Magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy in clinical settings. However, the inherent long scan time of MRI restricts its widespread applicability. Deep learning-based image super-resolution (SR) methods exhibit promise in improving MRI resolution without additional cost. Due to lacking of aligned high-resolution (HR) and low-resolution (LR) MRI image pairs, unsupervised approaches are widely adopted for SR reconstruction with unpaired MRI images. However, these methods still require a substantial number of HR MRI images for training, which can be difficult to acquire. To this end, we propose an unpaired MRI SR approach that employs contrastive learning to enhance SR performance with limited HR training data. 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 HR training data, thereby contributing to the advancement of MRI in clinical applications.

翻译：磁共振成像（MRI）对于提高临床诊断准确性至关重要。然而，MRI固有的长扫描时间限制了其广泛应用。基于深度学习的图像超分辨率（SR）方法有望在不增加额外成本的情况下提高MRI分辨率。由于缺乏对齐的高分辨率（HR）和低分辨率（LR）MRI图像对，无监督方法被广泛用于非配对MRI图像的SR重建。然而，这些方法仍需要大量HR MRI图像进行训练，而这些图像的获取可能较为困难。为此，我们提出了一种非配对MRI SR方法，该方法采用对比学习在有限HR训练数据下增强SR性能。本研究呈现的实验结果表明，即使在HR图像稀缺的情况下，峰值信噪比和结构相似性指数仍得到显著提升。这些发现凸显了我们的方法在解决HR训练数据有限这一挑战方面的潜力，从而促进MRI在临床应用中的发展。