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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