Domain-Distance Adapted Super-Resolution Reconstruction of Low-Field MR Brain Images
Cong, S.; Yang, Y.; Zhou, Y.; Wang, X.; Luo, H.; Wei, Y.; Liang, H.; Yao, X.
Show 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 The proposed approach yields visually convincing textures and successfully restores outdated MRI data from the ADNI1 dataset, outperforming state-of-the-art SR approaches in both perceptual and quantitative evaluations.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.
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