Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique capable of monitoring tissue perfusion changes, particularly in the brain. 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 the expected random flow model. Carp et al. [Biomedical Optics Express, 2011] proposed a hybrid model, referred to as the hydrodynamic diffusion model, to capture both the random ballistic and diffusive nature of erythrocyte motion. The purpose of this study was to compare how well the Brownian diffusion and the hydrodynamic diffusion models characterized DCS data acquired directly on the brain, avoiding the confounding effects of scalp and skull. Data were acquired from seven pigs during normocapnia (39.9 ± 0.7 mmHg) and hypocapnia (22.1 ± 1.6 mmHg) with the DCS fibers placed 7 mm apart, directly on the cerebral cortex. The hydrodynamic diffusion model was found to provide a consistently better fit to the autocorrelation functions compared to the Brownian diffusion model and was less sensitive to the chosen start and end time points used in the fitting. However, the decrease in cerebral blood flow from normocapnia to hypocapnia determined was similar for the two models (-42.6 ± 8.6 % for the Brownian model and -42.2 ± 10.2 % for the hydrodynamic model), suggesting that the latter is reasonable for monitoring flow changes.
The combination of near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) offers the ability to
provide real-time monitoring of cerebral oxygenation, blood flow and oxygen consumption. However, measuring these
parameters accurately requires depth-sensitive techniques that can remove the effects of signal contamination from
extracerebral tissues. Towards this goal, we developed and characterized a hybrid DCS/time-resolved (TR)-NIRS system.
Both systems acquire data at three source-detector distances (SDD: 7, 20 and 30 mm) to provide depth sensitivity. The
TR-NIRS system uses three pulsed lasers (760, 810, and 830 nm) to quantify tissue optical properties, and DCS uses one
continuous-wave, long coherence length (>5 m) laser (785 nm) for blood flow monitoring. The stability of the TR-NIRS
system was characterized by continuously measuring the instrument response function (IRF) for four hours, and a warmup
period of two hours was required to reduce the coefficient of variation of the extracted optical properties to < 2%. The
errors in the measured optical properties were <10% at SDDs of 20 and 30 mm; however, the error at 7 mm was greater
due to the effects of the IRF. The number of DCS detectors at each SDD and the minimum count-rate (20 kHz per detector
resulting in <10% uncertainty in the extracted blood flow index) were optimized using a homogenous phantom. The depth
sensitivity was assessed using a two-layer phantom, with the flow rate in the bottom layer altered to mimic cerebral blood
flow.
The purpose of this study was to develop a dynamic contrast-enhanced (DCE) near-infrared spectroscopy (NIRS) technique to characterize tumor physiology. Dynamic data were acquired using two contrast agents of different molecular weights, indocyanine green (ICG) and IRDye 800CW carboxylate (IRDcxb). The DCE curves were analyzed using a kinetic model capable of extracting estimates of tumor blood flow (F), capillary transit time (tc) and the amount of dye that leaked into the extravascular space (EVS) – characterized by the extraction fraction (E). Data were acquired from five nude rats with tumor xenografts (>10mm) implanted in the neck. Four DCE-NIR datasets (two from each contrast agent) were acquired for each rat. The dye concentration curve in arterial blood, which is required to quantify the model parameters, was measured non-invasively by dye densitometry. A modification to the kinetic model to characterize tc as a distribution of possible values, rather than finite, improved the fit of acquired tumor concentration curves, resulting in more reliable estimates. This modified kinetic model identified a difference between the extracted fraction of IRDcxb, 15 ± 6 %, and ICG, 1.6 ± 0.6 %, in the tumor, which can be explained by the difference in molecular weight: 67 kDa for ICG since it binds to albumin and 1.17 kDa for IRD. This study demonstrates the ability of DCENIRS to quantify tumor physiology. The next step is to adapt this approach with a dual-receptor approach.
Preterm infants are highly susceptible to ischemic brain injury; consequently, continuous bedside monitoring to detect ischemia before irreversible damage occurs would improve patient outcome. In addition to monitoring cerebral blood flow (CBF), assessing the cerebral metabolic rate of oxygen (CMRO 2 ) would be beneficial considering that metabolic thresholds can be used to evaluate tissue viability. The purpose of this study was to demonstrate that changes in absolute CMRO 2 could be measured by combining diffuse correlation spectroscopy (DCS) with time-resolved near-infrared spectroscopy (TR-NIRS). Absolute CBF was determined using bolus-tracking TR-NIRS to calibrate the DCS measurements. Cerebral venous blood oxygenation (SvO 2 ) was determined by multiwavelength TR-NIRS measurements, the accuracy of which was assessed by directly measuring the oxygenation of sagittal sinus blood. In eight newborn piglets, CMRO 2 was manipulated by varying the anesthetics and by injecting sodium cyanide. No significant differences were found between the two sets of SvO 2 measurements obtained by TR-NIRS or sagittal sinus blood samples and the corresponding CMRO 2 measurements. Bland–Altman analysis showed a mean CMRO 2 difference of 0.0268±0.8340 mL O 2 /100 g/min between the two techniques over a range from 0.3 to 4 mL O 2 /100 g/min .
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