ArticleRadiology. Cardiothoracic imaging2024
Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.
Article in Radiology. Cardiothoracic imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- Clinical evaluation of accelerated breath-held and free-breathing cine cardiac MRI using model-based deep learning reconstruction (SonicDL) in children and young adults.Pediatric radiology · 2026Article
- Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.Pediatric radiology · 2026Review
- Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated HyperpolarizedJournal of magnetic resonance imaging : JMRI · 2026Article
- Reproducibility of cardiac volumetric parameters derived from fully automatically prescribed image planes: a direct comparison to manual planning at 1.5-T and 3-T MRI.European radiology · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review.Biotech (Basel (Switzerland)) · 2026Review
- Fully Automated Plane Prescription in Cardiac MRI: A Prospective Cohort Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- Pushing the limits of cardiac MRI: deep-learning based real-time cine imaging in free breathing vs breath hold.European radiology · 2026Article
- 4D-Flow Cardiac Magnetic Resonance Imaging: An 8-Year Clinical Practice Review.Reviews in cardiovascular medicine · 2026Review
- Intra- and inter-field strength reproducibility of deep-learning based real-time cardiac MRI cine sequences with breath hold and in free breathing.Scientific reports · 2025Article
- Deep Learning-Based Breath-Hold and Free-Breathing Cine MRI for Comprehensive Cardiac Evaluation.Korean journal of radiology · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Qualitative and Quantitative Evaluation of a Deep Learning-Based Reconstruction for Accelerated Cardiac Cine Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- Clinical feasibility of two cardiac deep learning cine magnetic resonance imaging sequences: Single-breath-hold and free-breathing motion-corrected approaches.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Ultrafast deep learning super-resolution single-shot T2-weighted imaging for robust edema visualization in cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to March 2023) included 55 healthy volunteers who underwent a noncontrast cardiac MRI examination at 1.5 T. Short-axis stack DL cine sequences of the left ventricle (LV) were performed over one (1RR), three (3RR), and six cardiac (6RR) cycles and compared with a standard cine sequence (without DL, performed over 10-12 cardiac cycles) in regard to acquisition time, subjective image quality, edge sharpness, and volumetric results. Results Total acquisition time (median) for a short-axis stack was 47 seconds for the 1RR cine, 108 seconds for 3RR cine, 184 seconds for 6RR cine, and 227 seconds for the standard sequence. Volumetric results showed no difference for the conventional cine (median LV ejection fraction [EF] 63%), 6RR cine (median LVEF, 62%), and 3RR cine (median LVEF, 61%). The 1RR cine sequence significantly underestimated EF (57%) because of a different segmentation of the papillary muscles. Subjective image quality (
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.