Monte-Carlo modeling and design of a high-resolution hyperspectral computed tomography system with multi-material patterned anodes for material identification applications

Gabriella M. Dalton; Noelle M. Collins; Joshua M. Clifford; Emily L. Kemp; Ben Limpanukorn; Edward S. Jimenez

doi:10.1117/12.2593949

9 September 2021 Monte-Carlo modeling and design of a high-resolution hyperspectral computed tomography system with multi-material patterned anodes for material identification applications

Gabriella M. Dalton, Noelle M. Collins, Joshua M. Clifford, Emily L. Kemp, Ben Limpanukorn, Edward S. Jimenez

Author Affiliations +

Proceedings Volume 11840, Developments in X-Ray Tomography XIII; 118400H (2021) https://doi.org/10.1117/12.2593949
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

The goal of this work is to develop an x-ray computed tomography (CT) capability that delivers improved imaging resolutions while reliably identifying the material composition of the interrogated object. Through the use of a hyperspectral x-ray detector along with a multi-metal patterned anode, one can simultaneously enhance achievable spatial resolution and improve the spectral signal through the use of energy intervals that capture the k-lines of each material present in the anode. This paper will present preliminary Monte-Carlo results of the anode design and simulated CT datasets along with the applied machine learning techniques to identify materials and their concentrations.

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Gabriella M. Dalton, Noelle M. Collins, Joshua M. Clifford, Emily L. Kemp, Ben Limpanukorn, and Edward S. Jimenez "Monte-Carlo modeling and design of a high-resolution hyperspectral computed tomography system with multi-material patterned anodes for material identification applications", Proc. SPIE 11840, Developments in X-Ray Tomography XIII, 118400H (9 September 2021); https://doi.org/10.1117/12.2593949

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