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X-ray attenuation properties of matter (i.e. human body in medicalComputed Tomography) are energy and material dependent. This dependency is largely neglected in conventional CT techniques, which require the introduction of correction algorithms in order to prevent image artefacts. The exploitation of the inherent energy information contained in the x-ray spectrum allows distinguishing the two main physical causes of energy-dependent attenuation (photo-electric effect and Compton effect). Currently a number of methods exist that allow assessing the energy-dependent attenuation in conventional systems. These methods consist of using two distinct spectra (kVp switching ordual source) or by discriminating low and high energy photons by means of stacking two detectors. Further improvements can be achievedby transitioning to direct-conversion technologies and counting-modedetection, which inherently exhibits a better signal-to-noise ratio.Further including energy discrimination, enables new applications,which are not feasible with dual-energy techniques, e.g. the possibility to discriminate K-edge features (contrast agents, e.g. Gadolinium) from the other contributions to the x-ray attenuation of a human body. The capability of providing energy-resolved information withtwo or more independent measurements is referred as Spectral CT.A new proprietary photon counting ASIC (ChromAIX) has been developedto provide high count-rate capabilities while offering energy discrimination. The ChromAIX consists of a pixel array with an isotropicpitch of 300 µm. Each pixel contains independent discriminators which enable the possibility to discretize incoming photons into a number of energy levels. Extensive electrical characterization has been carried out to assess the performance in terms of count-rate performance and noise. Observed rates exceeding 10 Mcps/pixel (Poissonian, mean incoming rates > 27 Mcps). The energy resolution is better than4.1 keV FWHM and has been shown to be consistent with simulations. Pile-up behaviour and count-rate dependency have also been evaluated. Electrical crosstalk among pixels in terms of count-rate activity and threshold position has been assessed and show no measureable influences across the array. X-ray tests have also been performed onsamples directly flip-chip bonded to CdTe and CZT crystals. The pulse shape and spectrum obtained from a 241Am source is consistent with simulations.

A number of different methods for post reconstruction bone beam hardening (BBH) correction are available for conventional FBP reconstruction and are used in commercially available products. An incorporation of these existing methods into statistical iterative reconstruction for CT is desired for several reasons. There are two ways imaginable to incorporate the BBH correction into iterative reconstruction:The first option is to use the beam hardening corrected projectionsas input for the statistical iterative reconstruction. For this ithas to be considered that the noise level in the projection data changes due to the correction. The second option is to incorporate theinverse of the beam hardening correction into the forward projectionof the cost function, and derive an update equation from this modified cost function. Both methods are implemented and compared based on simulated data with respect to artifact suppression, image noise,and speed of convergence.

Circular scanning with an off-center planar detector is an acquisi-tion scheme that allows to save detector area while keeping a largefield of view (FOV). Several filtered back-projection (FBP) algorithmshave been proposed earlier. The purpose of this work is to present twonewly developed back-projection filtration (BPF) variants and evaluatethe image quality of these methods compared to the existing state-of-the-art FBP methods.

Electrophysiological simulations of the atria could improve diagnosis and treatment of cardiac arrhythmia, like atrial fibrillation or flutter. For this purpose, a precise segmentation of both atria is needed. However, the atrial epicardium and the electrophysiological structures needed for electrophysiological simulations are barely or not at all detectable in CT-images. Therefore, a model based segmentation of only the atrial endocardium was developed as a landmark generator to facilitate the registration of a finite wall thickness model of the right and left atrial myocardium. It further incorporates atlas information about tissue structures relevant for simulation purposes like Bachmann’s bundle, terminal crest, sinus node and the pectinate muscles. The correct model based segmentation of the atrial endocardium was achieved with a mean vertex to surface error of 0.53 mm for the left and 0.18 mm for the right atrium respectively. The atlas based myocardium segmentation yields physiologically correct results well suited for electrophysiological simulations.

Purpose: X-ray based tomographic blood perfusion imaging requires recovery of contrast time-attenuation-curves from dynamic projection data. When using slowly rotating imaging systems this task is challenging due to non-simultaneous projection acquisition. A dynamic reconstruction method is proposed that aims at compensating the lack of simultaneously acquired information by incorporating prior knowledge about the expected temporal contrast dynamics. Methods: A decomposition model using temporal basis functions to approximate time-attenuation-curves is integrated into an iterative tomographicre construction method. The computationally efficient implementationof the proposed approach makes use of standard forward- and back projections as well as scalar products in image space. The critical issue of projection noise propagation is tackled by application of regularization which is realizedby early stopping of iteration cycles and by proper selection of smooth temporal basis functions. The performance of the proposed dynamic reconstruction approach is evaluated in a simulation study concerning various aspects: noise propagation and regularization, specification of temporal model, and type of acquisition mode. Results: The evaluation based on dynamic phantom data indicates that tomographic recovery of contrast time-attenuation-curves in tissue can be achieved with an average range of accuracy of ca. 2% (with respect todynamic peak attenuation) under ideal noise-free conditions. The relative estimation error for arterial time-attenuation-curves is in the range of 8%, which is due to faster contrast dynamics in the artery. In general, performance depends on the level of acquired information contained in the projection data which is mainly influenced by the type of rotational acquisition mode; restrictions in angular range and speed can lead to limited accuracy. The analysis of propagated projection noise in a statistical Bias-Variance framework reveals relative noise levels in estimated time-attenuation-curves of 3-4% intissue regions and below 1% in vessels when using optimized settingsfor regularization. Here, the effect of noise suppression depends oninterrelation between the model. Conclusions: For usage with slowly rotating imaging systems the presented model-based iterative dynamic reconstruction method is capable of recovering contrast time-attenuation-curves related to tissue perfusion. The proposed regularization framework is an effective means to limit the impact of projection noise which is a factor dominating estimation accuracy in tissue regions.