Abstract
Diffusive correlation spectroscopy (DCS) is an emerging optical technique that measures blood perfusion in deep tissue. In a DCS measurement, temporal changes in the interference pattern of light, which has passed through tissue, are quantified by an autocorrelation function. This autocorrelation function is further parameterized through a non-linear curve fit to a solution to the diffusion equation for coherence transport. The computational load for this non-linear curve fitting is a barrier for deployment of DCS for clinical use, where real-time results, as well as instrument size and simplicity, are important considerations. We have mitigated this computational bottleneck through development of a hardware analyzer for DCS. This analyzer implements the DCS curving fitting algorithm on digital logic circuit using Field Programmable Gate Array (FPGA) technology. The FPGA analyzer is more efficient than a typical software analysis solution. The analyzer module can be easily duplicated for processing multiple channels of DCS data in real-time. We have demonstrated the utility of this analyzer in pre-clinical large animal studies of spinal cord ischemia. In combination with previously described FPGA implementations of auto-correlators, this hardware analyzer can provide a complete device-on-a-chip solution for DCS signal processing. Such a component will enable new DCS applications demanding mobility and real-time processing.