Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration

作者：Dan Zhao, Xiangchuang Kong, Kun Yang, Jiayu Wan, Ziyi Liu, Feng Pan, Peng Sun, Chuansheng Zheng, Lian Yang · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-02150-y · 被引用次数：5 · 研究领域：MRI in cancer diagnosis、Advanced Neuroimaging Techniques and Applications、Advanced MRI Techniques and Applications

Abstract Objectives To investigate the impact of deep learning reconstruction (DLR) on the image quality of diffusion-weighted imaging (DWI) for liver and its ability to differentiate benign from malignant focal liver lesions (FLLs). Materials and methods Consecutive patients with suspected liver disease who underwent liver MRI between January and May 2025 were included. All patients received conventional DWI (DWI C ) and an accelerated reconstructed DWI (DWI DLR ) in which acquisition time was prospectively halved by reducing signal averages. Image quality was compared qualitatively using Likert scores (e.g., lesion conspicuity, overall quality) and quantitatively by measuring signal-to-noise ratio of the liver (SNR Liver ) and lesion (SNR Lesion ), contrast-to-noise ratio (CNR), and edge rise distance (ERD). Apparent diffusion coefficient (ADC) values and diagnostic performance for differentiating benign from malignant FLLs were assessed. Results A total of 193 patients (128 males, 65 females; age range, 23-81 years) were included. For quantitative assessment, DWI DLR demonstrated higher SNR Liver , SNR Lesion , CNR, and a shorter ERD (all p < 0.05). For qualitative assessment, DWI DLR showed improved lesion conspicuity, liver edge sharpness, and overall image quality (all p < 0.01), with no significant difference in artifacts ( p = 0.08). ADC values were lower with DWI DLR for both benign and malignant FLLs ( p < 0.001). In differentiating benign from malignant le...