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SMIR: A Transformer-Based Model for MRI super-resolution reconstruction

Down-sampling magnetic resonance imaging super-resolution reconstruction is one of the main problems in the field of accelerating magnetic resonance imaging research. The current method with better results is the traditional method of compressed sensing, but the solution requires iteration, which consumes a lot of time and only solves the reconstruction for a single image. At present, the more advanced image restoration methods are based on convolutional neural networks, but few people have tried to apply Transformer to the field of medical image reconstruction and have shown relatively good results. In this article, we propose a magnetic resonance imaging reconstruction model SMIR based on Swin Transformers, namely SMIR. SMIR consists of two modules: a multi-level feature extraction module and a reconstruction module. The model combines frequency domain and spatial domain losses to better reconstruct image details. We compared this model with some traditional image processing methods and advanced convolutional neural networks image restoration methods. The experimental results show that our method achieves the best results.