# Reference-guided MRI super-resolution with dual attention aggregation network

## TL;DR

This paper introduces a new MRI super-resolution method that uses reference images to improve image quality and preserve anatomical details.

## Contribution

The novel DAASR framework uses dual attention mechanisms to balance structural modeling and reference integration for MRI super-resolution.

## Key findings

- DAASR outperforms existing methods on public and clinical MRI datasets in terms of PSNR and SSIM.
- The method achieves better structural similarity and visual consistency in enhanced MRI images.
- Reference-guided super-resolution preserves fine anatomical structures and tissue boundaries more effectively.

## Abstract

Magnetic resonance imaging (MRI) super-resolution aims to enhance spatial resolution from low-resolution acquisitions while preserving anatomically meaningful structures. Conventional single-image super-resolution methods are fundamentally limited by the lack of high-frequency information in the input and often fail to recover fine anatomical details under large upscaling factors. In many MRI scenarios, additional reference images acquired under similar imaging protocols are naturally available and can provide complementary structural information, yet effectively leveraging such references without introducing anatomically inconsistent artifacts remains challenging. In this work, we propose a reference-guided MRI super-resolution framework, termed Dual Attention Aggregation Super-Resolution (DAASR), which explicitly balances structural modeling and controlled reference integration in a hierarchical manner. DAASR employs a channel-wise attention mechanism to reinforce global anatomical coherence in low-resolution features and a structure-aware alignment strategy to selectively incorporate consistent reference information while suppressing unreliable transfers. Extensive experiments on a public MRI benchmark and a clinical brain MRI dataset demonstrate that the proposed method consistently outperforms state-of-the-art MRI super-resolution approaches across multiple scaling factors in terms of PSNR and SSIM. The proposed DAASR further achieves improved structural similarity and visual consistency, indicating better preservation of fine anatomical structures and tissue boundaries.