  Magnetic resonance imaging (MRI) is an important non-invasive clinical tool
that can produce high-resolution and reproducible images. However, a long
scanning time is required for high-quality MR images, which leads to exhaustion
and discomfort of patients, inducing more artefacts due to voluntary movements
of the patients and involuntary physiological movements. To accelerate the
scanning process, methods by k-space undersampling and deep learning based
reconstruction have been popularised. This work introduced SwinMR, a novel Swin
transformer based method for fast MRI reconstruction. The whole network
consisted of an input module (IM), a feature extraction module (FEM) and an
output module (OM). The IM and OM were 2D convolutional layers and the FEM was
composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D
convolutional layers. The RSTB consisted of a series of Swin transformer layers
(STLs). The shifted windows multi-head self-attention (W-MSA/SW-MSA) of STL was
performed in shifted windows rather than the multi-head self-attention (MSA) of
the original transformer in the whole image space. A novel multi-channel loss
was proposed by using the sensitivity maps, which was proved to reserve more
textures and details. We performed a series of comparative studies and ablation
studies in the Calgary-Campinas public brain MR dataset and conducted a
downstream segmentation experiment in the Multi-modal Brain Tumour Segmentation
Challenge 2017 dataset. The results demonstrate our SwinMR achieved
high-quality reconstruction compared with other benchmark methods, and it shows
great robustness with different undersampling masks, under noise interruption
and on different datasets. The code is publicly available at
https://github.com/ayanglab/SwinMR.
