Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction

Wang, Qian; Salehjahromi, Morteza; Yu, Hengyong

doi:10.1109/access.2021.3071492

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…In 2016, Zeng et al proposed a novel algorithm that combines penalized weighted least squares (PWLS) with structural tensor total variation (STV) regularization and employed an alternating optimization algorithm to solve the objective function, resulting in higher-quality spectral CT images [11]. Subsequently, more reconstruction algorithms based on single-energy channel regularization constraints have been proposed, all of which have achieved satisfactory reconstruction results [12][13][14][15][16]. However, these algorithms only process CT images at each energy channel separately during the image reconstruction stage, focusing solely on the correlation between singlechannel images.…”

Section: Introductionmentioning

confidence: 99%

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Li,

Wang,

Xue

et al. 2024

Electronics

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Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms.

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Section: Introductionmentioning

confidence: 99%

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Li,

Wang,

Xue

et al. 2024

Electronics

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An Iterative Threshold Algorithm of Log-Sum Regularization for Sparse Problem

Zhou

Liu

Zhang

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

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The log-sum regularization has been always drawing widespread attention in the field of sparse problem. However, it brings about a non-convex, non-smooth, and non-Lipschitz optimization problem that is difficult to tackle. To overcome the problem, an iterative threshold algorithm of log-sum regularization is proposed in this paper. Firstly, by deducing the derivative mathematical expression of log-sum function, a property theorem about solution for log-sum regularization is established. Secondly, based on the above theorem, the optimal setting rules of the compromising parameters are elaborated, and the iterative logsum threshold algorithm is proposed. Thirdly, under the situation that the compromising parameters of log-sum regularization are relatively small, it can be proven that the proposed algorithm converges to a local minimizer of log-sum regularization. Finally, a series of simulations are implemented to examine performance of the algorithm, and the results exhibit that the proposed algorithm outperforms the state-of-the-art algorithms in terms of iterations and precision.

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Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction

Cited by 2 publications

References 29 publications

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

An Iterative Threshold Algorithm of Log-Sum Regularization for Sparse Problem

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