K-Space Transformer for Undersampled MRI Reconstruction
This addresses the problem of faster and more accurate MRI scans for medical imaging, but it is incremental as it builds on existing Transformer and reconstruction techniques.
The paper tackles undersampled MRI reconstruction by proposing a Transformer-based framework that directly processes k-space signals, overcoming the grid limitations of ConvNets, and achieves superior or comparable performance to state-of-the-art methods on two public datasets.
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.