ArticleFrontiers in cardiovascular medicine2024
Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Monitoring carotid arterial stiffness using non-contrast-enhanced 4D MR angiography.European radiology experimental · 2026Article
- Utility of Thin-slice Single-shot T2-weighted MR Imaging with Deep Learning Reconstruction as a Protocol for Evaluating Pancreatic Cystic Lesions.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- The Use of Ultrasound Imaging in Continuous Blood Vessel Area and Velocity Data Acquisition for Determining the Local Pulse Wave Velocity.Journal of clinical medicine · 2025Review
- Effects of Deep Learning-Based Reconstruction on the Quality of Accelerated Contrast-Enhanced Neck MRI.Korean journal of radiology · 2025Article
- Deep learning-based denoising image reconstruction of body magnetic resonance imaging in children.Pediatric radiology · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- The New Frontiers of Fetal Imaging: MRI Insights into Cardiovascular and Thoracic Structures.Journal of clinical medicine · 2024Review
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD). Methods: Twenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins. Results: Fetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2-4) vs. 5 (4-5), Conclusion: DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.
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