DDASR: Domain-Distance Adapted Super-Resolution Reconstruction of MR Brain Images

Abstract High detail and fast magnetic resonance imaging (MRI) sequences are highly demanded in clinical settings, as inadequate imaging information can lead to diagnostic difficulties. MR image super-resolution (SR) is a promising way to address this issue, but its performance is limited due to the practical difficulty of acquiring paired low- and high-resolution (LR and HR) images. Most existing methods generate these pairs by down-sampling HR images, a process that often fails to capture complex degradations and domain-specific variations. In this study, we propose a domain-distance adapted SR framework (DDASR), which includes two stages: the domain-distance adapted down-sampling network (DSN) and the GAN-based super-resolution network (SRN). The DSN incorporates characteristics from unpaired LR images during down-sampling process, enabling the generation of domain-adapted LR images. Additionally, we present a novel GAN with enhanced attention U-Net and multi-layer perceptual loss. The proposed approach yields visually convincing textures and successfully restores outdated MRI data from the ADNI1 dataset, outperforming state-of-the-art SR approaches in both perceptual and quantitative evaluations. Code is available at https://github.com/Yaolab-fantastic/DDASR .