ArticleDiagnostics (Basel, Switzerland)2022
Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07164651 (Prospective Application Study of Intelligent Accelerated Magnetic Resonance Imaging in Children With Abdominal Pain), which is not on this map. Cited by 15 papers.
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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.
Prospective Application Study of Intelligent Accelerated Magnetic Resonance Imaging in Children With Abdominal Pain
Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- Deep learning-based super-resolution ultrashort echo time MRI for pulmonary nodule detection and longitudinal assessment in emphysema.BMC pulmonary medicine · 2026Article
- Reducing respiratory motion artifacts in gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI): assessing assisted breath-holding effectiveness.Quantitative imaging in medicine and surgery · 2026Article
- Effects of Artificial Intelligence-based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- High-acceleration pancreatobiliary MRI with deep learning-based super-resolution reconstruction for evaluating presumed pancreatic intraductal papillary mucinous neoplasm.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients.Radiology advances · 2025Article
- Deep learning enabled near-isotropic CAIPIRINHA VIBE in the nephrogenic phase improves image quality and renal lesion conspicuity.European journal of radiology open · 2025Article
- AI-augmented reconstruction provides improved image quality and enables shorter breath-holds in contrast-enhanced liver MRI.European radiology experimental · 2025Article
- Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI.Radiology advances · 2025Article
- High-resolution deep learning-reconstructed T2-weighted imaging for the improvement of image quality and extraprostatic extension assessment in prostate MRI.Frontiers in radiology · 2025Article
- Article
- Enhancing gadoxetic acid-enhanced liver MRI: a synergistic approach with deep learning CAIPIRINHA-VIBE and optimized fat suppression techniques.European radiology · 2024Article
- Article
- Advanced Abdominal MRI Techniques and Problem-Solving Strategies.Journal of the Korean Society of Radiology · 2024Review
- LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn's disease: utility in noise reduction and image quality improvement.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBEStd), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBESR). Image analysis of 40 patients with a mean age of 56 years (range 18−84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBESR compared to VIBEStd (each p < 0.001). Lesion detectability was better for VIBESR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBEStd, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBESR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.
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Registered trials
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