MR-guided graph learning of 18F-florbetapir PET enables accurate and interpretable Alzheimer’s disease staging
The proposed graph-based learning framework effectively integrates PET and MRI features for accurate AD stage distinction, showing significant promise for early detection and facilitating timely intervention.
Authors
- Tao Sun (ORCID: https://orcid.org/0000-0002-6734-6837)
- Qiankun Zuo (ORCID: https://orcid.org/0009-0002-8487-5762)
- Shuqiang Wang (ORCID: https://orcid.org/0000-0003-1119-320X)
- Ye Li (ORCID: https://orcid.org/0000-0002-2699-6630)
- Lijuan Chen (ORCID: https://orcid.org/0000-0002-8676-2377)
- Xinyi Chen (ORCID: https://orcid.org/0000-0003-3321-2483)
- Meiyun Wang
- Dong Liang (ORCID: https://orcid.org/0000-0001-6257-0875)
- Weiheng Yao
Institutions
- Chinese Academy of Sciences (CN)
- Southern University of Science and Technology (CN)
- Henan Provincial People's Hospital (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Zhengzhou People's Hospital (CN)
Publication Details
- Journal
- NeuroImage
- Published
- 2025-10-10
- DOI
- https://doi.org/10.1016/j.neuroimage.2025.121510
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00