Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography

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_DL) regarding acquisition time and image quality.

We conducted a prospective study of consecutive patients referred for MRCP between November 2023 and April 2024 at a single tertiary center. Each participant underwent 1.5T 3-D T2-weighted turbo spin echo MRCP using both a conventional sequence (threefold acceleration) and MRCP_DL (eightfold acceleration). Three blinded readers independently evaluated image quality, including background signal suppression, bile and pancreatic duct visibility, artifact level, and diagnostic confidence on an ordinal four-point scale. Acquisition times were compared using a paired t-test. Image quality parameters were assessed with repeated measures ANOVA. Interreader agreement was analyzed using Fleiss' κ.

Out of 419 consecutive patients, 30 participants were evaluated (mean age, 63 ± 15 years; 16 men, 14 women). The mean acquisition time was 10:30 ± 03:04 min for conventional MRCP and 3:57 ± 01:13 min for MRCP_DL, P < 0.001. MRCP_DL reduced acquisition time by 62.4%. Artifact levels were rated at 3.17 ± 0.77 for conventional MRCP and 3.56 ± 0.66 for MRCP_DL (P = 0.041). Background signal suppression, bile duct visibility, pancreatic duct visibility, and diagnostic confidence did not differ significantly (P > 0.05). Interreader agreement was substantial to almost perfect (κ: 0.64-87).

Deep learning-accelerated 3-D MRCP reduced acquisition time by 62%, minimized artifacts, and preserved bile and pancreatic duct visibility, supporting its adoption in routine clinical practice.