Purpose: Photon counting imaging detectors (PCD) has paved the way for spectral x-ray computed tomography (spectral CT), which simultaneously measures a sample’s linear attenuation coefficient (LAC) at multiple energies. However, cadmium telluride (CdTe)-based PCDs working under high flux suffer from detector effects, such as charge sharing and photon pileup. These effects result in the severe spectral distortions of the measured spectra and significant deviation of the extracted LACs from the reference attenuation curve. We analyze the influence of the spectral distortion correction on material classification performance.

Approach: We employ a spectral correction algorithm to reduce the primary spectral distortions. We use a method for material classification that measures system-independent material properties, such as electron density, ρ_e, and effective atomic number, Z_eff. These parameters are extracted from the LACs using attenuation decomposition and are independent of the scanner specification. The classification performance with the raw and corrected data is tested on different numbers of energy bins and projections and different radiation dose levels. We use experimental data with a broad range of materials in the range of 6  ≤  Z_eff  ≤  15, acquired with a custom laboratory instrument for spectral CT.

Results: We show that using the spectral correction leads to an accuracy increase of 1.6 and 3.8 times in estimating ρ_e and Z_eff, respectively, when the image reconstruction is performed from only 12 projections and the 15 energy bins approach is used.

Conclusions: The correction algorithm accurately reconstructs the measured attenuation curve and thus gives better classification performance.