ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer
This addresses the challenge of faster and more efficient MRI reconstruction for medical imaging applications, representing an incremental advancement in method design.
The paper tackles the problem of accelerating MRI reconstruction from under-sampled k-space data by proposing ReconFormer, a recurrent transformer model that achieves significant improvements over state-of-the-art methods with better parameter efficiency.
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.