Diffuse correlation spectroscopy (DCS) is an evolving optical modality that provides a fast, non-invasive, portable alternative to costly medical diagnostics in quantifying the blood flow. DCS involves monitoring the temporal statistics of scattered light from the sample, upon illumination by a coherent source. The detected signal is related to RBC motion, and blood flow is derived combining a model for photon propagation through the target tissue with the experimental observations. Conventionally, blood flow index (BFI) is calculated for long source-detector separations (SDS), that quantifies the blood flow from deeper tissue layers. Reduced SDS is required in measuring perfusion for immediate tissue subsurface, which is important in monitoring related changes. Here, we investigate the application of short-range DCS for skin blood flow monitoring and demonstrate the capability in determining BFI from immediate subsurface. Bilayer skin tissue with embedded micro vessels was modelled using finite element methods (FEM). Time correlated light diffusion equation was solved for this tissue geometry, with a point source illumination and autocorrelation plots were generated. Our in-silico analysis illustrates the change in BFI for varying blood flow rate and capillary depth from the skin surface. The work performed here, after experimental validation, is expected to have the potential for non-invasive quantitative blood flow assessment and can aid in diagnosing related skin disorders.
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