport security screenings. Our theoretical model is implemented in GPU (Graphics Processing Unit) accelerated software which can be used to further optimize scanner designs for applications in security, healthcare, and manufacturing quality control. Our XRD reconstruction adds material-specific information, albeit at a low resolution, to the already existing CT image, thus improving threat detection. We include a reasonable amount of photon counting noise (Poisson statistics), as well as measurement bias (incoherent scattering). We then show how to reconstruct XRD patterns from a large phantom with multiple diffracting objects. To simulate a realistic instrument, we propose a forward model that includes the resolution-limiting effects of the polychromatic spectrum, the detector, and all the finite-size geometric factors. In this article we numerically analyze a novel low-cost scanner design which captures CT and XRD signals simultaneously, and uses the least possible collimation to maximize the flux. Measurement of quality XRD data is therefore slower compared to CT, which is an economic challenge for potential customers like airports. Unfortunately, the diffracted photon flux is typically much weaker than the transmitted one. In these cases, X-ray diffraction (XRD) may be better suited to distinguish the threats. CT can provide the density and the effective atomic number, which is not always sufficient to identify threats like explosives and narcotics, since they can have a similar composition to benign plastics, glass, or light metals. X-ray computed tomography (CT) is a commercially established modality for imaging large objects like passenger luggage.
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