AIMC Topic: Cholangiopancreatography, Magnetic Resonance
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Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography.
La Radiologia medica
Mar 18, 2025
PURPOSE: This study aimed to compare a conventional three-dimensional (3-D) magnetic resonance cholangiopancreatography (MRCP) sequence with a deep learning (DL)-accelerated MRCP sequence (hereafter, MRCP) regarding acquisition time and image quality...
Deep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study.
Journal of computer assisted tomography
Nov 13, 2024
OBJECTIVE: This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images.
3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP).
Using artificial intelligence to predict choledocholithiasis: can machine learning models abate the use of MRCP in patients with biliary dysfunction?
ANZ journal of surgery
Mar 25, 2024
BACKGROUND: Prompt diagnosis of choledocholithiasis is crucial for reducing disease severity, preventing complications and minimizing length of stay. Magnetic resonance cholangiopancreatography (MRCP) is commonly used to evaluate patients with suspec...
Convolutional neural network for identifying common bile duct stones based on magnetic resonance cholangiopancreatography.
Clinical radiology
Mar 24, 2024
AIMS: To develop an auto-categorization system based on machine learning for three-dimensional magnetic resonance cholangiopancreatography (3D MRCP) to detect choledocholithiasis from healthy and symptomatic individuals.
Using deep learning models in magnetic resonance cholangiopancreatography images to diagnose common bile duct stones.
Scandinavian journal of gastroenterology
Sep 15, 2023
BACKGROUNDS AND AIMS: Magnetic resonance cholangiopancreatography (MRCP) plays a significant role in diagnosing common bile duct stones (CBDS). Currently, there are no studies to detect CBDS by using the deep learning (DL) model in MRCP. This study a...
Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time.
European radiology
May 13, 2023
OBJECTIVES: To evaluate the image quality of the 3D hybrid profile order technique and deep-learning-based reconstruction (DLR) for 3D magnetic resonance cholangiopancreatography (MRCP) within a single breath-hold (BH) at 3 T magnetic resonance imagi...
Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.
European radiology
Nov 10, 2022
OBJECTIVES: To 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 (...
Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique.
European journal of radiology
Oct 5, 2021
OBJECTIVES: To assess the image quality of conventional respiratory-triggered 3-dimentional (3D) magnetic resonance cholangiopancreatography (Resp-MRCP) and breath-hold 3D MRCP (BH-MRCP) with and without denoising procedure using deep learning-based ...
Efficacy of an artificial neural network algorithm based on thick-slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones.
Journal of gastroenterology and hepatology
Jun 16, 2021
BACKGROUND AND AIM: Magnetic resonance cholangiopancreatography (MRCP) can accurately diagnose common bile duct (CBD) stones but is laborious to interpret. We developed an artificial neural network (ANN) capable of automatically assisting physicians ...
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