Publications

Abstract not provided.

Applications such as counterfeit identification, quality control, and non-destructive material identification benefit from improved spatial and compositional analysis. X-ray Computed Tomography is used in these applications but is limited by the X-ray focal spot size and the lack of energy-resolved data. Recently developed hyperspectral X-ray detectors estimate photon energy, which enables composition analysis but lacks spatial resolution. Moving beyond bulk homogeneous transmission anodes toward multi-metal patterned anodes enables improvements in spatial resolution and signal-to-noise ratios in these hyperspectral X-ray imaging systems. We aim to design and fabricate transmission anodes that facilitate confirmation of previous simulation results. These anodes are fabricated on diamond substrates with conventional photolithography and metal deposition processes. The final transmission anode design consists of a cluster of three disjoint metal bumps selected from molybdenum, silver, samarium, tungsten, and gold. These metals are chosen for their k-lines, which are positioned within distinct energy intervals of interest and are readily available in standard clean rooms. The diamond substrate is chosen for its high thermal conductivity and high transmittance of X-rays. The feature size of the metal bumps is chosen such that the cluster is smaller than the 100 m diameter of the impinging electron beam in the X-ray tube. This effectively shrinks the X-ray focal spot in the selected energy bands. Once fabricated, our transmission anode is packaged in a stainless-steel holder that can be retrofitted into our existing X-ray tube. Innovations in anode design enable an inexpensive and simple method to improve existing X-ray imaging systems.

Applications such as counterfeit identification, quality control, and non-destructive material identification benefit from improved spatial and compositional analysis. X-ray Computed Tomography is used in these applications but is limited by the X-ray focal spot size and the lack of energy-resolved data. Recently developed hyperspectral X-ray detectors estimate photon energy, which enables composition analysis but lacks spatial resolution. Moving beyond bulk homogeneous transmission anodes toward multi-metal patterned anodes enables improvements in spatial resolution and signal-to-noise ratios in these hyperspectral X-ray imaging systems. We aim to design and fabricate transmission anodes that facilitate confirmation of previous simulation results. These anodes are fabricated on diamond substrates with conventional photolithography and metal deposition processes. The final transmission anode design consists of a cluster of three disjoint metal bumps selected from molybdenum, silver, samarium, tungsten, and gold. These metals are chosen for their k-lines, which are positioned within distinct energy intervals of interest and are readily available in standard clean rooms. The diamond substrate is chosen for its high thermal conductivity and high transmittance of X-rays. The feature size of the metal bumps is chosen such that the cluster is smaller than the 100 m diameter of the impinging electron beam in the X-ray tube. This effectively shrinks the X-ray focal spot in the selected energy bands. Once fabricated, our transmission anode is packaged in a stainless-steel holder that can be retrofitted into our existing X-ray tube. Innovations in anode design enable an inexpensive and simple method to improve existing X-ray imaging systems.

Abstract not provided.

Abstract not provided.

Abstract not provided.

We present a deep learning image reconstruction method called AirNet-SNL for sparse view computed tomography. It combines iterative reconstruction and convolutional neural networks with end-to-end training. Our model reduces streak artifacts from filtered back-projection with limited data, and it trains on randomly generated shapes. This work shows promise to generalize learning image reconstruction.

X-ray phase contrast imaging (XPCI) is a nondestructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating-based Talbot-Lau XPCI system to three-dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of the image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Sandia National Laboratories is developing a laboratory-based x-ray phase contrast imaging (XPCI) computed tomography (CT) system. This system utilizes a Talbot-Lau interferometer based on in-house fabricated gratings and a conventional x-ray system. Initial work has focused on adding CT capabilities to a 28 keV XPCI system. A new set of gratings tuned for an x-ray energy of 100 keV is being developed. This new grating set will facilitate imaging denser components. System configuration details will be presented as well as a discussion of the challenges associated with building an XPCI CT system. Additionally, initial imaging results will be presented.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Sandia National Laboratories has developed a model characterizing the nonlinear encoding operator of the world's first hyperspectral x-ray computed tomography (H-CT) system as a sequence of discrete-to-discrete, linear image system matrices across unique and narrow energy windows. In fields such as national security, industry, and medicine, H-CT has various applications in the non-destructive analysis of objects such as material identification, anomaly detection, and quality assurance. However, many approaches to computed tomography (CT) make gross assumptions about the image formation process to apply post-processing and reconstruction techniques that lead to inferior data, resulting in faulty measurements, assessments, and quantifications. To abate this challenge, Sandia National Laboratories has modeled the H-CT system through a set of point response functions, which can be used for calibration and anaylsis of the real-world system. This work presents the numerical method used to produce the model through the collection of data needed to describe the system; the parameterization used to compress the model; and the decompression of the model for computation. By using this linear model, large amounts of accurate synthetic H-CT data can be efficiently produced, greatly reducing the costs associated with physical H-CT scans. Furthermore, successfully approximating the encoding operator for the H-CT system enables quick assessment of H-CT behavior for various applications in high-performance reconstruction, sensitivity analysis, and machine learning.

