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Updated: May 12, 2026
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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
Published on: May 27, 2020
Characterization of a fiber-coupled SPAD camera system for deep-tissue blood-flow measurement using diffuse
Christopher H Moore1, Michael A Wayne2, Arin C Ulku2
1Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
Biomedical Optics Express
|May 11, 2026
Summary
This study introduces a new SPAD camera system for diffuse correlation spectroscopy (DCS), enhancing signal-to-noise ratio for deep brain blood flow measurement. This scalable technology improves noninvasive monitoring of cerebral blood flow.
Area of Science:
- Biomedical Optics
- Medical Imaging
- Photonics
Background:
- Diffuse correlation spectroscopy (DCS) measures blood flow, particularly cerebral blood flow, but faces signal-to-noise ratio (SNR) limitations in deep tissues.
- Conventional DCS requires multiple detectors, hindering scalability for deep-brain imaging with large source-detector separations.
Purpose of the Study:
- To demonstrate a novel single-photon avalanche diode (SPAD) array system for enhanced DCS measurements.
- To validate the performance of a fiber-coupled SPAD camera against conventional DCS systems.
Main Methods:
- Utilized a 250x500 SPAD array (SwissSPAD3) coupled with a custom FPGA design for parallel detection.
- Validated the SPAD-based DCS system using two-layer liquid phantoms and human subjects.
- Compared performance against a lab-standard continuous-wave DCS system.
Main Results:
- Achieved significant increases in SNR compared to conventional DCS systems.
- Demonstrated robust blood-flow tracking at source-detector separations up to 3.25 cm.
- Validated the SPAD camera system's efficacy in both phantom and human measurements.
Conclusions:
- SPAD-based parallel detection offers a scalable approach to improve deep-tissue DCS performance.
- The developed system enhances noninvasive blood flow monitoring, especially for cerebral applications.
- This technology holds promise for advancing noninvasive neuroimaging and diagnostics.
