Evidence map›Paper›PMID 39152167›Full record

ArticleScientific reports2024

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Minkook Seo, Woojin Jung, Geunu Jeong, Seungwook Yang, Ilah Shin, Ji Young Lee, Kook-Jin Ahn, Bum-Soo Kim, Jinhee Jang

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

9 authors.

Minkook SeoDepartment of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
Woojin JungAIRS Medical, Seoul, Republic of Korea.
Geunu JeongAIRS Medical, Seoul, Republic of Korea.
Seungwook YangAIRS Medical, Seoul, Republic of Korea.
Ilah ShinDepartment of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
Ji Young LeeDepartment of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
Kook-Jin AhnDepartment of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
Bum-Soo KimDepartment of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
Jinhee JangDepartment of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea. znee@catholic.ac.kr.

Funding

National Research Foundation of Korea RS-2023-00208409
6 · The paper itself

Abstract

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm

Indexed as

Deep LearningMagnetic Resonance ImagingPlaque, AtheroscleroticAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioDeep learningImage qualityIntracranial atherosclerosisIntracranial vessel wall imagingSuper-resolution

Identifiers

PMID39152167
PMCPMC11329665

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.