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Magnetic resonance imaging plays an essential role in clinical diagnosis byacquiring the structural information of biological tissue. Recently, manymulti-contrast MRI super-resolution networks achieve good effects. However,most studies ignore the impact of the inappropriate foreground scale and patchsize of multi-contrast MRI, which probably leads to inappropriate featurealignment. To tackle this problem, we propose the Flexible AlignmentSuper-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.The Flexible Alignment module of FASR-Net consists of two modules for featurealignment. (1) The Single-Multi Pyramid Alignment(S-A) module solves thesituation where low-resolution (LR) images and reference (Ref) images havedifferent scales. (2) The Multi-Multi Pyramid Alignment(M-A) module solves thesituation where LR and Ref images have the same scale. Besides, we propose theCross-Hierarchical Progressive Fusion (CHPF) module aiming at fusing thefeatures effectively, further improving the image quality. Compared with otherstate-of-the-art methods, FASR-net achieves the most competitive results onFastMRI and IXI datasets. Our code will be available at\href{https://github.com/yimingliu123/FASR-Net}{https://github.com/yimingliu123/FASR-Net}.

Flexible Alignment Super-Resolution Network for Multi-Contrast MRI