ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2022
An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance.
Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Application of AI-assisted compressed sensing cine sequence in the assessment of cardiac function in children with Kawasaki disease.Translational pediatrics · 2026Article
- Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.Journal of magnetic resonance imaging : JMRI · 2026Article
- Dynamic Mode Decomposition (DMD) for Low-Latency Real-Time Cardiac MRI.Magnetic resonance in medicine · 2026Article
- Retrospectively synchronized time-resolved ventricular cine images from 2D real-time exercise cardiac magnetic resonance imaging.Clinical physiology and functional imaging · 2025Article
- Simultaneous multi-slice cardiac real-time MRI at 0.55T.Magnetic resonance in medicine · 2025Article
- Fully automated on-scanner aortic four-dimensional flow magnetic resonance imaging processing and hemodynamic analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Accelerated real-time cine and flow under in-magnet staged exercise.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- The cardiovascular exercise response in children with overweight or obesity measured by cardiovascular magnetic resonance imaging.International journal of obesity (2005) · 2024Article
- Next-Generation Cardiac Magnetic Resonance Imaging Techniques for Characterization of Myocardial Disease.Current treatment options in cardiovascular medicine · 2024Article
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.Radiology. Cardiothoracic imaging · 2024Article
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- Physical exercise and cardiovascular response: design and implementation of a pediatric CMR cohort study.The international journal of cardiovascular imaging · 2023Article
- A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T.Magma (New York, N.Y.) · 2023Article
- High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
15 authors.
Funding
Abstract
backgroundExercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR.
methodsA 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal).
resultsThe DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [- 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [- 1.3, 15.3], P < 0.001) and LV ejection fraction (- 5.0% [- 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame.
conclusionsOur proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR.
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