High-quality image products in an X-Ray Phase Contrast Imaging (XPCI) system can be produced with proper system hardware and data acquisition. However, it may be possible to further increase the quality of the image products by addressing subtleties and imperfections in both hardware and the data acquisition process. Noting that addressing these issues entirely in hardware and data acquisition may not be practical, a more prudent approach is to determine the balance of how the apparatus may reasonably be improved and what can be accomplished with image post-processing techniques. Given a proper signal model for XPCI data, image processing techniques can be developed to compensate for many of the image quality degradations associated with higher-order hardware and data acquisition imperfections. However, processing techniques also have limitations and cannot entirely compensate for sub-par hardware or inaccurate data acquisition practices. Understanding system and image processing technique limitations enables balancing between hardware, data acquisition, and image post-processing. In this paper, we present some of the higher-order image degradation effects we have found associated with subtle imperfections in both hardware and data acquisition. We also discuss and demonstrate how a combination of hardware, data acquisition processes, and image processing techniques can increase the quality of XPCI image products. Finally, we assess the requirements for high-quality XPCI images and propose reasonable system hardware modifications and the limits of certain image processing techniques.

Sandia National Laboratories has developed a model characterizing the nonlinear encoding operator of the world's first hyperspectral x-ray computed tomography (H-CT) system as a sequence of discrete-to-discrete, linear image system matrices across unique and narrow energy windows. In fields such as national security, industry, and medicine, H-CT has various applications in the non-destructive analysis of objects such as material identification, anomaly detection, and quality assurance. However, many approaches to computed tomography (CT) make gross assumptions about the image formation process to apply post-processing and reconstruction techniques that lead to inferior data, resulting in faulty measurements, assessments, and quantifications. To abate this challenge, Sandia National Laboratories has modeled the H-CT system through a set of point response functions, which can be used for calibration and anaylsis of the real-world system. This work presents the numerical method used to produce the model through the collection of data needed to describe the system; the parameterization used to compress the model; and the decompression of the model for computation. By using this linear model, large amounts of accurate synthetic H-CT data can be efficiently produced, greatly reducing the costs associated with physical H-CT scans. Furthermore, successfully approximating the encoding operator for the H-CT system enables quick assessment of H-CT behavior for various applications in high-performance reconstruction, sensitivity analysis, and machine learning.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Sandia National Laboratories has developed a method that applies machine learning methods to high-energy spectral X-ray computed tomography data to identify material composition for every reconstructed voxel in the field-of-view. While initial experiments led by Koundinyan et al. demonstrated that supervised machine learning techniques perform well in identifying a variety of classes of materials, this work presents an unsupervised approach that differentiates isolated materials with highly similar properties, and can be applied on spectral computed tomography data to identify materials more accurately compared to traditional performance. Additionally, if regions of the spectrum for multiple voxels become unusable due to artifacts, this method can still reliably perform material identification. This enhanced capability can tremendously impact fields in security, industry, and medicine that leverage non-destructive evaluation for detection, verification, and validation applications.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

We are studying PMDI polyurethane with a fast catalyst, such that filling and polymerization occur simultaneously. The foam is over-packed to tw ice or more of its free rise density to reach the density of interest. Our approach is to co mbine model development closely with experiments to discover new physics, to parameterize models and to validate the models once they have been developed. The model must be able to repres ent the expansion, filling, curing, and final foam properties. PMDI is chemically blown foam, wh ere carbon dioxide is pr oduced via the reaction of water and isocyanate. The isocyanate also re acts with polyol in a competing reaction, which produces the polymer. A new kinetic model is developed and implemented, which follows a simplified mathematical formalism that decouple s these two reactions. The model predicts the polymerization reaction via condensation chemis try, where vitrification and glass transition temperature evolution must be included to correctly predict this quantity. The foam gas generation kinetics are determined by tracking the molar concentration of both water and carbon dioxide. Understanding the therma l history and loads on the foam due to exothermicity and oven heating is very important to the results, since the kinetics and ma terial properties are all very sensitive to temperature. The conservation eq uations, including the e quations of motion, an energy balance, and thr ee rate equations are solved via a stabilized finite element method. We assume generalized-Newtonian rheology that is dependent on the cure, gas fraction, and temperature. The conservation equations are comb ined with a level set method to determine the location of the free surface over time. Results from the model are compared to experimental flow visualization data and post-te st CT data for the density. Seve ral geometries are investigated including a mock encapsulation part, two configur ations of a mock stru ctural part, and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Thoughts on m odel improvements are also discussed.

Abstract not provided.