Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910575554985984 |
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| author | Yang, Zhiyuan Zhang, Bo Zeng, Zhiqiang Yeo, Si Yong |
| author_facet | Yang, Zhiyuan Zhang, Bo Zeng, Zhiqiang Yeo, Si Yong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_05307 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution Yang, Zhiyuan Zhang, Bo Zeng, Zhiqiang Yeo, Si Yong Image and Video Processing 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. |
| title | Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2407.05307 |