Identifying and intercepting prohibited items and explosives is a critical focus of aviation security. While computed tomography (CT) systems represent the industry standard for detecting explosives in baggage, x-ray diffraction imaging (XRDI) systems have shown increasing performance and commercial viability. Our approach to explosives detection involves the combination of CT and XRDI into a single, hybrid system where both the CT and XRDI data are utilized in the reconstruction and classification algorithms. In this work, we focus on comparing multiple reconstruction and classifier implementations and quantifying the resulting performance. Our analysis shows higher quality reconstructions lead to improved material separability, better classification performance (detection and false alarm rates), and reduces model uncertainty. Through this work, we demonstrate the relationship between improved quality of reconstructions and the separability of threat from non-threat objects in the domain of explosives detection.
The ability to determine the atomic structure or identify the material composition of a sample at high spatial resolution is paramount to a variety of research, imaging, and inspection tasks. We have developed a multi-modality x-ray transmission and x-ray diffraction imaging system that is compact and enables scanning of intact samples. To demonstrate the capabilities of the system, we characterize its spatial and momentum transfer resolution and provide examples of the contrast and utility of the system using a combination of resolution and anthropomorphic phantoms.
X-ray diffraction imaging (XRDI) offers the potential for reduced false alarm rates, increased throughputs, and more sensitive explosives detection performance in aviation security applications. The deployment of computed tomography (CT) systems across carry-on and checked baggage screening lanes has both reinforced the need for orthogonal detection technologies and created an exciting new opportunity for the implementation of XRDI. Our team at Quadridox built a novel XRDI system that, when combined with a CT system, realizes full-tunnel assessment of checked bags at a belt speed of 20 cm/s. We integrated our XRDI system with a Smiths CTX 5800 explosives detection system (EDS) and collected bag data containing both benign and threat objects. We describe the XRDI system, show examples of the resulting hybrid CT and XRD dataset, and present performance results for the hybrid system.
This work evaluates two different detector technologies in terms of their performance in making fast, low-signal diffraction measurements. The first detector is a large-area mammography detector that uses a complementary metal-oxide semiconductor (CMOS) crystal, and the second is a cadmium-telluride photon-counting detector. By measuring the diffraction spectra for a diverse range of materials and with acquisition times ranging from 10 seconds and 0.1 seconds, we show how each detector performs as signal-to-noise ratios decrease and counting statistics become less significant. As a result, we show that the photon counting detector slightly better preserves the long-time average signal in short acquisition times in comparison to the CMOS detector when diffraction signals display sharp/narrow features, but that the detectors performed similarly for materials with much broader diffraction signals, like those associated with soft tissue and biological specimen. This leads us to conclude that the photon-counting detector is slightly higher-performing for our purposes.
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