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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 this paper, based on an invited talk at SORMA (May 2016), we present an overview of x-ray sources, detectors and system configurations for non-intrusive inspection (NII) of cargo containers. Our emphasis is on dual-energy x-ray NII for detecting high-atomic-number ( $\text {Z}\geq 72$ ) materials such as tungsten shielding and special nuclear materials (SNM). Standard single-energy (MeV and above) x-rays needed to penetrate and image cargo provide little SNM contrast, whereas dual-energy x-ray NII is demonstrated as a way to improve the selectivity of materials with Z $\text {Z}\geq 72$ . For two possible dual-energy x-ray source technologies—polyenergetic dual-energy (PDE) and quasi-monoenergetic x-ray sources (QMXS)—we investigate their trade-offs and future prospects using experimental and simulated results. The reduced scatter and larger separation of low- and high-energy photons provided by QMXS offers improved high-Z material contrast, but practical considerations such as flux and pulse rate need to be solved before making a deployable system. Straight-ray simulations show factor of four increases in contrast for QMXS over PDE scans of tin (Z=50) and iron (Z=26) relative to a uranium plate (Z=92) behind 20 cm of iron simulated cargo.

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%).

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.

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.

Recent advances in cadmium telluride (CdTe) energy-discriminating pixelated detectors have enabled the possibility of Multi-Spectral X-ray Computed Tomography (MSXCT) to incorporate spectroscopic information into CT. MultiX ME 100 V2 is a CdTe-based spectroscopic x-ray detector array capable of recording energies from 20 to 160 keV in 1.1 keV energy bin increments. Hardware and software have been designed to perform radiographic and computed tomography tasks with this spectroscopic detector. Energy calibration is examined using the end-point energy of a bremsstrahlung spectrum and radioisotope spectral lines. When measuring the spectrum from Am-241 across 500 detector elements, the standard deviation of the peak-location and FWHM measurements are ± 0.4 and ± 0.6 keV, respectively. As these values are within the energy bin size (1.1 keV), detector elements are consistent with each other. The count rate is characterized, using a nonparalyzable model with a dead time of 64 ± 5 ns. This is consistent with the manufacturer’s quoted per detector-element linear-deviation at 2 Mpps (million photons per sec) of 8.9 % (typical) and 12 % (max). When comparing measured and simulated spectra, a low-energy tail is visible in the measured data due to the spectral response of the detector. If no valid photon detections are expected in the low-energy tail, then a background subtraction may be applied to allow for a possible first-order correction. If photons are expected in the low-energy tail, a detailed model must be implemented. A radiograph of an aluminum step wedge with a maximum height of 20 mm shows an underestimation of attenuation by about 10 % at 60 keV. This error is due to partial energy deposition from higher energy (>60 keV) photons into a lower-energy (∼60 keV) bin, reducing the apparent attenuation. A radiograph of a polytetrafluoroethylene (PTFE) cylinder taken using a bremsstrahlung spectrum from an x-ray voltage of 100 kV filtered by 1.3 mm Cu is reconstructed using Abel inversion. As no counts are expected in the low energy tail, a first order background correction is applied to the spectrum. The measured linear attenuation coefficient (LAC) is within 10% of the expected value in the 60 to 100 keV range. Below 60 keV, low counts in the corrected spectrum and partial energy deposition from incident photons of energy greater than 60 keV into energy bins below 60 keV impact the LAC measurements. This report ends with a demonstration of the tomographic capability of the system. The quantitative understanding of the detector developed in this report will enable further study in evaluating the system for characterization of an object’s chemical make-up for industrial and security purposes.

We propose a method for material characterization using Spectral X-ray Computed Tomography (SCT). Our SCT method takes advantage of recently-developed MultiX ME 100 photon counting detectors to simultaneously measure the energy dependence of a material's linear attenuation coefficient (LAC). Relative electron density ( ρ e ) and effective atomic number (Ze) are estimated directly from the energy-dependent LAC measurements. The method employs a spectral correction algorithm and automated selection and weighting of the energy bins for optimized performance. When examining materials with Ze ≤ 23, this method achieves accuracy comparable to traditional dual-energy CT, which is often realized through consecutive data acquisitions, and is compatible with any spectral detector. The method disregards data in photon starved energy channels improving the detection of highly attenuating materials, compared to techniques that use energy integrating detectors.

