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Flexible Alignment Super-Resolution Network for Multi-Contrast MRI

arXiv 2210.03460 ↗First posted 7 Oct 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

Magnetic resonance imaging plays an essential role in clinical diagnosis by acquiring the structural information of biological tissue. Recently, many multi-contrast MRI super-resolution networks achieve good effects. However, most studies ignore the impact of the inappropriate foreground scale and patch size of multi-contrast MRI, which probably leads to inappropriate feature alignment. To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution. The Flexible Alignment module of FASR-Net consists of two modules for feature alignment. (1) The Single-Multi Pyramid Alignment(S-A) module solves the situation where low-resolution (LR) images and reference (Ref) images have different scales. (2) The Multi-Multi Pyramid Alignment(M-A) module solves the situation where LR and Ref images have the same scale. Besides, we propose the Cross-Hierarchical Progressive Fusion (CHPF) module aiming at fusing the features effectively, further improving the image quality. Compared with other state-of-the-art methods, FASR-net achieves the most competitive results on FastMRI and IXI datasets. Our code will be available at hrefhttps://github.com/yimingliu123/FASR-Nethttps://github.com/yimingliu123/FASR-Net.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 38 of its 47 references that are in the map. First posted on arXiv 7 Oct 2022.

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References · 38

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26 more
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