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A Process to Colorize and Assess Visualizations of Noisy X-Ray Computed Tomography Hyperspectral Data of Materials with Similar Spectral Signatures

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022

Clifford, Joshua M.; Kemp, Emily K.; Limpanukorn, Ben L.; Jimenez, Edward S.

Dimension reduction techniques have frequently been used to summarize information from high dimensional hyperspectral data, usually done in effort to classify or visualize the materials contained in the hyperspectral image. The main challenge in applying these techniques to Hyperspectral Computed Tomography (HCT) data is that if the materials in the field of view are of similar composition then it can be difficult for a visualization of the hyperspectral image to differentiate between the materials. We propose novel alternative methods of preprocessing and summarizing HCT data in a single colorized image and novel measures to assess desired qualities in the resultant colored image, such as the contrast between different materials and the consistency of color within the same object. Proposed processes in this work include a new majority-voting method for multi-level thresholding, binary erosion, median filters, PAM clustering for grouping pixels into objects (of homogeneous materials) and mean/median assignment along the spectral dimension for representing the underlying signature, UMAP or GLMs to assign colors, and quantitative coloring assessment with developed measures. Strengths and weaknesses of various combinations of methods are discussed. These results have the potential to create more robust material identification methods from HCT data that has wide use in industrial, medical, and security-based applications for detection and quantification, including visualization methods to assist with rapid human interpretability of these complex hyperspectral signatures.

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TYPE Conference Proceeding YEAR 2021
ScopusOSTI

AirNet-SNL: End-to-end training of iterative reconstruction and deep neural network regularization for sparse-data XPCI CT

Optics InfoBase Conference Papers

Lee, Dennis J.; Mulcahy-Stanislawczyk, Johnathan M.; Jimenez, Edward S.; West, Roger D.; Goodner, Ryan; Epstein, Collin E.; Thompson, Kyle R.; Dagel, Amber L.

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.

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TYPE Conference Paper YEAR 2021
ScopusOSTIDOI

Big-Data Multi-Energy Iterative Volumetric Reconstruction Methods for As-Built Validation & Verification Applications

Jimenez, Edward S.

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.

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TYPE SAND Report YEAR 2018
OSTIDOI

Passenger baggage object database (PBOD)

AIP Conference Proceedings

Gittinger, Jaxon M.; Suknot, April S.; Jimenez, Edward S.; Spaulding, Terry W.; Wenrich, Steven A.

Detection of anomalies of interest in x-ray images is an ever-evolving problem that requires the rapid development of automatic detection algorithms. Automatic detection algorithms are developed using machine learning techniques, which would require developers to obtain the x-ray machine that was used to create the images being trained on, and compile all associated metadata for those images by hand. The Passenger Baggage Object Database (PBOD) and data acquisition application were designed and developed for acquiring and persisting 2-D and 3-D x-ray image data and associated metadata. PBOD was specifically created to capture simulated airline passenger "stream of commerce" luggage data, but could be applied to other areas of x-ray imaging to utilize machine-learning methods.

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TYPE Conference Poster YEAR 2018
ScopusOSTIDOI
Results 1–25 of 104
Results 1–25 of 104