2020
DOI: 10.1109/tmi.2020.2976692
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Full-Spectrum-Knowledge-Aware Tensor Model for Energy-Resolved CT Iterative Reconstruction

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Cited by 17 publications
(8 citation statements)
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“…The observation that most of the image pixels tend to be spanned by a small number of basis materials, whereas k-edge elements are often confined to localized regions, can be exploited for noise reduction as shown by Gao et al (2011). There are also algorithms exploiting the low dimensionality of spectral CT images in combination with SPICCS (Zeng et al 2020) or patch-based denoising (Niu et al 2018a). Finally, a new and promising research direction is the application of convolutional neural networks for bin image denoising (Clark and Badea 2020).…”
Section: Energy Bin Imagesmentioning
confidence: 99%
“…The observation that most of the image pixels tend to be spanned by a small number of basis materials, whereas k-edge elements are often confined to localized regions, can be exploited for noise reduction as shown by Gao et al (2011). There are also algorithms exploiting the low dimensionality of spectral CT images in combination with SPICCS (Zeng et al 2020) or patch-based denoising (Niu et al 2018a). Finally, a new and promising research direction is the application of convolutional neural networks for bin image denoising (Clark and Badea 2020).…”
Section: Energy Bin Imagesmentioning
confidence: 99%
“…This kind of method is difficult to be implemented straightforwardly in this study due to the spatial mismatch between the dual-energy projections. The second one is the image-domain decomposition method [18][19][20] , in which the CT images reconstructed using the filtered back-projection (FBP) or iterative algorithm are decomposed into different bases. Despite that the spatial mismatch of projections can be compensated after reconstruction, however, the image-domain method is not able to generate CT bases with the desired high spatial resolution.…”
Section: B Materials Decomposition Algorithmmentioning
confidence: 99%
“…8 Recently, Zeng et al proposed a full-spectrum-knowledge-aware tensor by imposing the global correlation, piecewise smooth and latent full-spectrum properties of PCCT images. 9 These methods have been shown great potential in preserving image details and suppressing noise. However, there remain some challenges in practice.…”
Section: Introductionmentioning
confidence: 99%