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This paper considers the problem of undersampled MRI reconstruction. Wepropose a novel Transformer-based framework for directly processing signal ink-space, going beyond the limitation of regular grids as ConvNets do. We adoptan implicit representation of k-space spectrogram, treating spatial coordinatesas inputs, and dynamically query the sparsely sampled points to reconstruct thespectrogram, i.e. learning the inductive bias in k-space. To strike a balancebetween computational cost and reconstruction quality, we build the decoderwith hierarchical structure to generate low-resolution and high-resolutionoutputs respectively. To validate the effectiveness of our proposed method, wehave conducted extensive experiments on two public datasets, and demonstratesuperior or comparable performance to state-of-the-art approaches.

K-Space Transformer for Undersampled MRI Reconstruction