Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, existing deep learning-based SR methods predominantly rely on convolutional neural networks (CNNs), which inherently limit the expressive capabilities of these models and therefore make it challenging to discover potential relationships between different image features. To overcome this limitation, we propose an A-network that utilizes multiple convolution operator feature extraction modules (MCO) for extracting image features using multiple convolution operators. These extracted features are passed through multiple sets of cross-feature extraction modules (MSC) to highlight key features through inter-channel feature interactions, enabling subsequent feature learning. An attention-based sparse graph neural network module is incorporated to establish relationships between pixel features, learning which adjacent pixels have the greatest impact on determining the features to be filled. To evaluate our model's effectiveness, we conducted experiments using different models on data generated from multiple datasets with different degradation multiples, and the experimental results show that our method is a significant improvement over the current state-of-the-art methods.

翻译：磁共振成像（MRI）是一种展示解剖结构并辅助准确诊断的重要临床工具。利用深度学习技术的医学图像超分辨率（SR）重建能够增强病变分析，帮助医生提高诊断效率和准确性。然而，现有的基于深度学习的SR方法主要依赖卷积神经网络（CNN），这从根本上限制了模型的表达能力，因此难以发现不同图像特征之间的潜在关系。为克服这一局限，我们提出了一种A网络，该网络利用多个卷积算子特征提取模块（MCO），通过多种卷积算子提取图像特征。这些提取的特征经过多组跨特征提取模块（MSC），通过通道间特征交互突出关键特征，从而促进后续特征学习。此外，引入基于注意力的稀疏图神经网络模块，以建立像素特征之间的关系，学习哪些相邻像素对确定待填充特征的影响最大。为评估模型的有效性，我们使用不同模型在来自多个数据集并以不同退化倍数生成的数据上进行了实验，实验结果表明，我们的方法相较于当前最先进方法有显著改进。