This work proposes a dedicated statistical algorithm to perform a direct reconstruction of material-decomposed images from data acquired with photon-counting detectors (PCDs) in computed tomography. It is based on local approximations (surrogates) of the negative logarithmic Poisson probability function. Exploiting the convexity of this function allows for parallel updates of all image pixels. Parallel updates can compensate for the rather slow convergence that is intrinsic to statistical algorithms. We investigate the accuracy of the algorithm for ideal photon-counting detectors. Complementarily, we apply the algorithm to simulation data of a realistic PCD with its spectral resolution limited by K-escape, charge sharing, and pulse-pileup. For data from both an ideal and realistic PCD, the proposed algorithm is able to correct beam-hardening artifacts and quantitatively determine the material fractions of the chosen basis materials. Via regularization we were able to achieve a reduction of image noise for the realistic PCD that is up to 90% lower compared to material images form a linear, image-based material decomposition using FBP images. Additionally, we find a dependence of the algorithms convergence speed on the threshold selection within the PCD.

A semi-analytical model describing spectral distortions in photon-counting detectors (PCDs) for clinical computed tomography was evaluated using simulated data. The distortions were due to count rate-independent spectral response effects and count rate-dependent pulse-pileup effects and the model predicted both the mean count rates and the spectral shape. The model parameters were calculated using calibration data. The model was evaluated by comparing the predicted x-ray spectra to Monte Carlo simulations of a PCD at various count rates. The data-model agreement expressed as weighted coefficient of variation [Formula: see text] was better than [Formula: see text] for dead time losses up to 28% and [Formula: see text] or smaller for dead time losses up to 69%. The accuracy of the model was also tested for the purpose of material decomposition by estimating material thicknesses from simulated projection data. The estimated attenuator thicknesses generally agreed with the true values within one standard deviation of the statistical uncertainty obtained from multiple noise realizations.

Spectral computed tomography (CT) with photon-counting detectors (PCDs) has the potential to substantially advance diagnostic CT imaging by reducing image noise and dose to the patient, by improving contrast and tissue specificity, and by enabling molecular and functional imaging. However, the current PCD technology is limited by two main factors: imperfect energy measurement (spectral response effects, SR) and count rate non-linearity (pulse pileup effects, PP, due to detector deadtimes) resulting in image artifacts and quantitative inaccuracies for material specification. These limitations can be lifted with image reconstruction algorithms that compensate for both SR and PP. A prerequisite for this approach is an accurate model of the count losses and spectral distortions in the PCD. In earlier work we developed a cascaded SR-PP model and evaluated it using a physical PCD. In this paper we show the robustness of our approach by modifying the cascaded SR-PP model for a faster PCD with smaller pixels and a different pulse shape. We compare paralyzable and non-paralyzable detector models. First, the SR-PP model is evaluated at low and high count rates using two sets of attenuators. Then, the accuracy of the compensation is evaluated by estimating the thicknesses of three basis functions.

Photon counting detectors are expected to bring along various clinical benefits in CT imaging. Among the benefits of these detectors is their intrinsic spectral sensitivity that allows to resolve the incident X-ray spectrum. Their capability for multi-energy imaging enables material segmentation, but it is also possible to use the spectral information to create fused gray-scale CT images with improved imaging properties.We have developed and investigated an optimization method that maximizes the image contrast-to-noise ratio, making use of the spectral information in data recorded with a counting detector with up to six energy thresholds. The resulting merged gray-scale CT images exhibit significantly improved CNR2 for a number of clinically established, potentially novel and hypothetical contrast agents in the thin absorber approximation.In this work we motivate and describe the optimization method, provide the deduced optimal sets of threshold energies and mixing weights, and summarize the maximally achievable gain in CNR2 for each contrast agent under study.

We have investigated the multi-energy performance of our most recent prototype CT scanner with CdTe-based counting detector. With its small pixel pitch of 225 μm this device is prepared for the high X-ray fluxes occurring in clinical CT. Each of these pixels is equipped with two adjustable counters. The ASIC architecture of the detector allows configuration of the counter thresholds in chess patterns, enabling data acquisition in up to four energy bins. We have studied the material separation capability of counting CT with respect to potential clinical applications. Therefore we have analyzed contrast and noise properties in material decomposed CT images using up to four base materials. We have studied contrast agents containing iodine, gadolinium, or gold, and the body-like materials calcium, fat, and water. We describe the mathematical framework used in this work and demonstrate the general multi-energy capability of counting CT with simulations and experimental data from our prototype scanner. To prove the clinical relevance of our studies we compare the results to those obtained with well-established dual-kVp techniques recorded at same patient dose and with identical image sharpness.

In clinical computed tomography (CT), images from patient examinations taken with conventional scanners exhibit noise characteristics governed by electronics noise, when scanning strongly attenuating obese patients or with an ultra-low X-ray dose. Unlike CT systems based on energy integrating detectors, a system with a quantum counting detector does not suffer from this drawback. Instead, the noise from the electronics mainly affects the spectral resolution of these detectors. Therefore, it does not contribute to the image noise in spectrally non-resolved CT images. This promises improved image quality due to image noise reduction in scans obtained from clinical CT examinations with lowest X-ray tube currents or obese patients. To quantify the benefits of quantum counting detectors in clinical CT we have carried out an extensive simulation study of the complete scanning and reconstruction process for both kinds of detectors. The simulation chain encompasses modeling of the X-ray source, beam attenuation in the patient, and calculation of the detector response. Moreover, in each case the subsequent image preprocessing and reconstruction is modeled as well. The simulation-based, theoretical evaluation is validated by experiments with a novel prototype quantum counting system and a Siemens Definition Flash scanner with a conventional energy integrating CT detector. We demonstrate and quantify the improvement from image noise reduction achievable with quantum counting techniques in CT examinations with ultra-low X-ray dose and strong attenuation.

Contrary to conventional energy integrating detectors, electronics noise in quantum counting detectors (also frequently referred to as photon counting detectors) mainly affects the spectral resolution of the detector. There is almost no impact on the counting signal itself. This promises improved image quality due to image noise reduction in scans obtained from clinical computed tomography (CT) examinations with lowest X-ray tube currents or strongly attenuating obese patients. In most of these examinations, noise from the electronics dominates the image noise when using conventional detectors. Applying quantum counting detectors instead can improve image quality of ultra low-dose scans. This improvement may as well be used to reduce X-ray dose while maintaining image noise on the level of conventional detectors. To quantify these benefits, we have simulated sinograms of various slice scans of the human body, using the parametric 3D XCAT phantom (abdomen, shoulders) and a geometric DRASIM phantom (cranium). The simulation chain includes modeling the X-ray source, beam attenuation in the patient, and calculation of the detector response followed by data corrections and image reconstruction.We quantify the image noise in selected Region Of Interest (ROI) in the difference image of two scans that differ only in their image noise realization. Furthermore, we provide a direct comparison of image noise in energy integrating and quantum counting CT system concepts.