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In 2016, we published a method for processing dual-energy computed tomography (DECT) data called system-independent rho-e/Z-e ( $\rho _{\mathrm {e}}/Z_{\mathrm {e}}$ ) or SIRZ. Using data from multiple DECT systems and spectra, SIRZ estimated the electron density $\rho _{\mathrm {e}}$ and effective atomic number $Z_{\mathrm {e}}$ (based on published X-ray cross sections), for a set of known specimens. However, the decomposition process required complex spectral modeling of the DECT system, which made SIRZ difficult to implement and automate. This paper describes the subsequent work on “SIRZ-2” to simplify the spectral modeling, automate the process, and improve its range and versatility. The SIRZ-2 basis functions are more accurate for the X-ray energy range ( $Z_{\mathrm {e}}$ of 6–20) were scanned with multiple spectral pairs (up to 200 keV) on seven different DECT systems, including a commercial airport luggage scanner. For these varied tests, the average SIRZ-2 relative errors for $\rho _{\mathrm {e}}$ estimates were four times lower (0.7% compared to 3.0% for SIRZ), while average $Z_{\mathrm {e}}$ relative errors were comparably low for both methods ( $\rho _{\mathrm {e}}$ , $Z_{\mathrm {e}}$ ) that can be directly compared across DECT systems and over time.

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.

We present a new decomposition approach for dual-energy computed tomography (DECT) called SIRZ that provides precise and accurate material description, independent of the scanner, over diagnostic energy ranges (30 to 200 keV). System independence is achieved by explicitly including a scanner-specific spectral description in the decomposition method, and a new X-ray-relevant feature space. The feature space consists of electron density, ${\rho _{\rm e}}$ , and a new effective atomic number, ${{\rm Z}_{\rm e}}$ , which is based on published X-ray cross sections. Reference materials are used in conjunction with the system spectral response so that additional beam-hardening correction is not necessary. The technique is tested against other methods on DECT data of known specimens scanned by diverse spectra and systems. Uncertainties in accuracy and precision are less than 3% and 2% respectively for the ( ${\rho _{\rm e}}$ , ${{\rm Z}_{\rm e}}$ ) results compared to prior methods that are inaccurate and imprecise (over 9%).

The U.S. Army Research Laboratory, in conjunction with Lawrence Livermore National Laboratory, is developing a Multi-Energy Flash Computed Tomography (MEFCT) diagnostic that will be used to capture tomographic image(s) of dynamic impact and detonation events. To accomplish dynamic tomography, the diagnostic uses numerous source–detector pairs to accumulate up to fifteen two-dimensional images, which are subsequently used to compute up to three three-dimensional tomographic reconstructions. The diagnostic is designed to provide either: a single-frame, three-dimensional tomographic reconstruction that delineates material specificity throughout the field, or a three-frame tomographic reconstruction movie spaced in time, while lacking the information pertaining to the material specificity. This work assesses aspects of the diagnostic development including structural design, dynamic capability, instrument resolution and computational reconstruction. Examples of real-time measurements are provided from static phantom fiducials, as well as a dynamic experiment depicting a non-symmetric ballistic penetration to demonstrate the usefulness of the capability.

Purpose: Metal objects present in x-ray computed tomography(CT) scans are accompanied by physical phenomena that render CT projections inconsistent with the linear assumption made for analytical reconstruction. The inconsistencies create artifacts in reconstructed images. Metal artifact reduction algorithms replace the inconsistent projection data passing through metals with estimates of the true underlying projection data, but when the data estimates are inaccurate, secondary artifacts are generated. The secondary artifacts may be as unacceptable as the original metal artifacts; therefore, better projection data estimation is critical. This research uses computer vision techniques to create better estimates of the underlying projection data using observations about the appearance and nature of metal artifacts. Methods: The authors developed a method of estimating underlying projection data through the use of an intermediate image, called the prior image. This method generates the prior image by segmenting regions of the originally reconstructed image, and discriminating between regions that are likely to be metal artifacts and those that are likely to represent anatomical structures. Regions identified as metal artifact are replaced with a constant soft-tissue value, while structures such as bone or air pockets are preserved. This prior image is reprojected (forward projected), and the reprojections guide the estimation of the underlying projection data using previously published interpolation techniques. The algorithm is tested on head CT test cases containing metal implants and compared against existing methods. Results: Using the new method of prior image generation on test images, metal artifacts were eliminated or reduced and fewer secondary artifacts were present than with previous methods. The results apply even in the case of multiple metal objects, which is a challenging problem. The authors did not observe secondary artifacts that were comparable to or worse than the original metal artifacts, as sometimes occurred with the other methods. The accuracy of the prior was found to be more critical than the particular interpolation method. Conclusions: Metals produce predictable artifacts in CTimages of the head. Using the new method, metal artifacts can be discriminated from anatomy, and the discrimination can be used to reduce metal artifacts.

