Publications
Big-Data Multi-Energy Iterative Volumetric Reconstruction Methods for As-Built Validation & Verification Applications
This document archives the results developed by the Lab Directed R esearch and Develop- ment (LDRD) project sponsored by Sandia National Laboratories (SNL). In this work, it is shown that SNL has developed the first known high-energy hyper spectral computed to- mography system for industrial and security applications. The main results gained from this work include dramatic beam-hardening artifact reduction by using t he hyperspectral recon- struction as a bandpass filter without the need for any other comp utation or pre-processing; additionally, this work demonstrated the ability to use supervised an d unsupervised learning methods on the hyperspectral reconstruction data for the app lication of materials charac- terization and identification which is not possible using traditional com puted tomography systems or approaches.