Affiliations: [a] College of Information Engineering, China Jiliang University, Hangzhou, China | [b] Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
Abstract: Magnetic Resonance Imaging (MRI) is a cornerstone of modern medical diagnosis due to its ability to visualize intricate soft tissues without ionizing radiation. However, noise artifacts significantly degrade image quality, hindering accurate diagnosis. Traditional denoising methods struggle to preserve details while effectively reducing noise. While deep learning approaches show promise, they often focus on local information, neglecting long-range dependencies. To address these limitations, this study proposes the deep and shallow feature fusion denoising network (DAS-FFDNet) for MRI denoising. DAS-FFDNet combines shallow and deep feature extraction with a tailored fusion module, effectively capturing both local and global image information. This approach surpasses existing methods in preserving details and reducing noise, as demonstrated on publicly available T1-weighted and T2-weighted brain image datasets. The proposed model offers a valuable tool for enhancing MRI image quality and subsequent analyses.
Keywords: Magnetic resonance imaging (MRI), image denoising, deep learning, UNet, convolutional neural networks (CNNs)
