Abstract
Spectral x-ray imaging using novel photon counting x-ray detectors (PCDs) with energy resolving abilities is capable of providing energy-selective images. PCDs have energy thresholds, enabling the classification of photons into multiple energy bins. The extra energy information provided may allow materials such as iodine and calcium, or water and fat to be distinguishable. The information content of spectral x-ray images, however, depends on how the photons are grouped together. In this work, we present a model to optimize energy windows for maximum material discrimination. Multivariate statistics allows the confidence region of the correlated uncertainties to be mapped in the thickness space. Minimization of the uncertainties enables optimization of energy windows. Applications related to small animal imaging and breast imaging are considered.
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