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.