Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution
| Title: | Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution |
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| Authors: | Yang, Zhiyuan, Zhang, Bo, Zeng, Zhiqiang, Yeo, Si Yong |
| Publication Year: | 2024 |
| Subject Terms: | Electrical Engineering and Systems Science - Image and Video Processing |
| More Details: | 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. Comment: submitted to ICPR2024 |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2407.05307 |
| Accession Number: | edsarx.2407.05307 |
| Database: | arXiv |
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