ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer

TL;DR

ReconFormer is a recurrent transformer model designed for accelerated MRI reconstruction, effectively handling highly under-sampled data by leveraging multi-scale information and recurrent states, resulting in improved performance and efficiency.

Contribution

The paper introduces ReconFormer, a novel recurrent transformer architecture utilizing Recurrent Pyramid Transformer Layers for efficient and accurate MRI reconstruction from under-sampled data.

Findings

Achieves significant improvements over state-of-the-art methods.

Demonstrates better parameter efficiency across multiple datasets.

Effectively reconstructs high-quality images from highly under-sampled k-space data.

Abstract

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.