ArticleInverse problems2018
Spatial-Spectral Cube Matching Frame 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 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 34 citations in OpenAlex.
- Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.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
- Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.IEEE transactions on instrumentation and measurement · 2021Article
- Multi-energy CT reconstruction using tensor nonlocal similarity and spatial sparsity regularization.Quantitative imaging in medicine and surgery · 2020Article
- Multi-energy CT imaging for large patients using dual-source photon-counting detector CT.Physics in medicine and biology · 2020Article
- Block matching frame based material reconstruction for spectral CT.Physics in medicine and biology · 2019Article
- Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction.IEEE transactions on medical imaging · 2019Article
- Image gradient LOptics express · 2019Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Spectral computed tomography (CT) reconstructs the same scanned object from projections of multiple narrow energy windows, and it can be used for material identification and decomposition. However, the multi-energy projection dataset has a lower signal-noise-ratio (SNR), resulting in poor reconstructed image quality. To address this thorny problem, we develop a spectral CT reconstruction method, namely spatial-spectral cube matching frame (SSCMF). This method is inspired by the following three facts:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.