Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning–Based Denoising

作者：Kang Wang, Matthew J. Middione, Andreas M. Loening, Ali Syed, Ariel Hannum, Xinzeng Wang, Arnaud Guidon, Patricia Lan, Daniel B. Ennis, Ryan L. Brunsing · 发表于：Investigative Radiology · 年份：2025 · DOI：10.1097/rli.0000000000001148 · 被引用次数：4 · 研究领域：MRI in cancer diagnosis、Advanced Neuroimaging Techniques and Applications、Pancreatic and Hepatic Oncology Research

OBJECTIVES: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal. Thus, a method that combines msDWI with MCGs while minimizing the echo time penalty and maximizing signal would improve pancreatic DWI. In this work, we combine MCGs generated via convex-optimized diffusion encoding (CODE), which reduces the echo time penalty of motion compensation, with deep learning (DL)-based denoising to address residual signal loss. We hypothesize this method will qualitatively and quantitatively improve msDWI of the pancreas. MATERIALS AND METHODS: This prospective institutional review board-approved study included 22 patients who underwent abdominal MR examinations from August 22, 2022 and May 17, 2023 on 3.0 T scanners. Following informed consent, 2-shot spin-echo echo-planar DWI (b = 0, 800 s/mm 2 ) without (M0) and with (M1) CODE-generated first-order gradient moment nulling was added to their c...