Evidence map›Paper›PMID 41873423›Full record

ArticlePeerJ2026

Enhanced muscle MRI using deep learning: shorter acquisition time with improved image quality.

Peilin Fan, Zaizhu Zhang, Bo Hou, Dong Liu, Marcel Dominik Nickel, Jinxia Zhu, Yi Dai, Qian Wang, Fengdan Wang, Wei Yu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2026. 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

11 authors.

Peilin Fan *Radiology, Peking Union Medical College Hospital, Beijing, China.
Zaizhu Zhang *Radiology, Peking Union Medical College Hospital, Beijing, China.
Bo HouRadiology, Peking Union Medical College Hospital, Beijing, China.
Dong LiuRadiology, Peking Union Medical College Hospital, Beijing, China.
Marcel Dominik NickelMR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany.
Jinxia ZhuMR Research Collaboration, Siemens Healthineers Ltd, Beijing, China.
Yi DaiNeurology, Peking Union Medical College Hospital, Beijing, China.
Qian WangRheumatology and Immunology, Peking Union Medical College Hospital, Beijing, China.
Fengdan WangRadiology, Peking Union Medical College Hospital, Beijing, China.
Wei YuRadiology, Peking Union Medical College Hospital, Beijing, China.
Feng FengRadiology, Peking Union Medical College Hospital, Beijing, China.ORCID 0000-0001-8515-5615

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to evaluate the effectiveness of a k-space learning type deep learning (DL) reconstruction combined with fat-suppressed turbo spin-echo (TSE) T2-weighted imaging (T2WI) in improving image quality and reducing acquisition time for muscle magnetic resonance imaging (MRI). Methods: In this prospective study, 98 controls (mean age 56.3 years) and 33 patients (mean age 45.2 years) underwent both DL reconstructed TSE (TSE Results: The acquisition times were 2 min 11s and 1 min 33s for standard TSE and TSE Conclusion: DL during image acquisition reconstruction, combined with fat-suppressed TSE T2WI, significantly enhances image quality and reduces acquisition time in muscle MRI, suggesting its potential for routine clinical use in musculoskeletal imaging.

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingMuscle, SkeletalAdultAgedFemaleHumansMaleMiddle AgedProspective StudiesSignal-To-Noise RatioThighArtificial intelligenceDeep learningDiagnostic imagingMagnetic resonance imagingThigh

Identifiers

PMID41873423
PMCPMC13006005

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.