Evidence map›Paper›PMID 36199853›Full record

ArticleApplied intelligence (Dordrecht, Netherlands)2022

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information.

Jiahao Huang, Weiping Ding, Jun Lv, Jingwen Yang, Hao Dong, Javier Del Ser, Jun Xia, Tiaojuan Ren, Stephen T Wong, Guang Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. [Application of generative adversarial network in magnetic resonance image reconstruction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023
    Review
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

10 authors.

Jiahao HuangCollege of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China.
Weiping DingSchool of Information Science and Technology, Nantong University, 226019 Nantong, China.
Jun LvSchool of Computer and Control Engineering, Yantai University, 264005 Yantai, China.
Jingwen YangDepartment of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing, China.
Hao DongCenter on Frontiers of Computing Studies, Peking University, Beijing, China.
Javier Del SerTECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain.
Jun XiaDepartment of Radiology, Shenzhen Second People's Hospital, The First Afliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Tiaojuan RenCollege of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China.
Stephen T WongSystems Medicine and Bioengineering Department, Departments of Radiology and Pathology, Houston Methodist Cancer Center, Houston Methodist Hospital, Weill Cornell Medicine, 77030 Houston, TX USA.
Guang YangNational Heart and Lung Institute, Imperial College London, London, UK.ORCID 0000-0001-7344-7733

Funding

Medical Research Council MC_PC_21013Medical Research Council MR/V023799/1
6 · The paper itself

Abstract

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in

Indexed as

Edge enhancementFast MRIGenerative adversarial networksMulti-view learningParallel imaging

Identifiers

PMID36199853
PMCPMC9526695

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.