Evidence map›Paper›PMID 40781512›Full record

ArticleEuropean radiology2026

Deep learning-based image enhancement for improved black blood imaging in brain metastasis.

Gaeun Oh, Seungyoon Paik, Sang Won Jo, Hye Jeong Choi, Roh-Eul Yoo, Seung Hong Choi

Erratum issuedAbstract read
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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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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

The trial behind it

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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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Gaeun Oh *Seoul National University College of Medicine, Seoul, Republic of Korea.
Seungyoon Paik *Seoul National University College of Medicine, Seoul, Republic of Korea.
Sang Won JoDepartment of Radiology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea.
Hye Jeong ChoiDepartment of Radiology, CHA University Bundang Medical Center, Seongnam-si, Republic of Korea.
Roh-Eul YooSeoul National University College of Medicine, Seoul, Republic of Korea. kong05@snu.ac.kr.ORCID http://orcid.org/0000-0002-5625-5921
Seung Hong ChoiSeoul National University College of Medicine, Seoul, Republic of Korea.

Funding

AIRS Medical Inc. 06- 2021-2210Ministry of Science and ICT, South Korea NRF-2023R1A2C3003250Seoul National University Hospital 0320230270Seoul National University Hospital 1820230040
6 · The paper itself

Abstract

objectivesTo evaluate the utility of a deep learning (DL)-based image enhancement for improving the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted black blood (BB) MR imaging for brain metastases. MATERIALS AND

methodsThis retrospective study included 126 patients with and 121 patients without brain metastasis who underwent 3-T MRI examinations. Commercially available DL-based MR image enhancement software was utilized for image post-processing. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of enhancing lesions were measured. For qualitative assessment and diagnostic performance evaluation, two radiologists graded the overall image quality, noise, and artifacts of each image and the conspicuity of visible lesions. The Wilcoxon signed-rank test and regression analyses with generalized estimating equations (GEEs) were used for statistical analysis.

resultsFor MR images that were not previously processed using other DL-based methods, SNR and CNR were higher in the DL-enhanced images than in the standard images (438.3 vs. 661.1, p < 0.01; 173.9 vs. 223.5, p < 0.01). Overall image quality and noise were improved in the DL images (p < 0.01, average score-5 proportion 38% vs. 65%; p < 0.01, 43% vs. 74%), whereas artifacts did not significantly differ (p ≥ 0.07). Sensitivity was increased after post-processing from 79 to 86% (p = 0.02), especially for lesions smaller than 5 mm (69 to 78%, p = 0.03), and changes in specificity (p = 0.24) and average false-positive (FP) count (p = 0.18) were not significant.

conclusionDL image enhancement improves the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted BB MR imaging for the detection of small brain metastases. KEY POINTS: Question Can deep learning (DL)-based image enhancement improve the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted black blood (BB) MR imaging for brain metastases? Findings DL-based image enhancement improved image quality of thin slice BB MR images and sensitivity for brain metastasis, particularly for lesions smaller than 5 mm. Clinical relevance DL-based image enhancement on BB images may assist in the accurate diagnosis of brain metastasis by achieving better sensitivity while maintaining comparable specificity.

Indexed as

Brain NeoplasmsDeep LearningImage EnhancementMagnetic Resonance ImagingAdultAgedAged, 80 and overContrast MediaFemaleHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioContrast MediaBrain neoplasmDeep learningImage enhancementMagnetic resonance imaging

Identifiers

PMID40781512
PMCPMC12953279

What Socratic holds

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LicenceCC BY
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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.