Abstract

Objective. Computed tomography (CT) has advanced since its inception, with breakthroughs such as dual-energy CT (DECT), which extracts additional information by acquiring two sets of data at different energies. As high-flux photon-counting detectors (PCDs) become available, PCD-CT is also becoming a reality. PCD-CT can acquire multi-energy data sets in a single scan by spectrally binning the incident x-ray beam. With this, K-edge imaging becomes possible, allowing high atomic number (high-Z) contrast materials to be distinguished and quantified. In this study, we demonstrated that DECT methods can be converted to PCD-CT systems by extending the method of Bourque et al (2014). We optimized the energy bins of the PCD for this purpose and expanded the capabilities by employing K-edge subtraction imaging to separate a high-atomic number contrast material. Approach. The method decomposes materials into their effective atomic number (Z eff) and electron density relative to water (ρ e ). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with known ρ e values and elemental compositions. Theoretical Z eff values were found for the appropriate energy ranges using the elemental composition of the materials. Z eff varied slightly with energy but was considered a systematic error. An ex vivo bovine tissue sample was decomposed to evaluate the model further and was injected with gold chloride to demonstrate the separation of a K-edge contrast agent. Main results. The mean root mean squared percent errors on the extracted Z eff and ρ e for PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in the ex vivo bovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from the ex vivo tissue sample with K-edge imaging. Significance. PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging.

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