@article{Huang2026, 
author = {Shu-Ying Huang and Xue-Ying Huang and Yong Yang and Xiao-Zheng Wang and Heng Ren and Yu-Fan Niu},
title = {MSCFN: Multiscale Spatial-Frequency Collaborative Fusion Network for Multicontrast Magnetic Resonance Imaging Super-Resolution},
year = {2026},
journal = {Journal of Computer Science and Technology},
volume = {41},
number = {3},
pages = {936-946},
keywords = {magnetic resonance imaging, super-resolution, multicontrast, collaborative learning},
url = {https://www.sciopen.com/article/10.1007/s11390-026-5762-3},
doi = {10.1007/s11390-026-5762-3},
abstract = {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.}
}