Evidence map›Paper›PMID 36692597›Full record

ArticleEuropean radiology2023

Free-breathing and instantaneous abdominal T

Xi Lin, Lixing Dai, Qinqin Yang, Qizhi Yang, Hongjian He, Lingceng Ma, Jingjing Liu, Jingliang Cheng, Congbo Cai, Jianfeng Bao and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomography
    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

13 authors at 4 institutions in 2 countries.

Xi Lin *Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Lixing Dai *Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Qinqin YangDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Qizhi YangDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Hongjian HeCenter for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Lingceng MaDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Jingjing LiuDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Jingliang ChengDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Congbo CaiDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China. cbcai@xmu.edu.cn.ORCID http://orcid.org/0000-0002-0600-8594
Jianfeng BaoDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China. baoguojianfeng@gmail.com.
Zhong ChenDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Shuhui CaiDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
Jianhui ZhongCenter for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Xiamen University · CNFirst Affiliated Hospital of Zhengzhou University · CNUniversity of Rochester · USZhejiang University · CN

Funding

National Natural Science Foundation of China 11775184National Natural Science Foundation of China 22161142024National Natural Science Foundation of China 82071913National Natural Science Foundation of China U1805261
6 · The paper itself

Abstract

objectivesTo develop a real-time abdominal T

methodsThe single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T

resultsThe results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T

conclusionsFree-breathing abdominal T KEY POINTS: • MOLED achieves free-breathing abdominal T

Indexed as

Deep LearningAbdomenHumansLiverMagnetic Resonance ImagingPhantoms, ImagingRespirationAbdomenDeep learningT2 mapping

Identifiers

PMID36692597
OpenAlexW4317831216

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.