Abstract

Diffuse correlation spectroscopy (DCS) is an optical modality used to measure an index of blood flow in biological tissue. This blood flow index depends on both the red blood cell flow rate and density (i.e., hematocrit), although the functional form of hematocrit dependence is not well delineated. Herein, we develop and validate a novel tissue-simulating phantom containing hundreds of microchannels to investigate the influence of hematocrit on blood flow index. For a fixed flow rate, we demonstrate a significant inverse relationship between hematocrit and blood flow index that must be accounted for to accurately estimate blood flow under anemic conditions.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Diffuse correlation spectroscopy (DCS) is a well-validated optical modality used to non-invasively measure blood flow in biological tissue [1,2]. The advantages of DCS over other clinically-available perfusion modalities, such as MRI, PET, or transcranial Doppler ultrasound, include a high temporal resolution (>20 Hz), relatively low cost (< $\$$50k), continuous bedside monitoring capabilities, and sensitivity to the microvasculature. The technique has shown promise in a wide array of clinical applications, including stroke [36], traumatic brain injury [710], and cardiac surgery [1117].

In DCS, the tissue is illuminated with a long-coherence length laser operating in the near-infrared. Light scattering off of moving red blood cells leads to temporal fluctuations in reflected light intensity measured by a remotely located detector on the tissue surface. These intensity fluctuations are characterized using an intensity autocorrelation function that is fit to simple analytical models to extract an index of blood flow, BFI (cm2/s) [1820]. In general, BFI reflects both the average flow and density of red blood cells in the interrogated tissue volume [21]. While numerous clinical and preclinical validation studies have experimentally confirmed that BFI correlates with blood flow measured by other gold standard perfusion modalities [2], the dependence of BFI on the density of red blood cells (i.e., hematocrit) has received considerably less attention.

The influence of hematocrit on BFI is an important factor that likely needs to be accounted for both in healthy populations as well as in conditions associated with anemia or polycythemia. Hematocrit levels within the normal population span a broad range and vary with age and between sexes. For example, normal hematocrit levels range from 38–52% for men and 35–44% for women. For women, hematocrit levels can vary significantly over the course of the menstrual cycle [22]. Moreover, for patients with blood disorders, especially chronic anemia, variations in hematocrit are a well-known phenomenon with significant adverse consequences that affect perfusion. For example, patients with sickle cell disease suffer from “crises” – including crises in which blood is pooled and sequestered in their spleens, crises in which bone marrow function temporarily ceases due to viral infection, and crises that involve microvascular occlusion – during which times hematocrit levels can drop precipitously. This acute worsening of anemia affects blood flow to major organs, including the brain, and a non-invasive monitor of adequate perfusion (e.g., DCS) could help avoid devastating complications like stroke [23]. At the other end of the spectrum, patients with polycythemia vera, a bone marrow disease that leads to elevated hematocrit levels, suffer from increasing hematocrit levels when their disease worsens or when medications begin to fail. Again, given the high risk of stroke in these patients, assessment of microvascular cerebral blood flow with DCS may provide valuable information to guide therapeutic management.

Determining the effects of hematocrit on the DCS-measured BFI is challenging in large part due to experimental limitations. In vivo, the vasculature dilates in response to decreases in hematocrit to enhance perfusion and maintain adequate oxygen delivery, making it inherently difficult to separate the influence of hematocrit from blood flow on BFI. Alternatively, determining these effects in vitro is also challenging given the lack of optical phantoms that recapitulate tissue microvasculature. Typically, phantoms for DCS consist of a bulk liquid wherein “blood flow” is manipulated by adjusting temperature, viscosity, or particle size [24,25]. While some in vitro DCS phantoms have employed small millimeter-diameter tubes embedded in a bulk medium [26], the dimensions of these vessels do not adequately recapitulate the microvasculature and thus limit interpretability of the results.

Herein, we present a novel in vitro DCS phantom platform that consists of hundreds of micron-sized channels embedded within a solid polydimethylsiloxane/titanium dioxide/India Ink substrate. While microfluidic systems are not inherently novel, their application to DCS offers a substantial improvement over the traditional bulk liquid phantoms. Our system allows us to utilize physiologically relevant arteriole-sized channel diameters and to flow blood through these channels. In this work, we first validate that DCS can accurately estimate flow in this novel platform, and then we use the platform to demonstrate that the DCS-measured blood flow index is strongly confounded by hematocrit. Finally, we use these results to approximate a hematocrit-correction factor that may be used to minimize the influence of hematocrit.

