K-Space Transformer for Undersampled MRI Reconstruction

Ziheng Zhao (Shanghai Jiao Tong University),* Tianjiao Zhang (Shanghai Jiao Tong University), Weidi Xie (Shanghai Jiao Tong University), Yan-Feng Wang (Cooperative medianet innovation center of Shanghai Jiao Tong University), Ya Zhang (Cooperative Medianet Innovation Center, Shang hai Jiao Tong University)
The 33rd British Machine Vision Conference


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 reconstruc- tion 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.



author    = {Ziheng Zhao and Tianjiao Zhang and Weidi Xie and Yan-Feng Wang and Ya Zhang},
title     = {K-Space Transformer for Undersampled MRI Reconstruction},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {https://bmvc2022.mpi-inf.mpg.de/0473.pdf}

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