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Statistical, iterative reconstruction techniques have become a major research topic in the CT sector. These techniques promise a better system model, which is used for the inversion of the tomographic problem, and therefore better reconstruction results. Due to the ill–posedness of these problems, regularization is required in the cost functions in order to stabilize the algorithm and to reduce the noise in the resulting images. The strength of the regularization is usually changed by using an appropriate multiplicative factor, which in most cases has to be determined empirically with major efforts. This paper describes a new automated selection of this factor by using a quality criterion and a regulator, which controls the multiplicative factor over the iterations to a desired level. The method is light–weight, robust and also applicable for other iterative methods like de–noising.

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.

Aiming at modeling the system’s geometry correctly accounting for the major effects influencing image quality within an iterative reconstruction framework we want to achieve this within reasonable processing times. This principle objective led us to using blobs for imagerepresentation and a dedicated GPU hardware implementation. Making extensive use of the texture interpolation capabilities of CUDA and implementing an asymmetric projector/backprojector pair we achieve reasonable processing times and good system modeling at the same time.We conclude from the above results that using GPUs and adequate implementations of the projectors, iterative reconstruction using blobsfor image representation becomes feasible. This, along with avoiding re-sampling, will allow us to apply detailed system modeling for enhanced resolution/noise tradeoff.

Purpose: The purpose of this work is to combine two areas of active research in tomographic x-ray imaging. The first one is the use of iterative reconstruction techniques. The second one is differential phase contrast imaging (DPCI). Method: We derive an SPS type maximum likelihood (ML) reconstruction algorithm with regularization for DPCI. Forward and back-projection are implemented using spherically symmetric basis functions (blobs) and differential footprints, thus completely avoiding the need for numerical differentiation throughout the reconstruction process. The method is applied to the problem of reconstruction of an object from sparsely sampled projection. Results: The results show that the proposed method can handle the sparely sampled data efficiently. In particular no streak artifacts are visible which are present images obtained by filtered back-projection (FBP). Conclusion: Iterative reconstruction algorithms have a wide spectrum of proven advantages in the area of conventional computed tomography. The present work describes for the first time, how a matched forward and back-projection can be implemented for DPCI, which is furthermore free of any heuristics. The newly developed ML reconstruction algorithm for DPCI shows that for the case of sparsely sampled projection data, an improvement in image quality is obtained that is qualitatively comparable to a corresponding situation in conventional x-ray imaging. Based on the proposed operators for forward and back-projection, a large variety of iterative reconstruction algorithms is thus made available for DPCI.

Today’s state-of the art clinical computed tomography (CT) scannersexclusively use energy-integrating, scintillation detector technology, despite the fact that a part of the information carried by the transmitted x-ray photons is lost during the detection process. Roomtemperature semiconductors, like CdTe or CZT, operated in energysensitive photon-counting mode provide information about the energy of every single x-ray detection event. This capability allows novel, promising approaches to selectively image abnormal tissue types like cancerous tissue or atherosclerotic plaque with the CT modality. In thisarticle we report on recent dual K-edge imaging results obtained inthe domain of pre-clinical, energy-sensitive photon counting CT. Inthis approach, the tuning of threshold levels in the detector electronics to the K-edge energy in the attenuation of contrast agents (CA) offers highly specific, quantitative imaging of the distributionof the CA on top of the conventional, morphological image information. The combination of the high specificity of the K-edge imaging technique together with the powerful tool of targeting specific diseases in the human body by dedicated contrast materials might enrich theCT modality with capabilities of functional imaging known from thenuclear medicine imaging modalities, e.g., positron-emission-tomography but with the additional advantage of high spatial and temporal resolution. We also discuss briefly the technological difficulties tobe overcome when translating the technique to human CT imaging andpresent the results of simulations indicating the feasibility of theKedge imaging of vulnerable plaque using targeted gold nano-particles as contrast materials. Our experiments in the pre-clinical domainshow that dual-K edge imaging of iodine and gold based CAs is feasible while our simulations for the imaging of gold CAs in the clinical case support the future possibility of translating the technique to human imaging.