Background: Multi-energy computed tomography (MECT) based on a photon-counting detector is an emerging imaging modality that collects projections at several energy bins with a single scan. However, the limited number of photons collected into the divided, narrow energy bins results in high quantum noise levels in reconstructed images. This study aims to improve MECT image quality by minimizing noise levels while retaining image details.Methods: A novel MECT reconstruction method was proposed by exploiting the nonlocal tensor similarity among interchannel images and spatial sparsity in single-channel images. Similar patches were initially extracted from the interchannel images in spectral and spatial domains, then stacked into a new three-order tensor. Intrinsic tensor sparsity regularization that combined the Tuker and canonical polyadic (CP) lowrank decomposition techniques were applied to exploit the nonlocal similarity of the formulated tensor.Spatial sparsity in single-channel images was modeled by total variation (TV) regularization that utilizes the compressibility of gradient image. A new MECT reconstruction model was established by simultaneously incorporating the intrinsic tensor sparsity and TV regularizations. The iterative alternating minimization method was utilized to solve the reconstruction model based on a flexible framework. Results:The proposed method was applied to the digital phantom and real mouse data to assess its feasibility and reliability. The reconstruction and decomposition results in the mouse data were encouraging and demonstrated the ability of the proposed method in noise suppression while preserving image details, not observed with other methods. Imaging data from the digital phantom illustrated this method as achieving the best intuitive reconstruction and decomposition results among all compared methods. They reduced the root mean square error (RMSE) by 89.75%, 50.75%, and 36.54% on the reconstructed images compared with analytic, TV-based, and tensor-based methods, respectively. This phenomenon was also observed with decomposition results, where the RMSE was also reduced by 97.96%, 67.74%, 72.05%, respectively.Conclusions: In this study, we proposed a reconstruction method for photon counting detector-based MECT, using the intrinsic tensor sparsity and TV regularizations. Improvements in noise suppression and detail preservation in the digital phantom and real mouse data were validated by the qualitative and quantitative evaluations on the reconstruction and decomposition results, verifying the potential of the proposed method in MECT reconstruction.

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A block-matching-based reconstruction method for low-dose CT is proposed. Improvements in image quality are verified by quantitative metrics and visual comparisons, thereby indicating the potential of the proposed method for real-life applications.

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The set pair analysis is a system analysis method to deal with uncertain problem. The concepts of the main same, supper same, assimilation degree and the new determining levels law are stipulated to make up for the defects of routine set pair analysis about the weights and determining levels law based on the theories of improved entropy weight and set pair analysis malleability. The method to determine system level and rank order projects by the main same and assimilation degree was put forward, and then, the multi-factors degree set pair analysis fuzzy evaluation model (EWMFSPAFM) has been suggested. A study under taken groundwater environment evaluation as demonstration, the water resources are evaluated by the EWMFSPAFM. And the results are compared with the results of the routine set pair analysis evaluation model and improved set pair analysis evaluation model. It is showed that the EWMFSPAFM is structure simple, calculation easy, result practical and reasonable, the method feasible and reliable.