ArticleInverse problems2018
Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction.
Article in Inverse problems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 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
- 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-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 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
- Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT Image Reconstruction.Sensors (Basel, Switzerland) · 2020Article
- DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering.IEEE transactions on medical imaging · 2020Article
- Block matching frame based material reconstruction for spectral CT.Physics in medicine and biology · 2019Article
- Energy enhanced tissue texture in spectral computed tomography for lesion classification.Visual computing for industry, biomedicine, and art · 2019Article
- Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction.IEEE transactions on medical imaging · 2019Article
- Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation.Computers in biology and medicine · 2018Article
- Spatial-Spectral Cube Matching Frame for Spectral CT Reconstruction.Inverse problems · 2018Article
- Statistical Iterative CBCT Reconstruction Based on Neural Network.IEEE transactions on medical imaging · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
Spectral computed tomography (CT) has been a promising technique in research and clinic because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifacts suppression and resolution preservation.
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