2. Material and methods

2.1 Microfluidic phantom platform

For all experiments described herein, we designed a phantom platform that consists of a thin layer of hundreds of long (> 1.5 cm), uniformly spaced, parallel, micron-sized channels embedded 0.3 cm below the surface of a solid polydimethylsiloxane (PDMS), titanium dioxide (TiO2), and India Ink substrate (Fig. 1). Four phantoms were independently tested with minimum microchannel cross-sections of 10×11, 30×28, 60×56, and 100×85 µm (w×h). Channel sizes were chosen to roughly mimic the dimensions of arterioles, capillaries, and postcapillary venules [27]. Vessel spacing within the microfluidic layer was maintained at a constant volume fraction of 3/8 for all phantoms [28,29].

To fabricate these phantoms, standard photolithography processes were used [28]. In brief, SU-8 series photoresists were spun onto silicon wafers to desired heights after cleaning and dehydration. The MLA150 Maskless Aligner (Heidelberg Instruments) was then used to directly expose patterns onto soft baked resist covered wafers using a 375 nm laser. Following post exposure baking and developing steps, wafers were silanized by treatment with vapor phase hexamethyldisilazane (HMDS) for at least 24 hours. The resulting patterned wafers served as masters for generation of multiple identical microfluidic devices via soft lithography. In this case, a mixture of PDMS/TiO2/India Ink was prepared by combining 80 g of PDMS at a 10:1 ratio of elastomer to curing agent (NC9285739, Fisher Scientific), 0.152 g of TiO2 (S25818, Fisher Scientific), and 0.084 g of India Ink (Higgins, Chartpak, MA) [30]. The PDMS/TiO2/India Ink mixture was poured over the silicon wafer to achieve a thickness of ∼0.3 cm. The remainder of this PDMS/TiO2/India Ink mixture was poured into a plain petri dish to create a thin ∼0.2 cm sheet. Both dishes were placed in a vacuum chamber for 1 hour and then cured overnight at 60°C. Next, the surfaces of the patterned device and the unpatterned sheet were exposed to oxygen plasma (NC9332171, Fisher Scientific) for 1 minute and then bonded together. Bonding was further secured by placing the device on a hot plate at 80°C for 10 minutes. The final microfluidic embedded device was trimmed to ∼2×3×0.5 cm. Finally, to model the experimental setup as a series of parallel slabs over a semi-infinite medium for data analysis, the microfluidic device was placed on a large PDMS/TiO2/India Ink block (∼13×9×6 cm) with the same optical properties as the embedded microfluidic device.

2.2 Experimental protocol

To validate that DCS can accurately measure flow in this phantom platform, we first flowed Intralipid (Fresenius Kabi, Baxter Healthcare, Deerfield, IL) through each of the microfluidic phantoms. Measurements were made on each phantom at multiple flow rates and at varied concentrations of Intralipid. Intralipid concentration was titrated from 20 to 7% by diluting with distilled water.

To validate the platform with blood and to assess the influence of hematocrit on DCS, we obtained blood samples from 7 healthy human subjects (6–24 mL/subject). Blood was collected in EDTA tubes to prevent clotting; all samples were used within 8 hours of collection. Hematocrit was measured via complete blood count (Element HT5, Heska). Each blood sample was aliquoted into 6 subsamples that were diluted with phosphate buffered saline (PBS) to achieve hematocrits ranging from 20 to 45% in increments of 5%. DCS measurements were made on the 30×28 and 100×85 µm microchannel phantoms using each hematocrit aliquot at multiple flow rates. Prior to running each hematocrit aliquot in the phantom, the microfluidic device was flushed with PBS for 2 minutes.

For all experiments, the input of each microfluidic device was connected to a standard syringe pump (70-3008, Harvard Apparatus) through microbore tubing (06418-02 and 06417-11, Cole-Parmer Instrument Company) [28]. The output was connected to a reservoir and allowed to drain freely. The pump flow rate was incrementally adjusted such that the average velocity in the smallest channels varied from 0.155 to 0.93 cm/s in steps of 0.155 cm/s to mimic physiologically relevant velocities [31]. Pump flow rate, Q, can be related to average channel velocity via $Q = {A_{cs}} \times {v_{avg}} \times {n_{chan}}$, where Acs, vavg, and nchan are the cross-sectional area of the microchannels, average velocity, and number of microchannels, respectively [32]. Once the desired flow rate was set, DCS data were acquired after a 1 minute equilibration period.

