arXiv Open Access 2022

K-Space Transformer for Undersampled MRI Reconstruction

Ziheng Zhao Tianjiao Zhang Weidi Xie Yanfeng Wang Ya Zhang

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Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (5)

Ziheng Zhao

Tianjiao Zhang

Weidi Xie

Yanfeng Wang

Ya Zhang

Format Sitasi

APA MLA BibTeX

Zhao, Z., Zhang, T., Xie, W., Wang, Y., Zhang, Y. (2022). K-Space Transformer for Undersampled MRI Reconstruction. https://arxiv.org/abs/2206.06947

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