Evidence map›Paper›PMID 42236638›Full record

ArticleEmergency radiology2026

Shortening MRI scanning time for acute ischemic stroke: analysis of the effect of 3.0T MRI compressed sensing deep learning reconstruction.

Xinxin Jiang, Feixiang Shang, Junqi Liu, Qiuzi Qin, Pei Li, Zhentao Chen, Li Li, Weijun Qian

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Emergency radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Xinxin JiangDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China.
Feixiang ShangDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China.
Junqi LiuDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China.
Qiuzi QinDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China.
Pei LiDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China.
Zhentao ChenImage Department, Canon Medical Systems (China), Beijing, 100027, China.
Li LiDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China. boylili2000@163.com.
Weijun QianDepartment of Imaging, Kaifeng Central Hospital, NO.85, Riverway Street, Shunhe Hui District, Kaifeng City, Henan Province, 475000, China. q196911@126.com.ORCID http://orcid.org/0009-0008-2669-9282

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute ischemic stroke requires rapid and accurate MRI diagnosis. This study aimed to evaluate whether 3.0T brain MRI with compressed sensing deep learning reconstruction (CS‑DLR) can reduce scanning time while maintaining diagnostic image quality.

methodsWe retrospectively enrolled 69 patients with acute ischemic stroke who underwent 3.0T MRI. Conventional images and CS‑DLR reconstructed images at three acceleration rates (R3, R4, R5) were compared for AX T2WI, AX T1WI, and AX T2W FLAIR sequences. Two radiologists performed qualitative evaluation (5‑point scale: overall quality, noise, clarity). Quantitative assessment included signal‑to‑noise ratio (SNR) and contrast‑to‑noise ratio (CNR). Statistical analysis was performed using generalized estimating equation (GEE).

resultsCS‑DLR significantly reduced scanning time. Quantitative analysis showed that SNR and CNR were significantly higher in the CS‑DLR groups than in the conventional group (P < 0.05). Qualitative scores for overall quality, noise, and clarity were significantly better in CS‑DLR R3 and R4 than in the conventional group (P < 0.05). CS‑DLR R5 showed similar overall image quality (P > 0.05) but significantly lower clarity (P < 0.05).

conclusionCS‑DLR markedly shortens 3.0T MRI scanning time for acute ischemic stroke. CS‑DLR R3 and R4 provide superior image quality compared with conventional MRI, whereas R5 achieves comparable overall quality. CS‑DLR is clinically feasible and valuable for rapid emergency neuroimaging.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedIschemic StrokeMagnetic Resonance ImagingAgedAged, 80 and overContrast MediaFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioTime FactorsContrast Media3.0T MRIAcute ischemic strokeCS-DLRImage qualityScanning time shortening

Identifiers

PMID42236638
PMCPMC13633324

What OpenQuestion holds

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Registered trials

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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.