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High-fidelity calibration and characterization of a spectral computed tomography system

Proceedings of SPIE - The International Society for Optical Engineering

Gallegos, Isabel O.; Dalton, Gabriella D.; Stohn, Adriana M.; Koundinyan, Srivathsan P.; Thompson, Kyle R.; Jimenez, Edward S.

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.

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TYPE Conference Poster YEAR 2019
ScopusOSTI

Unsupervised learning methods to perform material identification tasks on spectral computed tomography data

Proceedings of SPIE - The International Society for Optical Engineering

Gallegos, Isabel O.; Koundinyan, Srivathsan P.; Suknot, April S.; Jimenez, Edward S.; Thompson, Kyle R.; Goodner, Ryan N.

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.

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TYPE Conference Poster YEAR 2018
ScopusOSTI
14 Results
14 Results