Evidence map›Paper›PMID 40167648›Full record

ArticleAbdominal radiology (New York)2025

Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning.

Erjia Guo, Li Chen, Lili Xu, Daming Zhang, Jiahui Zhang, Xiaoxiao Zhang, Xin Bai, Qianyu Peng, Jinxia Zhu, Marcel Dominik Nickel and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2025. 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

13 authors.

Erjia Guo *Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Li Chen *Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Lili XuDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, No. 1, East Banshan Road, Gongshu District, Hangzhou, Zhejiang, 310022, China.
Daming ZhangPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Jiahui ZhangPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Xiaoxiao ZhangPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Xin BaiPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Qianyu PengPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Jinxia ZhuMR Research Collaboration, Siemens Healthineers Ltd., Beijing, China.
Marcel Dominik NickelMR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany.
Zhengyu JinPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Gumuyang ZhangPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. zhang_stacey@163.com.
Hao SunPeking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. sunhao_robert@126.com.

Funding

National High Level Hospital Clinical Research Funding Grant No. 2022-PUMCH-A-033 and 2022-PUMCH-B-069National High-Level Hospital Clinical Research Funding 2022-PUMCH-B-068the Beijing Natural Science Foundation Grant No. L232133the CAMS Innovation Fund for Medical Sciences Grant No. 2022-I2M-C&T-B-019the National High Level Hospital Clinical Research Funding Grant No. 2022-PUMCH-A-035
6 · The paper itself

Abstract

purposeTo investigate the value of deep learning (DL) in T2-weighted imaging (T2

methodsWe prospectively enrolled a total of 28 consecutive patients for the evaluation of bladder cancer. T2

resultsCompared to T2

conclusionsOur study preliminarily demonstrated the feasibility of T2-weighted imaging with DL reconstruction of bladder MR in clinical practice. T2

Indexed as

Deep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingUrinary Bladder NeoplasmsAdultAgedAged, 80 and overArtifactsFemaleHumansMaleMiddle AgedProspective StudiesTime FactorsUrinary BladderBladder cancerDeep learningMagnetic resonance imaging

Identifiers

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.