ArticleSL clinical medicine : research2022
Directly Filtering the Sparse-View CT Images by BM3D.
Article in SL clinical medicine : research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Image Reconstruction with Maclaurin Series Expansion.International journal of biomedical research & practice · 2025Article
- Extreme Few-View Tomography without Training Data.Biomedical journal of scientific & technical research · 2024Article
- Sinogram Interpolation Inspired by Single-Image Super Resolution.Journal of biotechnology and its applications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
The x-ray Computed Tomography (CT) images with sparse-view data acquisition contain severe angular aliasing artifacts. The common denoising filters do not work well if they are used to reduce the artifacts. The state-of-the-art methods to process the sparse-view CT images are deep-learning based; they require a large amount of training data pairs. This paper considers a situation where no clinical training data sets are available. All we have is one sparse scan of a patient. This paper attempts to use a BM3D filter to reduce the artifacts by using an artifact power spectral density function, which is calculated with computer simulations. The results in this paper show that the proposed method is promising in computer simulations. The proposed method has been applied to patient data, and we observe that the sparse-view artifacts are reduced, especially in the central region of the image, but the artifact reduction is not as effective at the peripheral if the control parameter in the BM3D filter is not properly chosen.
Indexed as
Identifiers
37126466PMC10138108What OpenQuestion holds
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.