Publications

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Hyperspectral Computed Tomography (HCT) Data is often visualized using dimension reduction algorithms. However, these methods often fail to adequately differentiate between materials with similar spectral signatures. Previous work showed that a combination of image preprocessing, clustering, and dimension reduction techniques can be used to colorize simulated HCT data and enhance the contrast between similar materials. In this work, we evaluate the efficacy of these existing methods on experimental HCT data and propose new improvements to the robustness of these methods. We introduce an automated channel selection method and compare the Feldkamp, Davis, and Kress filtered back-projection (FBP) algorithm with the maximum-likelihood estimation-maximization (MLEM) algorithm in terms of HCT reconstruction image quality and its effect on different colorization methods. Additionally, we propose adaptations to the colorization process that eliminate the need for a priori knowledge of the number distinct materials for material classification. Our results show that these methods generalize to materials in real-world experimental HCT data for both colorization and classification tasks; both tasks have applications in industry, medicine, and security, wherever rapid visualization and identification is needed.

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Industrial and security communities leverage x-ray computed tomography for several applications in non-destructive evaluation such as material detection and metrology. Many of these applications ultimately reach a limit as most x-ray systems have a nonlinear mathematical operator due to the Bremsstrahlung radiation emitted from the x-ray source. This work proposes a design of a multi-metal pattered anode coupled with a hyperspectral X-ray detector to improve spatial resolution, absorption signal, and overall data quality for various quantitative. The union of a multi-metal pattered anode x-ray source with an energy-resolved photon counting detector permits the generation and detection of a preferential set of X-ray energy peaks. When photons about the peaks are detected, while rejecting photons outside this neighborhood, the overall quality of the image is improved by linearizing the operator that defines the image formation. Additionally, the effective X-ray focal spot size allows for further improvement of the image quality by increasing resolution. Previous works use machine learning techniques to analyze the hyperspectral computed tomography signal and reliably identify and discriminate a wide range of materials based on a material's composition, improving data quality through a multi-material pattern anode will further enhance these identification and classification methods. This work presents initial investigations of a multi-metal patterned anode along with a hyperspectral detector using a general-purpose Monte Carlo particle transport code known as PHITS version 3.24. If successful, these results will have tremendous impact on several nondestructive evaluation applications in industry, security, and medicine.

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.

Industrial and security communities leverage x-ray computed tomography for several applications in non-destructive evaluation such as material detection and metrology. Many of these applications ultimately reach a limit as most x-ray systems have a nonlinear mathematical operator due to the Bremsstrahlung radiation emitted from the x-ray source. This work proposes a design of a multi-metal pattered anode coupled with a hyperspectral X-ray detector to improve spatial resolution, absorption signal, and overall data quality for various quantitative. The union of a multi-metal pattered anode x-ray source with an energy-resolved photon counting detector permits the generation and detection of a preferential set of X-ray energy peaks. When photons about the peaks are detected, while rejecting photons outside this neighborhood, the overall quality of the image is improved by linearizing the operator that defines the image formation. Additionally, the effective X-ray focal spot size allows for further improvement of the image quality by increasing resolution. Previous works use machine learning techniques to analyze the hyperspectral computed tomography signal and reliably identify and discriminate a wide range of materials based on a material's composition, improving data quality through a multi-material pattern anode will further enhance these identification and classification methods. This work presents initial investigations of a multi-metal patterned anode along with a hyperspectral detector using a general-purpose Monte Carlo particle transport code known as PHITS version 3.24. If successful, these results will have tremendous impact on several nondestructive evaluation applications in industry, security, and medicine.