ArticleJournal of the American Heart Association2024
Deep Learning Virtual Contrast-Enhanced T1 Mapping for Contrast-Free Myocardial Extracellular Volume Assessment.
Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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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.
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Who cites it
5 citing papers in PubMed.
- Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review.European radiology · 2026Article
- AI-Driven Generation of Post-Contrast T1 and ECV Maps from Native T1 Map in Cardiac MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Free-Running Three-Dimensional Cardiac Extracellular Volume Mapping in a Single Scan With Mid-Scan Contrast Injection.Magnetic resonance in medicine · 2026Article
- Promises and challenges of AI-enabled methods for myocardial characterisation in cardiovascular magnetic resonance.Frontiers in cardiovascular medicine · 2026Review
- Customizing native T1 mapping: The effects of compressed sensing, deep learning-based denoising, and high-resolution on measurement of native myocardial T1.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) requires contrast agent administration and is time consuming. This study investigates generative adversarial networks for contrast-free, virtual extracellular volume (vECV) by generating virtual contrast-enhanced T1 maps. METHODS AND
resultsThis retrospective study includes 2518 registered native and contrast-enhanced T1 maps from 1000 patients who underwent cardiovascular magnetic resonance at 1.5 Tesla. Recent hematocrit values of 123 patients (hold-out test) and 96 patients from a different institution (external evaluation) allowed for calculation of conventional ECV. A generative adversarial network was trained to generate virtual contrast-enhanced T1 maps from native T1 maps for vECV creation. Mean and SD of the difference per patient (ΔECV) were calculated and compared by permutation of the 2-sided
conclusionsGeneration of vECV on the basis of native T1 maps is feasible. Multicenter training data are required to further enhance generalizability of vECV in amyloidosis.
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Registered trials
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