Recent advancements in single-photon avalanche diode (SPAD) cameras have shown great promise for enhancing diffuse correlation spectroscopy (DCS), which assesses blood flow to the brain—a key factor in understanding brain function and potential stroke risks. Traditional DCS technologies rely on analyzing speckle patterns generated by laser light scattered off the scalp, but they often capture only a single speckle at a time. The emergence of SPAD cameras enables the simultaneous capture of multiple speckles, significantly improving sensitivity.
However, the high data rates produced by these cameras pose challenges for data transfer and processing, limiting the application of SPAD technology in high-resolution imaging. To overcome this, a team led by Professor Robert K. Henderson from the University of Edinburgh has developed an innovative data compression scheme using field-programmable gate arrays (FPGAs). This approach allows real-time computation of autocorrelations from a 192×128 pixel SPAD sensor array—named Quanticam—shifting the computing load from host systems to the FPGA.
This method achieved a signal-to-noise ratio improvement of 110 times compared to single-speckle DCS systems, enhancing multispeckle DCS techniques significantly and broadening their potential applications in biomedical research.
