Material decomposition with a prototype photon-counting detector CT system: expanding a stoichiometric dual-energy CT method via energy bin optimization and K-edge imaging.

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(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.The method decomposes materials into their effective atomic number () and electron density relative to water (). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with knownvalues and elemental compositions. Theoreticalvalues were found for the appropriate energy ranges using the elemental composition of the materials.varied slightly with energy but was considered a systematic error. Anbovine 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.The mean root mean squared percent errors on the extractedandfor PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in thebovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from thetissue sample with K-edge imaging.PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging.