ArticlePediatric radiology2024
Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.
Article in Pediatric radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- 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
- Pushing the limits of cardiac MRI: deep-learning based real-time cine imaging in free breathing vs breath hold.European 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
- Free-running isotropic three-dimensional cine magnetic resonance imaging with deep learning image reconstruction.Pediatric radiology · 2025Article
- Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.Pediatric radiology · 2025Article
- Cardiovascular magnetic resonance in Tetralogy of Fallot-state of the art.Cardiovascular diagnosis and therapy · 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
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9 authors.
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Abstract
backgroundVentricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds.
objectiveTo assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences. MATERIAL AND
methodsTwenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1-17 years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experienced readers who visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland-Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC).
resultsMean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8 s vs. 218.3 ± 44.8 s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1 g vs. 69.9 ± 32.5 g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05).
conclusionDeep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.
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