ABSTRACT
PURPOSE: To develop and validate a multivariate statistical method to optimize scanning parameters for material quantification in spectral x-rayimaging. METHODS: An optimization metric was constructed by extensively sampling the thickness space for the expected number of counts for m (two or three) materials. This resulted in an m-dimensional confidence region ofmaterial quantities, e.g. thicknesses. Minimization of the ellipsoidal confidence region leads to the optimization of energy bins. For the given spectrum, the minimum counts required for effective material separation can be determined by predicting the signal-to-noise ratio (SNR) of the quantification. A Monte Carlo (MC) simulation framework using BEAM was developed to validate the metric. Projection data of the m-materials was generated and material decomposition was performed for combinations of iodine, calcium and water by minimizing the z-score between the expected spectrum and binned measurements. The mean square error (MSE) and variance were calculated to measure the accuracy and precision of this approach, respectively. The minimum MSE corresponds to the optimal energy bins in the BEAM simulations. In the optimization metric, this is equivalent to the smallest confidence region. The SNR of the simulated images was also compared to the predictions from the metric. RESULTS: TheMSE was dominated by the variance for the given material combinations,which demonstrates accurate material quantifications. The BEAMsimulations revealed that the optimization of energy bins was accurate to within 1keV. The SNRs predicted by the optimization metric yielded satisfactory agreement but were expectedly higher for the BEAM simulations due to the inclusion of scattered radiation. CONCLUSIONS: The validation showed that the multivariate statistical method provides accurate material quantification, correct location of optimal energy bins and adequateprediction of image SNR. The BEAM code system is suitable for generating spectral x- ray imaging simulations.
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.
