Evidence map›Paper›PMID 29292249›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2017

[Total generalized variation minimization based on projection data for low?dose CT reconstruction].

Shan-Zhou Niu, Heng Wu, Ze-Feng Yu, Zi-Jun Zheng, Gao-Hang Yu

Abstract read
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. [Low-dose CT reconstruction based on high-dimensional partial differential equation projection recovery].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024
    Article
  3. [Nonlocal low-rank and sparse matrix decomposition for low-dose cerebral perfusion CT image restoration].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022
    Article
  4. [Low-dose cerebral perfusion CT image restoration using prior image constrained diffusion tensor].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021
    Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shan-Zhou NiuSchool of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China. E-mail: maszniu@163.com.
Heng Wu
Ze-Feng Yu
Zi-Jun Zheng
Gao-Hang Yu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo obtain high?quality low?dose CT images using total generalized variation regularization based on the projection data for low?dose CT reconstruction.

methodsThe projection data of the CT images were transformed from Poisson distribution to Gaussian distribution using the linear Anscombe transform. The transformed data were then restored by an efficient total generalized variation minimization algorithm. Reconstruction was finally achieved by inverse Anscombe transform and filtered back projection (FBP) method.

resultsThe image quality of low?dose CT was greatly improved by the proposed algorithm in both Clock and Shepp?Logan phantoms. The signal?to?noise ratios (SNRs) of the Clock and Shepp-Logan images reconstructed by FBP algorithm were 17.752 dB and 19.379 dB, which were increased by the proposed algorithm to 24.0352 and 23.4181 dB, respectively. The NMSE of the Clock and Shepp?Logan images reconstructed by FBP algorithm was 0.86% and 0.58%, which was reduced by the proposed algorithm to 0.2% and 0.23%, respectively.

conclusionThe proposed method can effectively suppress noise and strip artifacts in low?dose CT images when piecewise constant assumption is not possible.

Indexed as

AlgorithmsImage Processing, Computer-AssistedRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedPhantoms, ImagingRadiation DosageSignal-To-Noise Ratio

Identifiers

PMID29292249
PMCPMC6744009

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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.