Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution

TL;DR

This paper introduces CANM-Net, a novel multi-contrast MRI super-resolution network that leverages compound self-attention and neighbor matching to improve image quality, robustness, and clinical applicability over existing methods.

Contribution

The paper proposes a new network architecture with compound-attention and neighbor matching modules specifically designed for multi-contrast MRI super-resolution, addressing limitations of previous approaches.

Findings

Outperforms state-of-the-art methods on multiple datasets

Maintains good performance with imperfect image registration

Demonstrates robustness in clinical scenarios

Abstract

Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications. By utilizing the complementary information among different modalities, multi-contrast super-resolution (SR) of MRI can achieve better results than single-image super-resolution. However, existing methods of multi-contrast MRI SR have the following shortcomings that may limit their performance: First, existing methods either simply concatenate the reference and degraded features or exploit global feature-matching between them, which are unsuitable for multi-contrast MRI SR. Second, although many recent methods employ transformers to capture long-range dependencies in the spatial dimension, they neglect that self-attention in the channel dimension is also important for low-level vision tasks. To address these shortcomings, we proposed a novel network architecture with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI SR: The compound self-attention mechanism effectively captures the dependencies in both spatial and channel dimension; the neighborhood-based feature-matching modules are exploited to match degraded features and adjacent reference features and then fuse them to obtain the high-quality images. We conduct experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets. The CANM-Net outperforms state-of-the-art approaches in both retrospective and prospective experiments. Moreover, the robustness study in our work shows that the CANM-Net still achieves good performance when the reference and degraded images are imperfectly registered, proving good potential in clinical applications.