Joint MR Image Reconstruction and Super-Resolution via Mutual Co-Attention Network

Abstract

In the realm of medical diagnosis, recent strides in Deep Neural Network-guided Magnetic Resonance Imaging (MRI) restoration have shown promise. Nevertheless, persistent drawbacks overshadow these advancements. Challenges persist in balancing acquisition speed and image quality, while existing methods primarily focus on singular tasks like MRI reconstruction or super-resolution, neglecting the interplay between these tasks. To tackle these challenges, this paper introduces the Mutual Co-Attention Network (MCAN) specifically designed to concurrently address both MRI reconstruction and super-resolution tasks. Comprising multiple Mutual Cooperation Attention Blocks (MCABs) in succession, MCAN is tailored to maintain consistency between local physiological details and global anatomical structures. The intricately crafted MCAB includes a feature extraction block, a local attention block, and a global attention block. Additionally, to ensure data fidelity without compromising acquired data, we propose the Channel-wise Data Consistency (CDC) block. Thorough experimentation on the IXI and fastMRI dataset showcases MCAN’s superiority over existing state-of-the-art methods. Both quantitative metrics and visual quality assessments validate the enhanced performance of MCAN in MRI restoration. The findings underscore MCAN’s potential in significantly advancing therapeutic applications. By mitigating the trade-off between acquisition speed and image quality while simultaneously addressing both MRI reconstruction and super-resolution tasks, MCAN emerges as a promising solution in the domain of MR image restoration.