Spherical Harmonics Representation Learning for High-Fidelity and Generalizable Super-Resolution in Diffusion MRI
The proposed method can effectively improve the resolution of dMRI data without increasing the acquisition time, providing new possibilities for future clinical applications.
Authors
- Jian Cheng (ORCID: https://orcid.org/0000-0003-1435-673X)
- Ruoyou Wu (ORCID: https://orcid.org/0000-0002-4006-9243)
- Xinrui Ma
- Hua Guo (ORCID: https://orcid.org/0000-0002-0482-1493)
- Cheng Jung Li (ORCID: https://orcid.org/0000-0001-6110-8099)
- Shanshan Wang (ORCID: https://orcid.org/0000-0002-0575-6523)
- Yong Liang (ORCID: https://orcid.org/0000-0003-2212-6694)
- Wenxin Fan
- Juan Zou (ORCID: https://orcid.org/0009-0002-1283-7150)
Institutions
- Chinese Academy of Sciences (CN)
- Laboratoire d’Imagerie Biomédicale (FR)
- Peng Cheng Laboratory (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Artificial Intelligence in Medicine (Canada) (CA)
- Changsha University of Science and Technology (CN)
- Beihang University (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1109/tbme.2025.3607105
- Citations
- 2
- Primary Topic
- Advanced Neuroimaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.72