  Deep Learning (DL) based methods for magnetic resonance (MR) image
reconstruction have been shown to produce superior performance in recent years.
However, these methods either only leverage under-sampled data or require a
paired fully-sampled auxiliary modality to perform multi-modal reconstruction.
Consequently, existing approaches neglect to explore attention mechanisms that
can transfer textures from reference fully-sampled data to under-sampled data
within a single modality, which limits these approaches in challenging cases.
In this paper, we propose a novel Texture Transformer Module (TTM) for
accelerated MRI reconstruction, in which we formulate the under-sampled data
and reference data as queries and keys in a transformer. The TTM facilitates
joint feature learning across under-sampled and reference data, so the feature
correspondences can be discovered by attention and accurate texture features
can be leveraged during reconstruction. Notably, the proposed TTM can be
stacked on prior MRI reconstruction approaches to further improve their
performance. Extensive experiments show that TTM can significantly improve the
performance of several popular DL-based MRI reconstruction methods.

Computer Vision and Pattern Recognition