 

Fig. 1. In vitro DCS microfluidic phantom. Schematic representation of the (a) side and (b) top view of the microfluidic tissue-simulating phantom. The phantom is comprised of a layer of hundreds of microchannels with height, h, embedded 0.3 cm below the surface of a polydimethylsiloxane (PDMS), titanium dioxide (TiO2), and India Ink substrate. Intralipid or blood is flowed at a known rate using a standard syringe pump. A 1 cm source-detector separation DCS sensor is placed at the surface the device, and the 3 layer slab solution to the correlation diffusion equation is used to extract an average flow index within the microfluidic layer (c) Image of the experimental setup represented in (a) and (b). (d) Image of the microfluidic capillary network in a transparent PDMS substrate to visualize the channel architecture.

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2.3 DCS acquisition

DCS measurements were made with a custom built instrument comprised of an 852 nm long coherence length laser (iBeam smart, TOPTICA Photonics, Farmington, NY), a photon counting avalanche photodiode array (SPCM-AQ4C-IO, Perkin-Elmer, Quebec, Canada), and a hardware autocorrelator board (Flex05-8ch, www.correlator.com, NJ). A custom optical sensor was used that consisted of a 1 mm fiber bundle source (FTIIG23767, Fiberoptics Technology, Pomfret, CT) spaced 1 cm away from a single-mode detection fiber (780HP, Thorlabs, Newton, NJ). Fibers were embedded in a black 3D printed holder that was secured to the surface of the microfluidic phantom (Fig. 1(c)). The source and detector were oriented such that they aligned parallel with the microchannels; however, the sensor orientation and positioning did not significantly influence the results (Appendix 1). Measurements of the intensity autocorrelation function ${g_2}(\rho ,\tau )$ were made at a 3s integration time and 20-36 frames of data were acquired for each flow rate.

2.4 DCS analysis

To analyze DCS data, the experimental setup was modeled as a series of two parallel slabs over a semi-infinite medium. For this geometry, the steady state solution of the correlation diffusion equation for the unnormalized field correlation function at a distance, $\rho $, from the source on the surface of the top slab is [33]:

