E2D-Unroll: efficient equivariant deformable unrolling networks for cardiac cine MRI reconstruction
. E2D-Unroll unifies spatiotemporal deformable convolutions with rotation-equivariant networks to suppress large-scale aliasing artifacts more effectively while maintaining high parameter efficiency and low computational cost, providing a practical solution for accelerated cardiac cine MRI in real-world settings.
Authors
- Shiying Ke
- Zhuoxu Cui
- Qingyong Zhu
- Dong Liang
- Chengbo Wang
- Yuliang Zhu
- Yulin Wang
- Jing Cheng
- Yuanyuan Liu
- Zhaochi Wen
- Jianfeng Ren
Institutions
- University of Nottingham Ningbo China (CN)
- Chinese Academy of Sciences (CN)
- ShanghaiTech University (CN)
- Academy of Mathematics and Systems Science (CN)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- Physics in Medicine and Biology
- Published
- 2026-04-07
- DOI
- https://doi.org/10.1088/1361-6560/ae5c53
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Basic and Applied Basic Research Foundation of Guangdong Province