Evidence map›Paper›PMID 41457976›Full record

ArticleCirculation. Cardiovascular imaging2026

Synthetic Contrast-Free LGE via Diffusion-Based Framework in Acute MI for Image Quality and Quantitative Scar Analysis.

Jing Qi, Xiuzheng Yue, Miao Hu, Jianing Cui, Yanan Zhao, Jianan Li, Jian Wang, Yinyin Chen, Hang Jin, Chengyan Wang and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Circulation. Cardiovascular imaging, 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

12 authors.

Jing Qi *Medical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.).ORCID 0009-0000-7851-4341
Xiuzheng Yue *Medical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.).ORCID 0000-0002-9090-9263
Miao HuMedical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.).
Jianing CuiDepartment of Radiology (J.C., Y.Z., T.L.), The First Medical Center, Chinese PLA General Hospital, Beijing.
Yanan ZhaoDepartment of Radiology (J.C., Y.Z., T.L.), The First Medical Center, Chinese PLA General Hospital, Beijing.
Jianan LiMedical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.).
Jian WangDepartment of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China (J.W.).
Yinyin ChenDepartment of Radiology, Zhongshan Hospital (Y.C., H.J.), Fudan University, Shanghai, China.ORCID 0000-0002-1192-671X
Hang JinDepartment of Radiology, Zhongshan Hospital (Y.C., H.J.), Fudan University, Shanghai, China.ORCID 0000-0002-6810-2527
Chengyan WangHuman Phenome Institute and Shanghai Pudong Hospital (C.W.), Fudan University, Shanghai, China.ORCID 0000-0002-8890-4973
Tao LiDepartment of Radiology (J.C., Y.Z., T.L.), The First Medical Center, Chinese PLA General Hospital, Beijing.
Kunlun HeMedical Big Data Research Center, Chinese PLA General Hospital, Beijing (J.Q., X.Y., M.H., J.L., K.H.).ORCID 0000-0002-3335-5700

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aims to develop a diffusion model-based framework for generating late gadolinium enhancement (LGE)-like images without contrast. The resulting synthetic images are then comprehensively evaluated for subjective and objective image quality, as well as their clinical utility for quantifying scar in acute myocardial infarction.

methodsIn this retrospective study, we developed a diffusion mode-based framework, multisequence guided diffusion to generate synthetic native enhancement (SNE) images from cine magnetic resonance imaging, and T2 short tau inversion recovery sequences. Data were collected from 331 patients with acute myocardial infarction across 3 centers from January 2014 to July 2024. Subjective and objective image qualities were assessed using Likert scoring and contrast ratio analyses on both internal and external cohorts, comparing SNE with standard LGE to evaluate group differences. Myocardial contours were manually delineated, and scar size and transmurality were quantified using the full-width at half-maximum method to assess the accuracy of myocardial infarction detection.

resultsIn comparisons with general generative models and multimodal fusion-based generative approaches, multisequence guided diffusion demonstrated more favorable visual perceptual quality and the closest data distribution alignment to conventional LGE. SNE demonstrated significantly higher quality than LGE (internal: 4.250 [4.000-4.750] versus 4.000 [3.750-4.500]; external: 4.250 [4.000-4.750] versus 4.000 [3.500-4.250];

conclusionsSNE demonstrated strong agreement with LGE in quantitative assessment of acute myocardial infarction scar, with comparable or improved image quality.

Indexed as

CicatrixContrast MediaMagnetic Resonance Imaging, CineMyocardial InfarctionMyocardiumAgedFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsRetrospective StudiesContrast Mediadeep learninggadoliniummagnetic resonance imagingmyocardial infarction

Identifiers

PMID41457976
PMCPMC12908633

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.