Accelerated MRI Reconstruction Using a Lightweight Recurrent Transformer: ReconFormer

Accelerating MRI reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space. While state-of-the-art algorithms have shown a great progress based on convolutional neural networks (CNN), transformers for MRI reconstruction has not been fully explored in the literature. 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. 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.