  Accelerating magnetic resonance image (MRI) reconstruction process is a
challenging ill-posed inverse problem due to the excessive under-sampling
operation in k-space. In this paper, we propose a recurrent transformer model,
namely ReconFormer, for MRI reconstruction which can iteratively reconstruct
high fertility magnetic resonance images from highly under-sampled k-space
data. In particular, the proposed architecture is built upon Recurrent Pyramid
Transformer Layers (RPTL), which jointly exploits intrinsic multi-scale
information at every architecture unit as well as the dependencies of the deep
feature correlation through recurrent states. Moreover, the proposed
ReconFormer is lightweight since it employs the recurrent structure for its
parameter efficiency. We validate the effectiveness of ReconFormer on multiple
datasets with different magnetic resonance sequences and show that it achieves
significant improvements over the state-of-the-art methods with better
parameter efficiency. Implementation code will be available in
https://github.com/guopengf/ReconFormer.
