EAUR-Net: Enhancing MRI Reconstruction with Edge-Aware Undersampling and Deep Learning

Magnetic resonance imaging (MRI) is an essential imaging technique used for detailed anatomical assessment and clinical decision-making. Conventional MRI acquisition methods, however, are often time-consuming and resource-demanding. To overcome these limitations, compressed sensing (CS) approaches have been developed to accelerate MRI data acquisition by exploiting image sparsity. In this work, we present the Edge-Aware Undersampling and Reconstruction Network (EAUR-Net), an innovative deep learning architecture designed to enhance MRI reconstruction by incorporating dynamic edge-based sampling strategies. EAUR-Net focuses on intelligently sampling data points based on edge information, which is critical for preserving key structural details and improving reconstruction quality while reducing the amount of acquired data. This paper provides a thorough evaluation of EAUR-Net, detailing its architectural components, training procedures, experimental outcomes, and potential future improvements.