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World J Radiol. Oct 28, 2025; 17(10): 112271
Published online Oct 28, 2025. doi: 10.4329/wjr.v17.i10.112271
Diffusion-weighted magnetic resonance imaging of the pancreas: A narrative review
Qing-Yu Gao, Li-Jia Wang, Chao Ma
Qing-Yu Gao, Li-Jia Wang, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Qing-Yu Gao, Chao Ma, Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China
Chao Ma, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Author contributions: Gao QY wrote the paper; Wang LJ and Ma C performed the collected the data. All authors reviewed and edited the paper.
Supported by National Natural Science Foundation of China, No. 62472315; and Shanghai Science and Technology Innovation Action Plan Medical Innovation Research Project, No. 20Y11912500.
Conflict-of-interest statement: Authors declare no conflict of interests for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chao Ma, MS, Professor, Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, No. 168 Changhai Road, Shanghai 200433, China. machaohdsd@126.com
Received: July 22, 2025
Revised: August 20, 2025
Accepted: October 10, 2025
Published online: October 28, 2025
Processing time: 98 Days and 3 Hours
Core Tip

Core Tip: The development of diffusion-weighted magnetic resonance imaging (DWI) has been important for advancing pancreatic magnetic resonance imaging. In this article, we offer a narrative review of the utilization of both foundational DWI techniques and deep learning algorithms in the diagnosis of pancreatic disorders. Additionally, we delve into the possible advantages of employing sophisticated models in DWI data analysis for the detection and diagnosis of pancreatic diseases.