A gamma-ray-based, active (A) and passive (P) computed tomography (CT) technology has been developed that locates, identifies and quantifies gamma-ray emitting isotopes, transuranic (TRU) and others, in nuclear waste drums. ACT uses a collimated external source and a HPGe detector to measure selected mono-energetic gamma-rays that are attenuated by waste-drum contents; a separate PCT measurement uses the HPGe detector to record the spectra of gamma-rays emitted from within a drum. The ACT attenuation images and the PCT emission spectra are coupled to quantitatively assay drum contents for ∼0.1–200 g of TRU isotopes. Calibration requires a single measurement of a known radioactive standard; construction of waste-drum surrogates is not required. Fixed and mobile systems demonstrated compliance with a DOE quality assurance program via several independent blind tests.

Metal artifact reduction methods in computed tomography replace the projection data passing through metals with an estimate of the true data. Inaccurate estimation leads to the generation of secondary artifacts. Data estimates can be improved by the use of prior knowledge of the projection data. In this paper, a method has been created to generate a prior image. The method uses computer vision techniques to segment regions of the initially reconstructed image and then discriminates between regions that are likely to be artifacts and anatomical structures. Results on test images show that metal artifacts are reduced and that few secondary artifacts are present, even in the case of multiple metal objects.

We are developing a new technique using both active (A) and passive (P) computed tomography (CT) to overcome problems in the quantitative analysis of plutonium contents in the molten salt extract (MSE) plutonium buttons. Our first three dimensional image of the MSE button using ACT scan has already shown heterogeneous behavior throughout the button as opposed to the model of a homogeneous button as previously thought. An image from a PCT scan, although it does not have sufficient spatial resolution and data, also seems to support the heterogeneous picture.

Gamma-ray computed tomography (CT) is potentially powerful nondestructive method for assessing the degree of distress that exists in reinforced-concrete structures. In a study to determine the feasibility of using CT to inspect reinforced-concrete specimens, we verified that CT can quantitatively image the internal details of reinforced concrete. To assess the accuracy of CT in determining voids and cracks, we inspected two fiber-reinforced concrete cylinders (one loaded and one unloaded) and a third cylinder containing a single reinforcing bar (rebar). To evaluate the accuracy of CT in establishing the location of reinforcing bars, we also inspected a concrete block containing rebars with different diameters. The results indicate that CT was able to revolve the many different phases in reinforced concrete (voids, cracks, rebars, and concrete) with great accuracy. 15 refs., 10 figs.

An efficient method for accurately calculating the center-of-rotation, or projection center, for parallel computed tomography projection data, or sinograms, is described. This method uses all the data in the sinogram to estimate the center by a least-squares technique and requires no previous calibration scan. The method also finds the object's center-of-mass without reconstructing its image. Since the method uses the measured data, it is sensitive to noise in the measurements, but that sensitivity is relatively small compared to other techniques. Examples of its use on simulated and actual data are included. For fan-beam data over 360 degrees , two related methods are described to find the center in the presence or absence of a midline offset. >

x-Ray computed axial tomography (CT) provides cross-sectional views of materials, components, and assemblies for industrial non-destructive evaluation. We have applied CT imaging to quantitatively measure the 3-D distribution ogf x-ray attenuation at reasonably high resolutions. In our industrial x-ray CT-studies, we have centered on two technical approaches: a first-generation translate/rotate CT system that consist of well-collimated (∼ 0.55 mm) photon source detector, and a third-generation scanner that uses a fluoroscopy detector.

High energy density experiments, such as those planned at the National Ignition Facility (NIF), use mesoscale targets with the goals of studying high energy density physics, inertial confinement fusion, and the support of national security needs. Mesoscale targets are typically several millimeters in size and have complex micrometer‐sized structures composed of high‐density metals and low‐density foams and ices. These targets are designed with exacting tolerances that are difficult to achieve at present. Deviation from these tolerances can result in compromise of experimental goals and thus it is necessary to determine as‐built properties of these targets using NDE techniques. Radiography and computed tomography are being used to investigate these targets, but the mix between phase and absorption information is difficult to separate, making interpretation of results difficult. We have recently improved the HADES radiographic simulation code to include phase in simulations, as an aid for doing NDE on mesos...

A class of hybrid domain-iterative algorithms for 2-D image reconstruction is described. The consistent iterative reconstruction-reprojection (CIRR) algorithm was designed and implemented along with other classical domain-iterative methods. The algorithms are tested on the hollow projections problem, where simple object models are assumed known. Results show that the CIRR algorithm is robust and accurate, and converges quickly. >

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.