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Open AccessArticle

Diffuse Correlation Spectroscopy at Short Source-Detector Separations: Simulations, Experiments and Theoretical Modeling

Miami University, Department of Physics, 500 E Spring Street, Oxford, OH 45056, USA
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Appl. Sci. 2019, 9(15), 3047; https://doi.org/10.3390/app9153047
Received: 5 June 2019 / Revised: 15 July 2019 / Accepted: 25 July 2019 / Published: 28 July 2019
Diffuse correlation spectroscopy (DCS) has widely been used as a non-invasive optical technique to measure tissue perfusion in vivo. DCS measurements are quantified to yield information about moving scatterers using photon diffusion theory and are therefore obtained at long source-detector separations (SDS). However, short SDS DCS could be used for measuring perfusion in small animal models or endoscopically in clinical studies. Here, we investigate the errors in analytically retrieved flow coefficients from simulated and experimental data acquired at short SDS. Monte Carlo (MC) simulations of photon correlation transport was programmed to simulate DCS measurements and used to (a) examine the accuracy and validity of theoretical analyses, and (b) model experimental measurements made on phantoms at short SDS. Experiments consisted of measurements from a series of optical phantoms containing an embedded flow channel. Both the fluid flow rate and depth of the flow channel from the liquid surface were varied. Inputs to MC simulations required to model experiments were obtained from corrected theoretical analyses. Results show that the widely used theoretical DCS model is robust for quantifying relative changes in flow. We also show that retrieved flow coefficients at short SDS can be scaled to retrieve absolute values via MC simulations. View Full-Text
Keywords: turbid media; dynamic light scattering; radiative transport; tissue phantoms turbid media; dynamic light scattering; radiative transport; tissue phantoms
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Vishwanath, K.; Zanfardino, S. Diffuse Correlation Spectroscopy at Short Source-Detector Separations: Simulations, Experiments and Theoretical Modeling. Appl. Sci. 2019, 9, 3047.

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