ArticleIEEE transactions on medical imaging2017
Tensor-Based Dictionary Learning for Spectral CT Reconstruction.
Article in IEEE transactions on medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.Quantitative imaging in medicine and surgery · 2026Article
- 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
- A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.Journal of imaging informatics in medicine · 2025Article
- Optimizing energy threshold selection for low-concentration contrast agent quantification in small animal photon-counting CT.Physics in medicine and biology · 2025Article
- Enhancing photon-counting computed tomography reconstruction via subspace dictionary learning and spatial sparsity regularization.Quantitative imaging in medicine and surgery · 2025Article
- Semi-stationary Multi-source AI-powered Real-time Tomography.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.IEEE transactions on medical imaging · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis.IEEE access : practical innovations, open solutions · 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
- ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising.Biomedical optics express · 2022Article
- [Low-dose helical CT projection data restoration using noise estimation].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising.Precision clinical medicine · 2022Article
- Ultra-Low-Dose Spectral CT Based on a Multi-level Wavelet Convolutional Neural Network.Journal of digital imaging · 2021Article
- On the Optimization of Regression-Based Spectral Reconstruction.Sensors (Basel, Switzerland) · 2021Article
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods.
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