Deep-learning–enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology
Deep-learning-enabled spatial frequency domain imaging and SFDI-net may offer insights into the cardiorespiratory system and have promising clinical utility for disease diagnosis, surveillance, and therapeutic assessment. Future hardware and software advancements will bring SFDI-net to clinical practice.
Authors
- Yansen Hu
- G. S. Huang (ORCID: https://orcid.org/0000-0002-7510-3181)
- Yifan Ge
- C. P. Shen (ORCID: https://orcid.org/0000-0002-9012-4618)
- Jianmin Yang (ORCID: https://orcid.org/0009-0007-5377-8026)
- Zhineng Xie
- Min Xu (ORCID: https://orcid.org/0000-0001-8187-7637)
- Xiafei Qian
- Weihao Lin (ORCID: https://orcid.org/0000-0002-5344-8955)
- Xin Jin (ORCID: https://orcid.org/0000-0002-2169-7033)
Institutions
- Sun Yat-sen University (CN)
- Wenzhou Medical University (CN)
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
- Eighth Affiliated Hospital of Sun Yat-sen University
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2025-04-20
- DOI
- https://doi.org/10.1117/1.jbo.30.4.046008
- Primary Topic
- Optical Imaging and Spectroscopy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Wenzhou Municipal Science and Technology Bureau
- Department of Education of Zhejiang Province
- Natural Science Foundation of Zhejiang Province