$${G_1}(\rho ,\tau ) = \frac{1}{{2\pi }}\int\limits_0^\infty {\tilde{G}_1^0} (s,\tau )s{J_0}(s\rho )ds$$
where J0 is the Bessel function of zeroth order, and
$$\tilde{G}_1^0(s,\tau ) = \frac{{\textrm{numerator}}}{{\textrm{denominator}}},$$
$$\begin{array}{l} \textrm{numerator = }({\beta _1}{D_1}\cosh ({\beta _1}({\Delta _1} - {z_s}))({\beta _2}{D_2}\cosh ({\beta _2}{\Delta _2}) + {\beta _3}{D_3}\sinh ({\beta _2}{\Delta _2})) + \\ {\beta _2}{D_2}({\beta _3}{D_3}\cosh ({\beta _2}{\Delta _2}) + {\beta _2}{D_2}\sinh ({\beta _2}{\Delta _2}))\sinh ({\beta _1}({\Delta _1} - {z_s}))) \end{array}$$
$$\begin{array}{l} \textrm{denominator = }{\beta _2}{D_2}\cosh ({\beta _2}{\mathrm{\Delta }_2})({\beta _1}({D_1} + {\beta _3}{D_3}{z_0})\cosh ({\beta _1}{\mathrm{\Delta }_1}) + ({\beta _3}{D_3} + \beta _1^2{D_1}{z_0})\sinh ({\beta _1}{\mathrm{\Delta }_1}))\\ + ({\beta _1}({\beta _3}{D_1}{D_3} + \beta _2^2D_2^2{z_0})\cosh ({\beta _1}{\mathrm{\Delta }_1}) + (\beta _2^2D_2^2 + \beta _1^2{\beta _3}{D_1}{D_3}{z_0})\sinh ({\beta _1}{\mathrm{\Delta }_1})) \times \sinh ({\beta _2}{\mathrm{\Delta }_2}). \end{array}$$
Here, $\beta _i^2 = {s^2} + 3{\mu _{a,i}}\mu _{s,i}^, + 6\mu _{s,i}^{,2}k_{0,i}^2{P_i}\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle$, ${D_i} = 1/(3\mu _{s,i}^,)$, ${z_0} = 1/({\mu _{a,1}} + \mu _{s,1}^,)$, ${z_s} = 1/\mu _{s,1}^,$, ${\mathrm{\Delta }_i}$ is the thickness of the ith layer, ${\mu _{a,i}}$ and $\mu _{s,i}^,$ are the absorption and reduced scattering coefficients, respectively, for the ith layer, ${k_{0,i}} = 2\pi n/\mathrm{\lambda }$ is the wave number for the ith layer, λ is the wavelength of light (852 nm), n is the index of refraction (assumed to be 1.4 for all layers [30]), Pi is the probability of scattering off of a moving scatterer in the ith layer, and $\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle $ is the mean-square displacement of the moving scatterers in the ith layer. For our experimental setup, layers 1 and 3 consisted of the PDMS/TiO2/India Ink substrate, and layer 2 contained the microchannels embedded in the PDMS/TiO2/India Ink substrate, such that ${\mathrm{\Delta }_1} = $ 0.3 cm and ${\mathrm{\Delta }_2}$ was dependent on the height of the microchannels (either 11, 28, 56, or 85 μm). We measured the optical properties of the substrate in layers 1 and 3 (${\mu _{a,substrate}}$ and $\mu _{s,substrate}^,$) to be 0.2 cm-1 and 5.6 cm-1, respectively, using multi-distance frequency domain near-infrared spectroscopy on the bulk phantom without embedded microchannels [34]. The optical properties of the microfluidic layer (${\mu _{a,2}}$ and $\mu _{s,2}^,$) took the form of a weighted average by volume of the optical properties of the substrate and the flowing medium (either Intralipid or blood). Optical properties of Intralipid and blood were obtained from literature [35,36].

The probability of scattering off of moving scatterers within the 1st and 3rd layers was assumed to be negligible (i.e., P1 and P3∼0). For layer 2, this probability took the form of ${P_2} = f \times \mu _{s,flow}^,/\mu _{s,avg}^,$, where $f$ is the volume fraction of flowing medium within the microfluidic layer (0.375 for all phantoms), $\mu _{s,flow}^,$ is the reduced scattering coefficient for the flowing medium at 852 nm, and $\mu _{s,avg}^, = f \times \mu _{s,flow}^, + (1 - f) \times \mu _{s,substrate}^,$. When flowing Intralipid, we assumed $\mu _{s,flow}^, = 186 \times C$ cm-1, where C is the fractional concentration of Intralipid. When flowing blood, $\mu _{s,flow}^,$ for each blood sample was estimated using an empirically-derived relationship between the reduced scattering coefficient and hematocrit [36].

The mean-square particle displacement, $\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle $, of layers 1 and 3 was assumed to be negligible. For layer 2, we tested two flow models for $\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle $: convective and diffusive. For the convective model, $\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle $ took the form of ${\left\langle v \right\rangle ^2}{\tau ^2}$, where $\left\langle v \right\rangle $ is the spatially weighted average speed of the particles within a microchannel and $\tau $ is the delay time. For the diffusive model, $\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle $ took the form of $\left\langle D \right\rangle \tau $, where $\left\langle D \right\rangle $ is the spatially-weighted diffusion coefficient of the scatterers within the microchannels. For all experiments, data was separately fit to these two models, and the sum of squares of the fit residuals (SS) was used to determine the form of the mean-square displacement that yielded the best fit of the data. For Intralipid, we expect $\left\langle {\mathrm{\Delta }r_i^2(\tau )} \right\rangle $ to be dominated by convective motion, whereas for blood, we expect diffusive motion to dominate. The dominance of diffusion is expected due to an effect known as shear-induced or self diffusion, which occurs in the presence of a shear flow under small Reynolds numbers [3740]. Effectively, multiple inter-particle hydrodynamic interactions give rise to significant lateral displacements that, when taken together, constitute a random walk. Although shear-induced diffusion is present for both Intralipid and blood, the magnitude of this effect is expected to be much smaller for Intralipid than blood given the small size and spherical nature of Intralipid [38,39] compared to the larger size and deformable, disk-shape nature of red blood cells [21,4047].

