High-resolution (HR) magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy in clinical settings. Nonetheless, the inherent limitation of MRI resolution restricts its widespread applicability. Deep learning-based image super-resolution (SR) methods exhibit promise in improving MRI resolution without additional cost. However, these methods frequently require a substantial number of HR MRI images for training, which can be challenging to acquire. In this paper, we propose an unpaired MRI SR approach that employs self-supervised contrastive learning to enhance SR performance with limited training data. Our approach leverages both authentic HR images and synthetically generated SR images to construct positive and negative sample pairs, thus facilitating the learning of discriminative features. Empirical results presented in this study underscore significant enhancements in the peak signal-to-noise ratio and structural similarity index, even when a paucity of HR images is available. These findings accentuate the potential of our approach in addressing the challenge of limited training data, thereby contributing to the advancement of high-resolution MRI in clinical applications.

翻译：高分辨率磁共振成像（MRI）对于提升临床诊断精度至关重要。然而，MRI固有的分辨率限制制约了其广泛应用。基于深度学习的图像超分辨率（SR）方法无需额外成本即可改善MRI分辨率。但这些方法通常需要大量高分辨率MRI图像进行训练，而此类数据往往难以获取。本文提出一种非配对MRI超分辨率方法，通过自监督对比学习在有限训练数据条件下增强超分辨率性能。该方法利用真实高分辨率图像与合成超分辨率图像构建正负样本对，从而促进判别特征的学习。研究实验结果表明，即便在高分辨率图像匮乏的情况下，该方法仍能显著提升峰值信噪比与结构相似性指标。这些发现凸显了该方法在解决训练数据不足这一挑战中的潜力，进而推动高分辨率MRI在临床应用中取得进展。