  High-resolution (HR) magnetic resonance imaging is critical in aiding doctors
in their diagnoses and image-guided treatments. However, acquiring HR images
can be time-consuming and costly. Consequently, deep learning-based
super-resolution reconstruction (SRR) has emerged as a promising solution for
generating super-resolution (SR) images from low-resolution (LR) images.
Unfortunately, training such neural networks requires aligned authentic HR and
LR image pairs, which are challenging to obtain due to patient movements during
and between image acquisitions. While rigid movements of hard tissues can be
corrected with image registration, aligning deformed soft tissues is complex,
making it impractical to train neural networks with authentic HR and LR image
pairs. Previous studies have focused on SRR using authentic HR images and
down-sampled synthetic LR images. However, the difference in degradation
representations between synthetic and authentic LR images suppresses the
quality of SR images reconstructed from authentic LR images. To address this
issue, we propose a novel Unsupervised Degradation Adaptation Network (UDEAN).
Our network consists of a degradation learning network and an SRR network. The
degradation learning network downsamples the HR images using the degradation
representation learned from the misaligned or unpaired LR images. The SRR
network then learns the mapping from the down-sampled HR images to the original
ones. Experimental results show that our method outperforms state-of-the-art
networks and is a promising solution to the challenges in clinical settings.
