Recent publications in the field of Computed Tomography (CT) demonstrate the rising interest in applying dual-energy methods for material classification during clinical routine examinations. Based on today's standard of technology, dual-energy CT can be realized by either scanning with different X-ray spectra or by deployment
of energy selective detector technologies. The list of so-called dual-kVp methods contains sequential scans, fast kVp-switching and dual-source CT. Examples of energy selective detectors are scintillator-based energyintegrating dual-layer devices or direct converter with quantum counting electronics. The general difference of
the approaches lies in the shape of the effectively detected X-ray energy spectra and in the presence of crossscatter
radiation in the case of dual-source devices. This leads to different material classification capabilities for the various techniques. In this work, we present detector response simulations of realistic CT scans with subsequent CT image reconstruction. Analysis of the image data allows direct and objective comparison of the
dual-kVp, dual-layer, and quantum counting CT system concepts. The dual-energy performance is benchmarked in terms of image noise and Iodine-bone separation power at given image sharpness and dose exposure. For the case of dual-source devices the effect of cross-scatter radiation, as well as the benefit of additional filtering are
taken into account.
We report the implementation and first test results of a two-channel spectral Computed Tomography (CT) prototype. We use an energy-resolving CT detector with a sandwich-like two layer set-up. Compared to dual-energy approaches with tube voltage switching, it yields a low and a high energy channel in a one shot measurement. We explain the basic set-up of the system and its calibration. The effects of spectral weighting are examined and the weighting functions w(E) of the detector channels are calculated. We present spectral image data of a water phantom, a set of calibration materials and an organic sample. Finally, we show how the data can be used for quantitative CT measurements. The system is work in progress and currently not available in the United States.