The k-space data generated from magnetic resonance imaging (MRI) is only a finite sampling of underlying signals. Therefore, MRI images often suffer from low spatial resolution and Gibbs ringing artifacts. Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images. It is also a challenge that high resolution ground truth is hard to obtain in clinical MRI. In this paper, we propose an unsupervised learning framework for both MRI super resolution and Gibbs artifacts removal without using high resolution ground truth. Furthermore, we propose regularization methods to improve the model's generalizability across out-of-distribution MRI images. We evaluated our proposed methods with other state-of-the-art methods on eight MRI datasets with various contrasts and anatomical structures. Our method not only achieves the best SR performance but also significantly reduces the Gibbs artifacts. Our method also demonstrates good generalizability across different datasets, which is beneficial to clinical applications where training data are usually scarce and biased.

翻译：磁共振成像（MRI）生成的k空间数据仅是潜在信号的有限采样，因此MRI图像常面临低空间分辨率和吉布斯振铃伪影问题。以往研究分别处理这两个问题：超分辨率方法易增强吉布斯伪影，而吉布斯振铃去除方法易导致图像模糊。此外，临床MRI中难以获取高分辨率真实数据也是一大挑战。本文提出一种无需高分辨率真实数据的无监督学习框架，可同时实现MRI超分辨率和吉布斯伪影去除。我们进一步引入正则化方法，提升模型对分布外MRI图像的泛化能力。在包含多种对比度和解剖结构的八个MRI数据集上，我们与前沿方法进行了对比评估。所提方法不仅实现了最优的超分辨率性能，还显著减少了吉布斯伪影。此外，该方法在不同数据集间展现出良好的泛化能力，这对训练数据通常稀缺且存在偏倚的临床应用场景具有重要价值。