Evidence map›Paper›PMID 36443529›Full record

ArticleJournal of digital imaging2023

Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction.

Yuanwei He, Li Zeng, Wei Chen, Changcheng Gong, Zhaoqiang Shen

Abstract read
In one paragraph

Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuanwei HeCollege of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China.
Li ZengCollege of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China. drlizeng@cqu.edu.cn.ORCID 0000-0001-6109-9583
Wei ChenDepartment of Radiology, Southwest Hospital of AMU, Chongqing, Chongqing, 400038, China.
Changcheng GongCollege of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, 400067, China.
Zhaoqiang ShenCollege of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low-dose computed tomography (LDCT) has been widely used for various clinic applications to reduce the X-ray dose absorbed by patients. However, LDCT is usually degraded by severe noise over the image space. The image quality of LDCT has attracted aroused attentions of scholars. In this study, we propose the bilateral weighted relative total variation (BRTV) used for image restoration to simultaneously maintain edges and further reduce noise, then propose the BRTV-regularized projections onto convex sets (POCS-BRTV) model for LDCT reconstruction. Referring to the spacial closeness and the similarity of gray value between two pixels in a local rectangle, POCS-BRTV can adaptively extract sharp edges and minor details during the iterative reconstruction process. Evaluation indexes and visual effects are used to measure the performances among different algorithms. Experimental results indicate that the proposed POCS-BRTV model can achieve superior image quality than the compared algorithms in terms of the structure and texture preservation.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray ComputedAlgorithmsHumansRadiation DosageImage reconstructionLow-dose computed tomography (LDCT)Relative total variationStructure preservation

Identifiers

PMID36443529
PMCPMC9707190

What OpenQuestion holds

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Registered trials

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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.