  Fast MRI aims to reconstruct a high fidelity image from partially observed
measurements. Exuberant development in fast MRI using deep learning has been
witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer
based models, are fast-growing in natural language processing and promptly
developed for computer vision and medical image analysis due to their prominent
performance. Nevertheless, due to the complexity of the Transformer, the
application of fast MRI may not be straightforward. The main obstacle is the
computational cost of the self-attention layer, which is the core part of the
Transformer, can be expensive for high resolution MRI inputs. In this study, we
propose a new Transformer architecture for solving fast MRI that coupled
Shifted Windows Transformer with U-Net to reduce the network complexity. We
incorporate deformable attention to construe the explainability of our
reconstruction model. We empirically demonstrate that our method achieves
consistently superior performance on the fast MRI task. Besides, compared to
state-of-the-art Transformer models, our method has fewer network parameters
while revealing explainability. The code is publicly available at
https://github.com/ayanglab/SDAUT.
