High-Fidelity Calibration and Characterization of a Hyperspectral Computed Tomography System
DescriptionThis work presents a numerical method to characterize 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 imaging system matrices across unique and narrow energy windows. H-CT has various applications in the non-destructive analysis of materials and objects in fields such as national security, industry, and medicine, but acquiring physical H-CT data requires significant time and money. Additionally, many approaches to CT make gross assumptions about the image formation process in order to apply post-processing and reconstruction techniques that lead to inferior data, resulting in faulty measurements, assessments, and quantifications. Through the analysis of the point source response for each energy channel at each location in the field of view, we present a linear model that describes the H-CT 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. Successfully approximating the encoding operator for the H-CT system through a point spread distribution enables quick assessment of H-CT behavior for various applications in high-performance reconstruction, sensitivity analysis, and machine learning. This project was conducted at Sandia National Laboratories (SNL). SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.