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

作者：Sun Kyung Jeon, Jeong Min Lee, Junghoan Park, Sungjun Hwang, Rae Rim Ryu · 发表于：Cancer Imaging · 年份：2025 · DOI：10.1186/s40644-025-00932-7 · 被引用次数：1 · 研究领域：Pancreatic and Hepatic Oncology Research、MRI in cancer diagnosis、Gallbladder and Bile Duct Disorders

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 phase and 3-min delayed phase were also acquired using DL-SR CS-VIBE. The image quality of standard and DL-SR CS-VIBE PVP sequences was compared using Wilcoxon signed-rank test. The diagnostic performance of full-sequence pancreatobiliaryMRI including DL-SR CS-VIBE for predicting malignant IPMN was assessed using multi-reader multi-case analysis. Diagnostic accuracy was assessed using receiver operating characteristic analysis, while sensitivity and specificity were estimated with corresponding 95% confidence intervals. RESULTS: Among 162 patients, 15 had malignant IPMN, while 147 had benign IPMN. DL-SR CS-VIBE demonstrated significantly better overall image quality (3.73 ± 0.33 vs. 3.22 ± 0.43) and cysti...