ArticleIEEE transactions on radiation and plasma medical sciences2021
Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.
Article in IEEE transactions on radiation and plasma medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Spectral CT and Photon-counting Detector CT: Principles and Applications in Neuroradiology.Radiology. Imaging cancer · 2026Review
- An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.Quantitative imaging in medicine and surgery · 2026Article
- 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
- Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation.Sensors (Basel, Switzerland) · 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
- Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment.IEEE transactions on radiation and plasma medical sciences · 2021Article
- PWLS-PR: low-dose computed tomography image reconstruction using a patch-based regularization method based on the penalized weighted least squares total variation approach.Quantitative imaging in medicine and surgery · 2021Article
- Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.IEEE transactions on instrumentation and measurement · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.
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