Assessment of the best flow model to characterize diffuse correlation spectroscopy data acquired directly on the brain

Verdecchia, Kyle; Diop, Mamadou; Morrison, Laura; Lee, Ting Yim; Lawrence, Keith St.

doi:10.1364/boe.6.004288

Cited by 36 publications

(33 citation statements)

References 33 publications

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“…Note that Eq. (3) can be expanded to use the Langevin formulation for diffusion, 13,19,20 accounting for the diffusive motion velocity vector randomization that occurs at short-time delays.…”

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confidence: 99%

Establishing the diffuse correlation spectroscopy signal relationship with blood flow

et al. 2016

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Abstract. Diffuse correlation spectroscopy (DCS) measurements of blood flow rely on the sensitivity of the temporal autocorrelation function of diffusively scattered light to red blood cell (RBC) mean square displacement (MSD). For RBCs flowing with convective velocity v RBC , the autocorrelation is expected to decay exponentially with ðv RBC τÞ 2 , where τ is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient D shear and an MSD of 6D shear τ. Surprisingly, experimental data primarily reflect diffusive behavior. To provide quantitative estimates of the relative contributions of convective and diffusive movements, we performed Monte Carlo simulations of light scattering through tissue of varying vessel densities. We assumed laminar vessel flow profiles and accounted for shear-induced diffusion effects. In agreement with experimental data, we found that diffusive motion dominates the correlation decay for typical DCS measurement parameters. Furthermore, our model offers a quantitative relationship between the RBC diffusion coefficient and absolute tissue blood flow. We thus offer, for the first time, theoretical support for the empirically accepted ability of the DCS blood flow index (BF i ) to quantify tissue perfusion. We find BF i to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Establishing the diffuse correlation spectroscopy signal relationship with blood flow

et al. 2016

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“…However, neither of the two models [Eqs. (9) and (10)] perfectly fits the data. This can be seen, e.g., in the figures appearing in Refs.…”

Section: Introductionmentioning

confidence: 52%

Derivation of the correlation diffusion equation with static background and analytical solutions

et al. 2017

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A new correlation diffusion equation has been derived from a correlation transport equation allowing one to take into account the presence of moving scatterers and static background. Solutions for the reflectance from a semi-infinite medium have been obtained (point-like and ring detectors). The solutions have been tested by comparisons with "gold standard" Monte Carlo (MC) simulations. These formulas suitably describe the electric field autocorrelation function, for Brownian or random movement of the scatterers, even in the case where the probability for a photon to interact with a moving scatterer is very low. The proposed analytical models and the MC simulations show that the "classical" model, often used in diffuse correlation spectroscopy, underestimates the normalized field autocorrelation function for increasing correlation times.

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“…(1), (11) and (12). It should be noted that in recent years, more accurate models for describing the dynamics in tissue has been investigated [10, 11,23]. For example, the hydrodynamic diffusion model which accounts for not only Brownian diffuse motion, but also ballistic motion at short lag times.…”

Section: Discussionmentioning

confidence: 99%

Analytical models for time-domain diffuse correlation spectroscopy for multi-layer and heterogeneous turbid media

Qiu

Poon

et al. 2017

Biomed. Opt. Express

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Abstract:A novel approach for time-domain diffuse correlation spectroscopy (TD-DCS) has been recently proposed, which has the unique advantage by simultaneous measurements of optical and dynamical properties in a scattering medium. In this study, analytical models for calculating the time-resolved electric-field autocorrelation function is presented for a multilayer turbid sample, as well as a semi-infinite medium embedded with a small dynamic heterogeneity. To verify the analytical models, we used Monte Carlo simulations, which demonstrated that the theoretical prediction for the time-resolved autocorrelation function was highly consistent with the Monte Carlo simulation, validating the proposed analytical models. Using these analytical models, we also showed that TD-DCS has a higher sensitivity compared to conventional continuous-wave (CW) DCS for detecting the deeper dynamics. The presented analytical models and simulations can be utilized for quantification of optical and dynamical properties from future TD-DCS experimental data as well as for optimization of the experimental design to achieve maximum contrast for deep tissue dynamics.

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Assessment of the best flow model to characterize diffuse correlation spectroscopy data acquired directly on the brain

Cited by 36 publications

References 33 publications

Establishing the diffuse correlation spectroscopy signal relationship with blood flow

Establishing the diffuse correlation spectroscopy signal relationship with blood flow

Derivation of the correlation diffusion equation with static background and analytical solutions

Analytical models for time-domain diffuse correlation spectroscopy for multi-layer and heterogeneous turbid media

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