Evidence map›Paper›PMID 39704803›Full record

ArticleEuropean radiology2025

Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.

Yarong Yu, Dijia Wu, Ziting Lan, Xiaoting Dai, Wenli Yang, Jiajun Yuan, Zhihan Xu, Jiayu Wang, Ze Tao, Runjianya Ling and 2 more

Abstract read
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In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

12 authors.

Yarong Yu *Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Dijia Wu *Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Ziting LanDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoting DaiDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenli YangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiajun YuanDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhihan XuSiemens Healthineers, CT collaboration, Shanghai, China.
Jiayu WangShanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Ze TaoShanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Runjianya LingInstitute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Su ZhangBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Jiayin ZhangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. andrewssmu@msn.com.ORCID http://orcid.org/0000-0001-7383-7571

Funding

National Key Research and Development Program of China 2021YFF0501402Shanghai Health Commission Discipline Leader Project 2022XD031Shanghai Jiao Tong University "Star Project" of Biomedical Multi-discipline Research Program YG2022ZD015Shenkang 3-year project of clinical innovation SHDC2022CRD016
6 · The paper itself

Abstract

objectivesTo develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.

methodsThis study retrospectively included patients with chest discomfort who underwent CT myocardial perfusion + CT angiography + LIE from two hospitals. Two DL models, residual dense network (RDN) and conditional generative adversarial network (cGAN), were developed and validated. 423 patients were randomly divided into training (182 patients), tuning (48 patients), internal validation (92 patients) and external validation group (101 patients). LIE

resultsThe image quality of LIE

conclusionsRDN model generated denoised LIE images with markedly higher SNR and CNR than the cGAN-model and original images, which significantly improved the identifiability of visual analysis. Moreover, using denoised single-stack images led to accurate CT-ECV quantification. KEY POINTS: Question Can the developed models denoise CT-derived late iodine enhancement high images and improve signal-to-noise ratio? Findings The residual dense network model significantly improved the image quality for late iodine enhancement and enabled accurate CT- extracellular volume quantification. Clinical relevance The residual dense network model generates denoised late iodine enhancement images with the highest signal-to-noise ratio and enables accurate quantification of extracellular volume.

Indexed as

Computed Tomography AngiographyContrast MediaDeep LearningMyocardial Perfusion ImagingRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedFemaleHumansIodineMaleMiddle AgedRadiation DosageRetrospective StudiesSignal-To-Noise RatioContrast MediaIodineComputed tomographyDeep learningExtracellular volumeLate iodine enhancementMyocardial interstitial fibrosis

Identifiers

PMID39704803

What Socratic 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 Socratic graph.