X-ray imaging can be used to inspect cargos imported into the United States. In order to better understand the performance of X-ray inspection systems, the X-ray characteristics (density, complexity) of cargo need to be quantified. In this project, an image complexity measure called integrated power spectral density (IPSD) was studied using both DNDO engineered cargos and stream-of-commerce (SOC) cargos. A joint distribution of cargo density and complexity was obtained. A support vector machine was used to classify the SOC cargos into four categories to estimate the relative fractions.

X-ray inspection systems can be used to detect radiological and nuclear threats in imported cargo. In order to better understand performance of these systems, the attenuation characteristics of imported cargo need to be determined. This project focused on developing image processing algorithms for segmenting cargo and using x-ray attenuation to quantify equivalent steel thickness to determine cargo density. These algorithms were applied to over 450 cargo radiographs. The results are summarized in this report.

These are slides about in-situ X-ray CT results of damage evolution in L6 ordinary chondrite meteorites. The following topics are covered: mechanical and thermal damage characterization, list of Grosvenor Mountain (GRO) meteorite samples, in-situ x-ray compression test setup, GRO-chipped reference at 0 N - existing cracks, GRO-chipped loaded at 1580 N, in-situ x-ray thermal fatigue test setup, GRO-B14 room temperature reference, GRO-B14 Cycle 47 at 200°C, GRO-B14 Cycle 47 at room temperature, conclusions from qualitative analysis, future work and next steps. Conclusions are the following: Both GRO-Chipped and GRO-B14 had existing voids and cracks within the volume. These sites with existing damage were selected for CT images from mechanically and thermally loaded scans since they are prone to damage initiation. The GRO-Chipped sample was loaded to 1580 N which resulted in a 14% compressive engineering strain, calculated using LVDT. Based on the CT cross sectional images, the GRO-B14 sample at 200°C has a thermal expansion of approximately 96 μm in height (i.e. ~1.6% engineering strain).

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 (

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.

Laser experiments, such as those planned at the National Ignition Facility (NIF) and the Omega facility, use small targets with the goal of studying high-energy density physics and inertial confinement fusion. One particular application is a target with layers whose density changes in a carefully designed gradient (from 0.2 to 1.2 g/cm 3 ) for use in isentropic compression experiments (ICE). We are nondestructively determining the density of these layers using two X-ray microscopes. Because of the many interfaces that comprise the layers, a plethora of X-ray phase contrast fringes appear in the images, leading to many radiographic and tomographic artifacts which compromise the ability to infer the density of the layer. In this paper, we describe how we are attacking this problem with a variety of radiographic standards and through radiographic simulation using the HADES radiographic simulation code.

Indirectly driven double shell implosions are being investigated as a possible noncryogenic path to ignition on the National Ignition Facility. Lawrence Livermore National Laboratory has made several technological advances that have produced double shell targets that represent a significant improvement to previously fielded targets. The inner capsule is supported inside the ablator shell by SiO2 aerogel with a nominal density of 50 mg/cm3. The aerogel is cast around the inner capsule and then machined concentric to it. The seamless sphere of aerogel containing the embedded capsule is then assembled between the two halves of the ablator shell. The concentricity between the two shells has been improved to less than 1.5 μm. The ablator shell consists of two hemispherical shells that mate at a step joint that incorporates a gap with a nominal thickness of 0.1 μm. Using a new flexure-based tool holder that precisely positions the diamond cutting tool on the diamond turning machine, step discontinuities...

The goal of “as-built” computational modeling is to incorporate the most representative geometry and material information for an (fabricated or legacy) object into simulations. While most engineering finite element simulations are based on an object’s idealized “as-designed” configuration with information obtained from technical drawings or computer-aided design models, as-built modeling uses nondestructive characterization and metrology techniques to provide the feature information. By incorporating more representative geometry and material features as initial conditions, the uncertainty in the simulation results can be reduced, providing a more realistic understanding of the event and object being modeled. In this paper, key steps and technology areas in the as-built modeling framework are: (1) inspection using nondestructive characterization and metrology techniques; (2) data reduction (signal and image processing including artifact removal, data sensor fusion, and geometric feature extraction); and (3) engineering and physics analysis using finite element codes. We illustrate the process with a cylindrical phantom and include a discussion of the key concepts and areas that need improvement. Our results show that reasonable as-built initial conditions based on a volume overlap criteria can be achieved and that notable differences between simulations of the as-built and as-designed configurations can be observed for a given load case. Specifically, a volume averaged difference of accumulated plastic strain of 3% and local spatially varying differences up to 10%. The example presented provides motivation and justification to engineering teams for the additional effort required in the as-built modeling of high value parts. Further validation of the approach has been proposed as future work.

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.