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    Area of Science:

    • Biomedical Optics
    • Photonics
    • Physiology

    Background:

    • Diffusing wave spectroscopy (DWS) and diffuse correlation spectroscopy (DCS) are optical techniques used to measure blood flow index (BFI) in biological tissues.
    • These methods rely on analyzing multiply scattered light, typically assuming red blood cell (RBC) motion follows Brownian dynamics.
    • However, the primary physiological role of RBCs is advection (flow), creating a discrepancy with the Brownian motion assumption.

    Purpose of the Study:

    • To critically evaluate the validity of the cumulant approximation, a key assumption in DWS and DCS for BFI assessment.
    • To identify specific conditions in realistic tissue models where the cumulant approximation is inaccurate.
    • To propose a more accurate model for assessing random flow dynamics from scattered light measurements.

    Main Methods:

    • Theoretical examination of the cumulant approximation used in DWS and DCS.
    • Development of a precise criterion to determine the validity of the cumulant approximation.
    • Simulation and analysis using realistic biological tissue models.

    Main Results:

    • Identified conditions in physiologically relevant scenarios where the cumulant approximation is invalidated.
    • Demonstrated that the first cumulant (random flow) and second cumulant (Brownian motion) terms can cancel each other out.
    • Found that the common practice of assuming pure Brownian motion and the first cumulant approximation can yield seemingly good agreement with data due to compensating errors.

    Conclusions:

    • The standard cumulant approximation in DWS/DCS is insufficient for accurately assessing random flow in biological tissues.
    • Apparent agreement with data can arise from the cancellation of errors from incorrect assumptions about RBC motion.
    • Accurate assessment of random flow requires moving beyond the cumulant approximation, necessitating the development and application of more precise models.