Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution

TL;DR

This paper introduces ECFNet, an advanced MRI super-resolution network that leverages cross-scale feature fusion and structure priors to produce sharper, more detailed images, outperforming existing methods.

Contribution

The paper proposes a novel edge-guided, cross-scale feature fusion network with deformable convolution and cross-attention transformer for improved MRI super-resolution.

Findings

Achieves state-of-the-art results on IXI and BraTS2020 datasets.

Robust performance across different super-resolution scales.

Enhances image sharpness with structure information collaboration.

Abstract

In recent years, MRI super-resolution techniques have achieved great success, especially multi-contrast methods that extract texture information from reference images to guide the super-resolution reconstruction. However, current methods primarily focus on texture similarities at the same scale, neglecting cross-scale similarities that provide comprehensive information. Moreover, the misalignment between features of different scales impedes effective aggregation of information flow. To address the limitations, we propose a novel edge-guided and cross-scale feature fusion network, namely ECFNet. Specifically, we develop a pipeline consisting of the deformable convolution and the cross-attention transformer to align features of different scales. The cross-scale fusion strategy fully integrates the texture information from different scales, significantly enhancing the super-resolution. In addition, a novel structure information collaboration module is developed to guide the super-resolution reconstruction with implicit structure priors. The structure information enables the network to focus on high-frequency components of the image, resulting in sharper details. Extensive experiments on the IXI and BraTS2020 datasets demonstrate that our method achieves state-of-the-art performance compared to other multi-contrast MRI super-resolution methods, and our method is robust in terms of different super-resolution scales. We would like to release our code and pre-trained model after the paper is accepted.