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In x-ray computed tomography (CT) scanning, the presence of metal objects gives rise to artifacts. Although dual-energy CT scanning and decomposition can reduce metal artifacts, in practice, the decomposition is unstable in the presence of noise and yields residual and new artifacts. A common practice in metal artifact reduction (MAR) algorithms is to use a prior-image as a guide to estimating the underlying data that are corrupted by the metal. We have developed a method in which one prior-image can be used to correct the various sinograms generated by dual-energy decomposition. We applied our method to data acquired on a commercial CT scanner. Compared to the uncorrected images, the MAR images have superior uniformity in known uniform regions while preserving edges, and better visual definition of structures.

An adaptive automatic threat recognition system (AATR) developed at the Lawrence Livermore National Laboratory (LLNL) is described for x-ray CT images of baggage. The AATR automatically adapts to the input object requirement specification (ORS), which can change or evolve over time. These specifications characterize materials of interest (MOIs), basic physical features of interest (FOIs) (such a mass and thickness) and performance goals (detection and false alarm probability) for objects of interest (OOIs). The need and technical requirements for an AATR were developed in collaboration with DHS’s Explosives Division and Northeastern University’s Awareness and Localization of Explosives-Related Threats (ALERT) Center, a DHS Center of Excellence (http://www.northeastern.edu/alert/). Independent of the input ORS, LLNL’s AATR always uses the same algorithm and codes to process CT images. The algorithm adapts in real-time to changes in the input ORS. LLNL’s AATR is thus suitable for dynamic scenarios in which the nature of the OOIs can change rapidly. The AATR uses a spatial consensus relaxation method to determine the most likely material composition for each CT image voxel. The resulting image of most likely material compositions is segmented. An OOI classification statistic (OOI score) is computed for each voxel and each extracted image volume. OOI recognition performance is reported using various metrics on a test set of ~180 plastic bins supplied by the ALERT Center of Excellence. A method is then proposed for automatic decision threshold estimation that can adapt to the detection performance goal, the most likely material composition, and the contents of the baggage.

In a recent journal article [IEEE Trans. Nuc. Sci., 63(1), 341-350, 2016], we introduced a novel method that decomposes dual-energy X-ray CT (DECT) data into electron density (ρe) and a new effective-atomic-number called Ze in pursuit of system-independent characterization of materials. The Ze of a material, unlike the traditional Zeff, is defined relative to the actual X-ray absorption properties of the constituent atoms in the material, which are based on published X-ray cross sections. Our DECT method, called SIRZ (System-Independent ρe, Ze), uses a set of well-known reference materials and an understanding of the system spectral response to produce accurate and precise estimates of the X-ray-relevant basis variables (ρe, Ze) regardless of scanner or spectra in diagnostic energy ranges (30 to 200 keV). Potentially, SIRZ can account for and correct spectral changes in a scanner over time and, because the system spectral response is included in the technique, additional beam-hardening correction is not needed. Results show accuracy (

Computed Tomography (CT) using penetrating radiation (x- or gamma-rays) can be used in a number of aircraft applications. This technique results in 3D volumetric attenuation data that is related to density and effective atomic number. CT is a transmission scanning method that must allow complete access to both sides of the object under inspection; the radiation source and detection systems must surround the object. This normally precludes the inspection of some large or planar (large aspect ratio) parts of the aircraft. However, we are pursuing recent limited-data techniques using object model information to obtain useful data from the partial information acquired. As illustrative examples, we describe how CT was instrumental in the analysis of particular aircraft components. These include fuselage panels, single crystal turbine blades, and aluminum-lithium composites.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Members of the Nondestructive Evaluation Section at the Lawrence Livermore National Laboratory (LLNL) are implementing the advanced three-dimensional imaging technique of x- and gamma-ray computed tomography (CAT or CT) for industrial and scientific obscured object evaluation. This technique provides internal and external views of materials, components, and assemblies nonintrusively. Our work includes building of CT scanners as well as data preprocessing, image reconstruction, display and analysis algorithms. These capabilities have been applied to a variety of industrial and scientific NDE applications. We have used CT to study various objects with obscured features at our laboratory ranging in size from 1 mm3 to 1 m3. In these studies, CT has revealed flaws (e.g. cracks, voids, and inclusions), internal and external dimensional information, differences in elemental composition or material density, and other important material characteristics. CT has also been used to localize, identify, and quantify radioisotopes within canisters. As illustrative examples, we describe how CT was instrumental in the analysis of concrete specimens, diesel engine thermocouple plugs, jet engine turbine blades, ballistic target materials, and radioactive waste canisters.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.