Enhancing MRI Resolution with a Lightweight Network and Reverse Residual Attention Fusion

Motivation: In MRI reconstruction, deep-learning methods often increase network complexity for improved super-resolution, leading to longer reconstruction times and training difficulties. Goal(s): Our solution introduces an enhanced lightweight network that maintains high-quality performance. Approach: We accomplish this by stacking Reverse Residual Attention Fusion (RRAF) with PCA and Enhanced Spatial Attention (ESA) for precise feature extraction, utilizing Transformers with depth-wise dilated convolution for better context information, and employing High-Frequency Image Refinement (HFIR) for detailed information recovery. Results: Our experiments confirm the effectiveness of our approach. Impact: Introducing the lightweight network represents an important improvement in MRI SR reconstruction. By integrating Reverse Residual Attention Fusion, it upholds exceptional image quality, streamlines network complexity, reduces reconstruction time, and simplifies training for SR MRI image reconstruction.