ArticleCirculation2022
Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.
Article in Circulation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Machine learning-based myocardial infarction bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Guideline
- Reply to the Letter to the Editor: Late gadolinium enhancement synthesis from cardiac cine MRI: physical, methodological, and editorial considerations.European radiology · 2026Article
- Generation of contrast-enhanced cardiac MRI from contrast-free scans: a multi-center, multi-manufacturer study.European radiology · 2026Article
- Myocardial Fibrosis in Hypertrophic Cardiomyopathy: Leveraging Quantitative Imaging and Artificial Intelligence for Precision Management.Reviews in cardiovascular medicine · 2026Review
- Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial-intelligence-based feature mapping of native oxygenation-sensitive cardiovascular magnetic resonance images for classifying cardiomyopathies.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2026Article
- Contrast-induced acute kidney injury and nephrogenic systemic fibrosis in children.Pediatric nephrology (Berlin, Germany) · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Enhanced Telerehabilitation in Post-Acute Coronary Syndrome: A Narrative Review of Opportunities, Evidence, and Future Directions.Life (Basel, Switzerland) · 2026Review
- Contrast-Free Myocardial Infarction Segmentation with Attention U-Net.Diagnostics (Basel, Switzerland) · 2026Article
- From Artificial Intelligence to Robotics: How to Navigate Technological Innovation in Radiology With the Gartner Hype Cycle.Academic radiology · 2026Review
- Knowledge-Guided Framework for Synthesizing Contrast-Dependent Data from Multi-Sequence Non-Contrast MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Synthetic Contrast-Free LGE via Diffusion-Based Framework in Acute MI for Image Quality and Quantitative Scar Analysis.Circulation. Cardiovascular imaging · 2026Article
- Synthetic T1 mapping using artificial intelligence and oxygenation-sensitive cardiovascular magnetic resonance for myocardial tissue characterization in hypertrophic cardiomyopathy and cardiac amyloidosis.Frontiers in cardiovascular medicine · 2026Article
- Promises and challenges of AI-enabled methods for myocardial characterisation in cardiovascular magnetic resonance.Frontiers in cardiovascular medicine · 2026Review
- A call to action: improving access to cardiac MRI for diagnosis of immune checkpoint inhibitor related myocarditis in low and middle income countries.Cardio-oncology (London, England) · 2025Review
- Review
- The Role of Imaging Modalities in Estimating Myocardial Viability: A Narrative Review.Journal of clinical medicine · 2025Review
Corrections and comments
- Commented on by
Authors and funding
20 authors at 6 institutions in 4 countries.
Funding
Abstract
backgroundMyocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems.
methodsVirtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (
resultsVNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all
conclusionsVNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.