# Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
## 4 Discussion
Wide energy bins have lower noise but suffer from poor contrast in the reconstructed images. In single photon counting detectors, a photon is only counted if the signal pulse is higher than the user specified energy threshold. However, the signal created by a single X-ray photon could be registered simultaneously by all energy thresholds depending on photon energy. Therefore, the selection of energy ranges needs to be balanced with the requirements for achieving desirable contrast and material's K-edge. Moreover, CT reconstruction for spectral CT imaging needs to be performed in subtracted energy bins to reconstruct the difference of the photon counts between the two subsequent counters. It is difficult to pinpoint the exact cause of misidentification but factors such as X-ray scatter, Compton scattering, K-fluorescence X-rays from high-Z semiconductor materials and imperfections of the CdTe detector (pulse pileup, charge trapping, semiconductor detector inhomogenities, and crosstalk within the semiconductor material) are likely to play a part. All these effects are intrinsic to detector properties and not all of them can be compensated by flat field correction, especially energy-dependent effects. They contribute to misidentification of materials in material decomposition. Image related artefacts also have negative effects on the material decomposition. Other groups [25; 63; 64] have reported noisy regions with incorrect material identification of high-Z materials but they claimed their ability to detect low concentrations of high-Z materials with high accuracy by measuring the concentrations in decomposed images and comparing it to the true concentration value. Our results show that good correlation over the full range of the known and measured concentrations fails to provide information about the misidentification between different materials and the extent of such misidentification in decomposed material images. Results from the misidentification chart can be used to choose contrast materials best suited to a spectral CT imaging task in relation to the anticipated concentration of those materials. We can use the binary information to quantitatively compute sensitivity and specificity performance of the (data acquisition and data processing) system for target materials of different concentrations and composition and scanned with different imaging protocols. The materials selected for this study are commonly used for radiology related studies due to their attenuation properties. A shortcoming of the study could be the evaluation of a limited number of high-Z materials and limited image acquisition protocols. Despite these limitations, we showed that the technique can be applied to various concentrations of high-Z materials simultaneously and can be used (1) as a quality assurance tool to set up a threshold for lower detectable limits of any given material in the post-MD domain; (2) for optimization of the spectral CT imaging parameters and acquisition protocol to maximize the sensitivity and specificity of the given materials; and (3) to establish a baseline of misidentification measurement for daily QA of spectral CT scanners. The ground truth for the identity and concentration of all materials in a complex biological specimen is frequently unknown. There are many potential sources of error in spectral CT analysis of compositionally complex specimens, including the MD method itself. This paper describes a new method that measures the net effect of all these sources of error on the ability to identify and quantify materials at spectral CT. We have successfully tested the method in both a phantom and a biological sample. We have demonstrated its utility for one material differentiation method applied to two scanning protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT.