High-acceleration pancreatobiliary MRI with deep learning-based super-resolution reconstruction for evaluating presumed pancreatic intraductal papillary mucinous neoplasm.

ABSTRACT:

Background

To evaluate the feasibility and diagnostic utility of a deep learning (DL)-based super-resolution (SR) reconstruction algorithm applied to pancreatobiliary MRI for assessing pancreatic intraductal papillary mucinous neoplasms (IPMNs).

Methods

This retrospective study included 162 patients with presumed pancreatic IPMN (≥ 1 cm) who underwent pancreatobiliary MRI between May 2019 and May 2022. Two portal venous phase (PVP) images of dynamic T1-wegithed imaging were sequentially acquired: early PVP image obtained using standard compressed sensing (CS)-volumetric interpolated breath-hold examination (VIBE) (standard CS-VIBE) and late PVP image obtained using CS-VIBE with DL-based SR reconstruction algorithm to generate 1 mm-thickness images (DL-SR CS-VIBE). Arterial p

SUBMITTER: Jeon SK 

PROVIDER: S-EPMC12551210 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature