ArticleEuropean radiology2023
Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Best practices for pediatric liver MRI: guidelines from members of the Society for Pediatric Radiology Magnetic Resonance and Abdominal Imaging Committees.Pediatric radiology · 2025Guideline
- Deep learning-based respiratory-triggered magnetic resonance cholangiopancreatography: comparison with conventional respiratory-triggered and breath-hold magnetic resonance cholangiopancreatography.Abdominal radiology (New York) · 2026Article
- Comparative analysis of 3 T versus 1.5 T MRCP image quality and age-stratified biliary normative values in infants.La Radiologia medica · 2026Article
- Evaluation of high-resolution pituitary dynamic contrast-enhanced MRI using deep learning-based compressed sensing and super-resolution reconstruction.European radiology · 2025Article
- High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- 3D gadolinium-enhanced high-resolution near-isotropic pancreatic imaging at 3.0-T MR using deep-learning reconstruction.Insights into imaging · 2025Article
- Utility of Thin-slice Fat-suppressed Single-shot T2-weighted MR Imaging with Deep Learning Image Reconstruction as a Protocol for Evaluating the Pancreas.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography.La Radiologia medica · 2025Article
- Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.BMC medical imaging · 2025Article
- Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography.Japanese journal of radiology · 2025Article
- Advanced MRI techniques in abdominal imaging.Abdominal radiology (New York) · 2024Review
- Modified respiratory-triggered SPACE sequences for magnetic resonance cholangiopancreatography.European journal of radiology open · 2024Article
- Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI.Frontiers in cardiovascular medicine · 2024Article
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
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12 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo compare the image quality of three-dimensional breath-hold magnetic resonance cholangiopancreatography with deep learning-based compressed sensing reconstruction (3D DL-CS-MRCP) to those of 3D breath-hold MRCP with compressed sensing (3D CS-MRCP), 3D breath-hold MRCP with gradient and spin-echo (3D GRASE-MRCP) and conventional 2D single-shot breath-hold MRCP (2D MRCP).
methodsIn total, 102 consecutive patients who underwent MRCP at 3.0 T, including 2D MRCP, 3D GRASE-MRCP, 3D CS-MRCP, and 3D DL-CS-MRCP, were prospectively included. Two radiologists independently analyzed the overall image quality, background suppression, artifacts, and visualization of pancreaticobiliary ducts using a five-point scale. The signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast-to-noise ratio (CNR) of the CBD and liver, and contrast ratio between the periductal tissue and CBD were measured. The Friedman test was performed to compare the four protocols.
results3D DL-CS-MRCP resulted in improved SNR and CNR values compared with those in the other three protocols, and better contrast ratio compared with that in 3D CS-MRCP and 3D GRASE-MRCP (all, p < 0.05). Qualitative image analysis showed that 3D DL-CS-MRCP had better performance for second-level intrahepatic ducts and distal main pancreatic ducts compared with 3D CS-MRCP (all, p < 0.05). Compared with 2D MRCP, 3D DL-CS-MRCP demonstrated better performance for the second-order left intrahepatic duct but was inferior in assessing the main pancreatic duct (all, p < 0.05). Moreover, the image quality was significantly higher in 3D DL-CS-MRCP than in 3D GRASE-MRCP.
conclusion3D DL-CS-MRCP has superior performance compared with that of 3D CS-MRCP or 3D GRASE-MRCP. Deep learning reconstruction also provides a comparable image quality but with inferior main pancreatic duct compared with that revealed by 2D MRCP. KEY POINTS: • 3D breath-hold MRCP with deep learning reconstruction (3D DL-CS-MRCP) demonstrated improved image quality compared with that of 3D MRCP with compressed sensing or GRASE. • Compared with 2D MRCP, 3D DL-CS-MRCP had superior performance in SNR and CNR, better visualization of the left second-level intrahepatic bile ducts, and comparable overall image quality, but an inferior main pancreatic duct.
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