Residual Attention Mechanism-Based MRI Super-Resolution

MRI (Magnetic Resonance Imaging) super-resolution technology is a crucial research direction in the field of medical image processing. This paper aims to enhance the spatial resolution of MRI, improve image quality, and enhance the diagnostic capabilities of medical imaging. In this study, we first preprocess MR images to ensure the quality of input data. Subsequently, we designed a network, RPCN, based on a residual attention mechanism. This network adaptively focuses on important regions in MRI and employs the concept of residual learning to effectively capture high-frequency information for super-resolution reconstruction. In the RPCN network, we introduce a channel attention mechanism and a pixel attention mechanism, and design an attention-serial-fusion RPC structure to enable the network to more effectively explore correlations between different channels and better capture image features. Extensive experiments demonstrate the significant success of our method in enhancing MRI resolution. The introduction of residual structure and attention mechanisms allows our network to delve deeper into learning image details, enabling better reconstruction of image edges, textures, and microstructures. We also use quantitative evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to objectively assess the performance of the RPCN network structure. Multiple sets of comparative experiments validate its robustness and generality on different MRI datasets.