K-Space Transformer for Undersampled MRI Reconstruction

TL;DR

This paper introduces a Transformer-based method for MRI reconstruction that processes k-space data directly, leveraging an implicit spectrogram representation and hierarchical decoding to improve reconstruction quality over existing methods.

Contribution

A novel Transformer framework for k-space MRI reconstruction that uses implicit spectrogram representation and hierarchical decoding for better performance.

Findings

Outperforms state-of-the-art methods on public datasets.

Achieves superior or comparable reconstruction quality.

Balances computational cost with high-quality results.

Abstract

This paper considers the problem of undersampled MRI reconstruction. We propose a novel Transformer-based framework for directly processing signal in k-space, going beyond the limitation of regular grids as ConvNets do. We adopt an implicit representation of k-space spectrogram, treating spatial coordinates as inputs, and dynamically query the sparsely sampled points to reconstruct the spectrogram, i.e. learning the inductive bias in k-space. To strike a balance between computational cost and reconstruction quality, we build the decoder with hierarchical structure to generate low-resolution and high-resolution outputs respectively. To validate the effectiveness of our proposed method, we have conducted extensive experiments on two public datasets, and demonstrate superior or comparable performance to state-of-the-art approaches.