TY  - JOUR
AU  - Huang, Shu-Ying
AU  - Huang, Xue-Ying
AU  - Yang, Yong
AU  - Wang, Xiao-Zheng
AU  - Ren, Heng
AU  - Niu, Yu-Fan
PY  - 2026
TI  - MSCFN: Multiscale Spatial-Frequency Collaborative Fusion Network for Multicontrast Magnetic Resonance Imaging Super-Resolution
JO  - Journal of Computer Science and Technology
SN  - 1000-9000
SP  - 936
EP  - 946
VL  - 41
IS  - 3
AB  - Magnetic resonance imaging (MRI) can generate images with varying contrasts and acquisition times depending on imaging parameters. Utilizing a high-resolution contrast with a short acquisition time as a reference for the super-resolution (SR) of low-resolution contrasts with long acquisition times is effective for the rapid acquisition of high-quality images. However, existing methods mainly process features in the spatial domain, and overlook potential features in the frequency domain. This paper proposes Multiscale Spatial-Frequency Collaborative Fusion Network (MSCFN), which jointly leverages information in the spatial and frequency domains for SR. A global-local fusion block optimizes global structural features and local texture details at different scales, and an adaptive low-high frequency fusion module utilizes the complementary nature of multiple contrasts to decompose reference images into high- and low-frequency components and adaptively fuse them to enhance feature integration. Experimental results indicate that MSCFN outperforms existing multicontrast MRI SR methods. The code is publicly available at https://github.com/crystal177/MSCFN.
UR  - https://doi.org/10.1007/s11390-026-5762-3
DO  - 10.1007/s11390-026-5762-3