Evidence map›Paper›PMID 41831007›Full record

ArticleNeuroradiology2026

Super-resolution deep learning reconstruction enhances visualization of cerebral aneurysms on magnetic resonance angiography.

Jun Kanzawa, Koichiro Yasaka, Masayoshi Kato, Noriko Kanemaru, Yusuke Watanabe, Yusuke Asari, Yuki Sonoda, Shigeru Kiryu, Osamu Abe

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In one paragraph

Article in Neuroradiology, 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
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1 · What the graph read from it

What it found

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jun KanzawaThe University of Tokyo, Tokyo, Japan.
Koichiro YasakaThe University of Tokyo, Tokyo, Japan. koyasaka@gmail.com.
Masayoshi KatoThe University of Tokyo, Tokyo, Japan.
Noriko KanemaruThe University of Tokyo, Tokyo, Japan.
Yusuke WatanabeThe University of Tokyo, Tokyo, Japan.
Yusuke AsariThe University of Tokyo, Tokyo, Japan.
Yuki SonodaThe University of Tokyo, Tokyo, Japan.
Shigeru KiryuInternational University of Health and Welfare, Ōtawara, Japan.
Osamu AbeThe University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the depiction of cerebral aneurysms on magnetic resonance angiography (MRA) reconstructed using super-resolution deep learning reconstruction (SR-DLR).

methodsWe retrospectively reviewed the MRA images of 79 patients (49 with cerebral aneurysms and 30 without). The MRA images were subjected to SR-DLR and compared with the original images using both quantitative (signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for the basilar artery (BA) and aneurysms; full width at half maximum (FWHM), edge rise distance (ERD), and edge rise slope (ERS) for the BA) and qualitative metrics (depiction of aneurysms and BA, image sharpness, noise, artifacts, and overall image quality performed by three radiologists). Statistical comparisons were performed using a paired t-test for quantitative metrics and the Wilcoxon signed-rank test for qualitative metrics.

resultsSR-DLR significantly improved SNR, CNR, and ERD compared with the original images. ERS values showed a trend toward improvement with SR-DLR, whereas FWHM showed no significant difference. Depiction of the vasculature, image sharpness, noise, and overall image quality were rated as significantly better for SR-DLR by all readers. Most readers rated that the depiction of aneurysms was improved with SR-DLR, particularly for aneurysms < 5 mm. The assessment of artifacts varied among readers.

conclusionSR-DLR significantly improves the image quality of MRA and enhances the visualization of cerebral aneurysms, with greater improvements in the depiction of smaller aneurysms.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedIntracranial AneurysmMagnetic Resonance AngiographyAdultAgedFemaleHumansImage EnhancementMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioCerebral aneurysmsDeep learningMagnetic resonance angiographyMagnetic resonance imagingSuper-resolution deep learning reconstruction

Identifiers

PMID41831007
PMCPMC13139297

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.