ArticleSkeletal radiology2023
Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.
Article in Skeletal radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Best fast MRI protocols for the knee: advantages and limitations.Skeletal radiology · 2026Review
- Enhanced image quality in head and neck MRI with PROPELLER and deep learning reconstruction.European journal of radiology open · 2026Article
- Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.European radiology experimental · 2026Article
- Prospective study assessing the validity of accelerated 2D Fast Spin Echo (2D FSE) based high-resolution knee MRI and T2 mapping using deep learning reconstruction.BMC musculoskeletal disorders · 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
- Advances in Shoulder Pain Imaging: A Narrative Review of Current Practice and Emerging Trends.Current medical imaging · 2025Article
- Deep learning reconstruction for accelerating synthetic magnetic resonance imaging of the breast: a comparative analysis of accelerated and standard protocols.Quantitative imaging in medicine and surgery · 2025Article
- Is a 3-Minute Knee MRI Protocol Sufficient for Daily Clinical Practice? A SuperResolution Reconstruction Approach Using AI and Compressed Sensing.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.Dento maxillo facial radiology · 2025Article
- Improving image quality using the pause function combination to PROPELLER sequence in brain MRI: a phantom study.Radiological physics and technology · 2024Article
- Review
- Article
- Impact of deep learning-based reconstruction and anti-peristaltic agent on the image quality and diagnostic performance of magnetic resonance enterography comparing single breath-hold single-shot fast spin echo with and without anti-peristaltic agent.Quantitative imaging in medicine and surgery · 2024Article
- Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers.European radiology experimental · 2023Article
- Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI.Diagnostics (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
objectiveTo compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder. MATERIALS AND
methodsWe included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.
resultsNinety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1).
conclusionAccelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.
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