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.
@article{arxiv.2206.06947,
title = {K-Space Transformer for Undersampled MRI Reconstruction},
author = {Ziheng Zhao and Tianjiao Zhang and Weidi Xie and Yanfeng Wang and Ya Zhang},
journal= {arXiv preprint arXiv:2206.06947},
year = {2022}
}