Publications

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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.

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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.

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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.

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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.

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.

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