Cerebral blood flow monitoring using a deep learning implementation of the two-layer diffuse correlation spectroscopy analytical model with a 512 × 512 SPAD array
This study demonstrates that integrating DL with the two-layer DCS analytical model enables accurate, real-time cerebral perfusion monitoring without sacrificing depth sensitivity. The proposed method enhances CBFi sensitivity and recovery accuracy, supporting future deployment in bedside neuro-monitoring applications.
Authors
- Ahmet T. Erdogan (ORCID: https://orcid.org/0000-0003-2451-9395)
- Zhenya Zang (ORCID: https://orcid.org/0000-0003-4952-6727)
- Neil Finlayson (ORCID: https://orcid.org/0000-0003-1598-9563)
- Chenxu Li (ORCID: https://orcid.org/0000-0003-1359-5798)
- Yuanyuan Hua (ORCID: https://orcid.org/0000-0002-1538-0953)
- Quan Wang (ORCID: https://orcid.org/0000-0001-6913-8604)
- David Li (ORCID: https://orcid.org/0000-0002-6401-4263)
- Robert K. Henderson (ORCID: https://orcid.org/0000-0002-0398-7520)
- Mingliang Pan (ORCID: https://orcid.org/0009-0001-9732-8963)
- Alan Mollins
- Yuanzhe Zhang (ORCID: https://orcid.org/0009-0004-5107-3856)
Institutions
- University of Strathclyde (GB)
- National Microelectronics Institute (GB)
Publication Details
- Journal
- Neurophotonics
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1117/1.nph.12.3.035008
- Citations
- 2
- Primary Topic
- Optical Imaging and Spectroscopy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- University of Strathclyde
- QuantIC
- Meta
- China Scholarship Council
- Engineering and Physical Sciences Research Council