ArticleFrontiers in cardiovascular medicine2022
Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AI-Driven Generation of Post-Contrast T1 and ECV Maps from Native T1 Map in Cardiac MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR.Sensors (Basel, Switzerland) · 2026Article
- Cardiac MRI in MINOCA: Current Evidence, Parametric Mapping Advances, and Future AI Applications-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- An Innovative Artificial Intelligence Classification Model for Non-Ischemic Cardiomyopathy Utilizing Cardiac Biomechanics Derived from Magnetic Resonance Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.Scientific reports · 2025Article
- Radiomics-based detection of acute myocardial infarction on noncontrast enhanced midventricular short-axis cine CMR images.The international journal of cardiovascular imaging · 2024Article
- Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?Frontiers in cardiovascular medicine · 2024Review
- AI-powered contrast-free cardiovascular magnetic resonance imaging for myocardial infarction.Frontiers in cardiovascular medicine · 2024Review
- Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?Diagnostics (Basel, Switzerland) · 2023Review
- Quantification of Epicardial Adipose Tissue Volume and Attenuation for Cardiac CT Scans Using Deep Learning in a Single Multi-Task Framework.Reviews in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Objectives: Currently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges. Methods: The study population consists of 272 retrospectively selected CMR studies with diagnoses of MI ( Results: Overall, for the UNet segmentation model, the performance based on the mean Dice score for the test set ( Conclusion: We have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.
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