High-resolution (HR) MRI is critical in assisting the doctor's diagnosis and image-guided treatment but is highly time-consuming and costly to acquire. Therefore, deep learning-based super-resolution reconstruction (SRR) has been investigated to generate super-resolution images from low-resolution (LR) images. Training such neural networks requires authentic HR and LR image pairs, which are difficult to acquire due to patient movement during and between the acquisitions of LR and HR images. Rigid movements of hard tissues can be corrected with image registration. In contrast, the alignment of deformed soft tissues is challenging, making it impractical to train neural networks with authentic HR and LR image pairs. Existing studies in the literature focused on SRR using authentic HR images and down-sampled synthetic LR images. Yet, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SRR from authentic LR images. In this work, we propose a novel Unsupervised Degradation Adaptation Network (UDEAN) to mitigate this problem. Our network consists of the degradation learning network and the SRR network. The degradation learning network down-samples the HR images by addressing the degradation representation of the misaligned or unpaired LR images. The SRR network 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 can potentially be applied in real clinical settings.

翻译：高分辨率（HR）MRI对于辅助医生诊断和图像引导治疗至关重要，但获取过程耗时且成本高昂。为此，基于深度学习的超分辨率重建（SRR）方法被研究用于从低分辨率（LR）图像生成超分辨率图像。训练此类神经网络需要真实的HR和LR图像对，但由于患者在一次LR与HR图像采集期间及之间的运动，这类图像对难以获取。硬组织的刚性运动可通过图像配准校正，而变形软组织的对齐则较为困难，这使得使用真实HR和LR图像对训练神经网络不切实际。现有文献研究主要集中于利用真实HR图像与降采样合成LR图像进行SRR，但合成LR图像与真实LR图像之间降解表征的差异限制了基于真实LR图像的SRR质量。本研究提出一种新颖的无监督降解自适应网络（UDEAN）以缓解该问题。该网络包含降解学习网络与SRR网络：降解学习网络通过处理未对齐或未配对LR图像的降解表征对HR图像进行降采样，而SRR网络则学习从降采样后的HR图像到原始HR图像的映射。实验结果表明，该方法性能优于现有最优网络，并具备在真实临床环境中应用的潜力。