ArticleRadiology2021
Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults.
Article in Radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 69 citations in OpenAlex.
- Generative AI-based low-dose digital subtraction angiography for intra-operative radiation dose reduction: a randomized controlled trial.Nature medicine · 2026Trial
- Late Gadolinium Enhancement of Nonischemic Cardiomyopathy at 5.0 T versus 3.0 T: A Crossover Design Study.Radiology. Cardiothoracic imaging · 2024Trial
- New pulse sequence for improving cardiac black-blood and PSIR MRI quality in cases of arrhythmia.Magnetic resonance letters · 2027Article
- Optimization of Respiratory Training Methods for Cardiac Magnetic Resonance Imaging.Journal of magnetic resonance imaging : JMRI · 2026Article
- Deep learning reconstruction improves appendix visualization on pediatric magnetic resonance imaging (MRI): a single-center experience.Pediatric radiology · 2026Article
- Application of AI-assisted compressed sensing cine sequence in the assessment of cardiac function in children with Kawasaki disease.Translational pediatrics · 2026Article
- 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
- Completely free-breathing cardiac MRI using deep learning reconstruction reduces sedation and scan time in children.Pediatric radiology · 2026Article
- Reference ranges for myocardial native T1, T2, and extracellular volume at 5.0T cardiac magnetic resonance imaging in healthy adults.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2026Article
- Clinical application of a full free-breathing cardiac MRI protocol: feasibility, efficiency, and diagnostic accuracy.Quantitative imaging in medicine and surgery · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Pushing the limits of cardiac MRI: deep-learning based real-time cine imaging in free breathing vs breath hold.European radiology · 2026Article
- Accelerated magnetic resonance imaging of hippocampal sclerosis in pediatric patients with deep learning-based reconstruction: comparison of image quality and diagnostic performance with conventional reconstruction.Pediatric radiology · 2026Article
- 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
- Feasibility of finger-pulse triggering as a substitute for electrocardiogram triggering in cardiac magnetic resonance T1 mapping: a prospective comparative study.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning-Based Breath-Hold and Free-Breathing Cine MRI for Comprehensive Cardiac Evaluation.Korean journal of radiology · 2025Article
- Groupwise image registration with edge-based loss for low-SNR cardiac MRI.Magnetic resonance in medicine · 2025Article
- Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.Pediatric radiology · 2025Article
- Accelerated deep learning-based function assessment in cardiovascular magnetic resonance.La Radiologia medica · 2025Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Background Obtaining ventricular volumetry and mass is key to most cardiac MRI but challenged by long multibreath-hold acquisitions. Purpose To assess the image quality and performance of a highly accelerated, free-breathing, two-dimensional cine cardiac MRI sequence incorporating deep learning (DL) reconstruction compared with reference standard balanced steady-state free precession (bSSFP). Materials and Methods A DL algorithm was developed to reconstruct custom 12-fold accelerated bSSFP cardiac MRI cine images from coil sensitivity maps using 15 iterations of separable three-dimensional convolutions and data consistency steps. The model was trained, validated, and internally tested in 10, two, and 10 adult human volunteers, respectively, based on vendor partner-supplied fully sampled bSSFP acquisitions. For prospective external clinical validation, consecutive children and young adults undergoing cardiac MRI from September through December 2019 at a single children's hospital underwent both conventional and highly accelerated short-axis bSSFP cine acquisitions in one MRI examination. Two radiologists scored overall and volumetric three-dimensional mesh image quality of all short-axis stacks on a five-point Likert scale and manually segmented endocardial and epicardial contours. Scan times and image quality were compared using the Wilcoxon rank sum test. Measurement agreement was assessed with intraclass correlation coefficient and Bland-Altman analysis. Results Fifty participants (mean age, 16 years ± 4 [standard deviation]; range, 5-30 years; 29 men) were evaluated. The mean prescribed acquisition times of accelerated scans (non-breath-held) and bSSFP (excluding breath-hold time) were 0.9 minute ± 0.3 versus 3.0 minutes ± 1.9 (
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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.