  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.
