  Dynamic magnetic resonance imaging (DMRI) is an effective imaging tool for
diagnosis tasks that require motion tracking of a certain anatomy. To speed up
DMRI acquisition, k-space measurements are commonly undersampled along spatial
or spatial-temporal domains. The difficulty of recovering useful information
increases with increasing undersampling ratios. Compress sensing was invented
for this purpose and has become the most popular method until deep learning
(DL) based DMRI reconstruction methods emerged in the past decade.
Nevertheless, existing DL networks are still limited in long-range sequential
dependency understanding and computational efficiency and are not fully
automated. Considering the success of Transformers positional embedding and
"swin window" self-attention mechanism in the vision community, especially
natural video understanding, we hereby propose a novel architecture named
Reconstruction Swin Transformer (RST) for 4D MRI. RST inherits the backbone
design of the Video Swin Transformer with a novel reconstruction head
introduced to restore pixel-wise intensity. A convolution network called
SADXNet is used for rapid initialization of 2D MR frames before RST learning to
effectively reduce the model complexity, GPU hardware demand, and training
time. Experimental results in the cardiac 4D MR dataset further substantiate
the superiority of RST, achieving the lowest RMSE of 0.0286 +/- 0.0199 and 1 -
SSIM of 0.0872 +/- 0.0783 on 9 times accelerated validation sequences.

Image and Video Processing;Computer Vision and Pattern Recognition