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Spectral photon-counting X-ray computed tomography (sCT) opens up new possibilities for the quantitative measurement of materials in an object compared with conventional energy-integrating CT or dual-energy CT. However, achieving reliable and accurate material decomposition in sCT is extremely challenging, due to the similarity between different basis materials, strong quantum noise, and photon-counting detector limitations. We propose a novel material decomposition method that works in a region-wise manner. The method consists in optimizing basis materials based on spatioenergy segmentation of regions of interest (ROIs) in sCT images and performing a fine material decomposition involving optimized decomposition matrix and sparsity regularization. The effectiveness of the proposed method was validated on both digital and physical data. The results showed that the proposed ROI-wise material decomposition method presents clearly higher reliability and accuracy compared with common decomposition methods based on total variation (TV) or L1-norm (lasso) regularization. [ABSTRACT FROM AUTHOR]

We propose a multiresolution X-ray imaging method designed for non-destructive testing/evaluation (NDT/NDE) applications which can also be used for small animal imaging studies. Two sets of projections taken at different magnifications are combined and a multiresolution image is reconstructed. A geometrical relation is introduced in order to combine properly the two sets of data and the processing using wavelet transforms is described. The accuracy of the reconstruction procedure is verified through a comparison to the standard filtered backprojection (FBP) algorithm on simulated data. [ABSTRACT FROM AUTHOR]