Gao QY, Wang LJ, Ma C. Diffusion-weighted magnetic resonance imaging of the pancreas: A narrative review. World J Radiol 2025; 17(10): 112271 [PMID: 41180908 DOI: 10.4329/wjr.v17.i10.112271]
Corresponding Author of This Article
Chao Ma, MS, Professor, Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, No. 168 Changhai Road, Shanghai 200433, China. machaohdsd@126.com
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Gastroenterology & Hepatology
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Minireviews
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Oct 28, 2025 (publication date) through Nov 21, 2025
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World Journal of Radiology
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1949-8470
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Gao QY, Wang LJ, Ma C. Diffusion-weighted magnetic resonance imaging of the pancreas: A narrative review. World J Radiol 2025; 17(10): 112271 [PMID: 41180908 DOI: 10.4329/wjr.v17.i10.112271]
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
Abstract
Diffusion-weighted magnetic resonance imaging (DWI) has become an essential tool in the field of pancreatic magnetic resonance imaging, enabling the detection, characterization, prediction, and evaluation of pancreatic diseases. In this article, we review the acquisition parameters, postprocessing techniques, and quantitative methods utilized in pancreatic DWI. Various postprocessing models, including monoexponential, biexponential, stretched exponential and non-Gaussian kurtosis models, as well as deep learning networks, have been used to assess the clinical utility of these models in diagnosing pancreatic diseases. The single-shot echo-planar imaging sequence is the most commonly used sequence for DWI data acquisition in clinical settings, and the apparent diffusion coefficient (ADC) calculated using the monoexponential model is the most widely used quantitative parameter in clinical practice. The repeatability threshold for the ADC of a normal pancreas is 37% for test-retest scans, but the repeatability threshold for pancreatic tumors needs to be further investigated. Complex postprocessing models exploring novel DWI-based biomarkers beyond ADC to assess histological features, and artificial intelligence in DWI postprocessing and data analyses hold promise in the diagnosis of pancreatic diseases. Future work should focus on standardizing protocols, conducting multicentre studies, and exploring variety of methods to improve the accuracy of quantitative DWI results to increase the clinical effectiveness of DWI in patients with pancreatic diseases.
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.
