Monte Carlo validation of optimal material discrimination using spectral x-ray imaging
The aim of this work was to develop a framework to validate an algorithm for determination of optimal material discrimination in spectral x-ray imaging. Using Monte Carlo (MC) simulations based on the BEAMnrc package, material decomposition was performed on the projection images of phantoms containing up to three materials. The simulated projection data was first decomposed into material basis images by minimizing the z-score between expected and simulated counts. Statistical analysis was performed for the pixels within the region-of-interest consisting of contrast material(s) in the MC simulations. With the consideration of scattered radiation and a realistic scanning geometry, the theoretical optima of energy bin borders provided by the algorithm were shown to have an accuracy of ±2 keV for the decomposition of 2 and 3 materials. Finally, the signal-to-noise ratio predicted by the theoretical model was also validated. The counts per pixel needed for achieving a specific imaging aim can therefore be estimated using the validated model.