Characterization and Energy Calibration of a Silicon-Strip Detector for Photon-Counting Spectral Computed Tomography

Abstract: Multibin photon-counting x-ray detectors are promising candidates to be applied in next generation computed tomography (CT), whereby energy information from a broad x-ray spectrum can be extracted and properly used for improving image quality and correspondingly reducing radiation dose. A silicon-strip detector has been developed for spectral CT, which operates in photon-counting mode and allows pulse-height discrimination with 8 adjustable energy bins.Critical characteristics, energy resolution and count-rate performance, of the detector are evaluated. An absolute energy resolution (E) from 1.5 keV to 1.9 keV with increasing x-ray energy from 40 keV to 120 keV is found. Pulse pileup degrades the energy resolution by 0.4 keV when increasing the input count rate from zero to 100 Mcps mm−2, while charge sharing shows negligible effect. A near linear relationship between the input and output count rates is observed up to 90 Mcps mm−2 in a clinical CT environment. In addition, no saturation effect appears for the maximally achieved photon flux of 485 Mphotons s−1 mm−2 with a count rate loss of 30%.The detector is energy calibrated in terms of gain and offset with the aid of monoenergetic x rays. The gain variation among channels is below 4%, whereas the variation of offsets is on the order of 1 keV. In order to do the energy calibration in a routinely available way, a method that makes use of the broad x-ray spectrum instead of using monoenergetic x rays is proposed. It is based on a regression analysis that adjusts a modelled spectrum of deposited energies to a measured pulse-height spectrum. Application of this method shows high potential to be applied in an existing CT scanner with an uncertainty of a calibrated threshold between 0.1 and 0.2 keV.The energy-calibration method is further used in the development of a spectral response model of the detector. This model is used to accurately bin-wise predict the response of each detector channel, which is validated by two application examples. First, the model is used in combination with the inhomogeneity compensation method to eliminate ring artefacts in CT images. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. Additionally, the contrast agent concentrations are reconstructed with more than 94% accuracy.