Quantifying the cerebral metabolic rate of oxygen by combining diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy

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Diffuse correlation spectroscopy (DCS) is a promising technique for brain monitoring as it can provide a continuous signal that is directly related to cerebral blood flow (CBF); however, signal contamination from extracerebral tissue can cause flow underestimations. The goal of this study was to investigate whether a multi-layered (ML) model that accounts for light propagation through the different tissue layers could successfully separate scalp and brain flow when applied to DCS data acquired at multiple source-detector distances. The method was first validated with phantom experiments. Next, experiments were conducted in a pig model of the adult head with a mean extracerebral tissue thickness of 9.8 ± 0.4 mm. Reductions in CBF were measured by ML DCS and computed tomography perfusion for validation; excellent agreement was observed by a mean difference of 1.2 ± 4.6% (CI 95% : −31.1 and 28.6) between the two modalities, which was not significantly different.

Section: Resultsmentioning

confidence: 99%

Assessment of a multi-layered diffuse correlation spectroscopy method for monitoring cerebral blood flow in adults

Verdecchia

Diop

Lee

et al. 2016

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“…For example, blood flow can be measured by manipulating arterial oxygenation saturation or injecting a light-absorbing contrast agent [4][5][6]; however, these techniques only enable single time-point measurements. An alternative approach that provides continuous blood flow monitoring is diffuse correlation spectroscopy (DCS) [3].…”

Section: Introductionmentioning

confidence: 99%

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

Verdecchia¹,

Diop²,

Morrison³

et al. 2015

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Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique capable of monitoring tissue perfusion. The normalized temporal intensity autocorrelation function generated by DCS is typically characterized by assuming that the movement of erythrocytes can be modeled as a Brownian diffusion-like process instead of by the expected random flow model. Recently, a hybrid model, referred to as the hydrodynamic diffusion model, was proposed, which combines the random and Brownian flow models. The purpose of this study was to investigate the best model to describe autocorrelation functions acquired directly on the brain in order to avoid confounding effects of extracerebral tissues. Data were acquired from 11 pigs during normocapnia and hypocapnia, and flow changes were verified by computed tomography perfusion (CTP). The hydrodynamic diffusion model was found to provide the best fit to the autocorrelation functions; however, no significant difference for relative flow changes measured by the Brownian and hydrodynamic diffusion models was observed.

“…Considering the current lack of adequate methods for RA treatment monitoring, this technique could play a significant role in the management of this debilitating disease. Note that there are other optical techniques-diffuse correlation spectroscopy (DCS) and changes in Hb and HbO 2 following venous occlusion-that can noninvasively measure deep tissue perfusion [54][55][56][57]. However, while it has been shown that the venous occlusion method can quantify blood flow in muscle [57], its ability to quantify joint perfusion has yet to be tested.…”

Section: Discussionmentioning

confidence: 99%

Joint blood flow is more sensitive to inflammatory arthritis than oxyhemoglobin, deoxyhemoglobin, and oxygen saturation

Rajaram

Ioussoufovitch

Morrison

et al. 2016

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Joint hypoxia plays a central role in the progression and perpetuation of rheumatoid arthritis (RA). Thus, optical techniques that can measure surrogate markers of hypoxia such as blood flow, oxyhemoglobin, deoxyhemoglobin, and oxygen saturation are being developed to monitor RA. The purpose of the current study was to compare the sensitivity of these physiological parameters to arthritis. Experiments were conducted in a rabbit model of RA and the results revealed that joint blood flow was the most sensitive to arthritis and could detect a statistically significant difference (p<0.05, power = 0.8) between inflamed and healthy joints with a sample size of only four subjects. Considering that this a quantitative technique, the high sensitivity to arthritis suggests that joint perfusion has the potential to become a potent tool for monitoring disease progression and treatment response in RA.