ArticleIEEE transactions on medical imaging2019
Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- ZS4D: Zero-Shot Self-Similarity-Steered Denoiser for Volumetric Photon-Counting CT.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Sparse-view spectral CT reconstruction via a coupled subspace representation and score-based generative model.Quantitative imaging in medicine and surgery · 2025Article
- Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.Scientific reports · 2025Article
- Enhancing photon-counting computed tomography reconstruction via subspace dictionary learning and spatial sparsity regularization.Quantitative imaging in medicine and surgery · 2025Article
- Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.IEEE transactions on medical imaging · 2024Article
- Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.Quantitative imaging in medicine and surgery · 2023Article
- Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction.IEEE transactions on medical imaging · 2021Article
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.IEEE transactions on instrumentation and measurement · 2021Article
- Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction.IEEE access : practical innovations, open solutions · 2021Article
- Multi-energy CT reconstruction using tensor nonlocal similarity and spatial sparsity regularization.Quantitative imaging in medicine and surgery · 2020Article
- Spectral CT Reconstruction via Low-Rank Representation and Region-Specific Texture Preserving Markov Random Field Regularization.IEEE transactions on medical imaging · 2020Article
- A Robust Regularizer for Multiphase CT.IEEE transactions on medical imaging · 2020Article
- Block matching frame based material reconstruction for spectral CT.Physics in medicine and biology · 2019Article
- Image gradient LOptics express · 2019Article
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5 authors.
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Abstract
Spectral computed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows, which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a series of non-local spatial-spectral cubes. The small size of spatial patches for such a group makes the SSCMF fail to fully encode the sparsity and low-rank properties. The hard-thresholding and collaboration filtering in the SSCMF also cause difficulty in recovering the image features and spatial edges. While all the steps are operated on 4-D group, the huge computational cost and memory load might not be affordable in practice. To avoid the above limitations and further improve the image quality, we first formulate a non-local cube-based tensor instead of group to encode the sparsity and low-rank properties. Then, as a new regularizer, the Kronecker-basis-representation tensor factorization is employed into a basic spectral CT reconstruction model to enhance the capability of image feature extraction and spatial edge preservation, generating a non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman method is adopted to solve the NLCTF model. Both numerical simulations and preclinical mouse studies are performed to validate and evaluate the NLCTF algorithm. The results show that the NLCTF method outperforms the other state-of-the-art competing algorithms.
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