Experimentally, DCS measures the normalized intensity autocorrelation function, ${g_2}(\rho ,\tau )$, which is typically converted to the normalized field correlation function, ${g_1}(\rho ,\tau )$, using the Siegert relation [48]. However, our phantom is non-ergodic such that the measured time averaged ${g_2}(\rho ,\tau )$ is not equivalent to the ensemble average of ${g_2}(\rho ,\tau )$ due to the presence of both dynamic and static layers; hence, the Siegert relation breaks down. To relate experimental measurements of ${g_2}(\rho ,\tau )$ to theoretical models for ${g_1}(\rho ,\tau )$ in a non-ergodic sample, we isolate our fit of ${g_2}(\rho ,\tau )$ to the early portion of the curve that is dominated by photons that have sampled the dynamic microchannel layer such that the Siegert relation is valid [49]. For Intralipid, fits were constrained to ${g_2}(\rho ,\tau ) \ge 1.33$, and for blood, fits were constrained to ${g_2}(\rho ,\tau ) \ge 1.25$. These thresholds were determined based on visual inspection of the data and were chosen conservatively to isolate the appropriate portion of the ${g_2}(\rho ,\tau )$ curve for all flow rates tested. Each measured frame of ${g_2}(\rho ,\tau )$ data was fit to Eq. (1) for either $\left\langle v \right\rangle $ or $\left\langle D \right\rangle$ within the microfluidic layer (for Intralipid or blood experiments, respectively) and $\beta$, a coherence factor used to relate ${g_2}(\rho ,\tau )$ to ${g_1}(\rho ,\tau )$ in the Siegert relation. Results were averaged to obtain a mean and standard error $\left\langle v \right\rangle $ or $\left\langle D \right\rangle$ across all frames for each pump flow rate. For consistency with in vivo DCS studies wherein P is not known, when flowing blood through the phantom we also estimated a blood flow index, $BFI \equiv {P_2}\left\langle D \right\rangle $, using the known P2.

2.5 Statistical analysis

For Intralipid experiments, Lin’s concordance correlation coefficient (CCC) [50] was used to quantify the agreement between the DCS-measured velocity and the true average velocity in the channels. The CCC is the product of Pearson’s R, a measure of precision, and a bias correction factor which reflects the degree that the linear association between two variables differs from 45 degrees through the origin. For whole blood, we plotted the DCS-measured blood flow index (BFI) against the known velocity (vavg) or flow rate (BFabs) to assess the functional relationship. We observed a strong linear relationship (Fig. 3); thus, we modeled the relationships between BFI and vavg and BFI and BFabs with linear mixed effects models using sample specific random intercepts that took the forms of $BFI = {\beta _0} + {\beta _1} \times r + {\beta _2} \times {v_{avg}} + {\beta _3} \times r \times {v_{avg}}$ and $BFI = {\beta _0} + {\beta _1} \times r + {\beta _2} \times B{F_{abs}} + {\beta _3} \times r \times B{F_{abs}}$, respectively, where r is the microchannel dimension (30 or 100 μm).

To assess the influence of hematocrit on $\left\langle D \right\rangle$, we first graphically examined the relationship between $\left\langle D \right\rangle $ and hematocrit across several flow velocities and two vessel sizes. Prior to modeling, we assessed the functional form of this relationship using linear models with both linear and higher ordered polynomial terms. The relationship appeared linear across both vessel sizes and all velocities tested; higher-order polynomials did not significantly improve the model fit. Thus, to empirically describe the relationship between hematocrit (Hct) and $\left\langle D \right\rangle $, we used a linear mixed effects model that took the form of $\left\langle D \right\rangle = {\beta _0} + {\beta _1} \times Hct + {\beta _2} \times {v_{avg}} + {\beta _3} \times Hct \times {v_{avg}}$. All data from both vessel sizes were included in this model. The coefficients derived from this model were used to develop a hematocrit-correction factor for $\left\langle D \right\rangle $. In order to estimate a confidence interval (CI) for the hematocrit-correction factor, we simulated 1,000 bootstrap samples of size n = 17 with replacement from the overall dataset. For each bootstrap sample, a linear regression was applied using the equation above, and the resulting correction factor was noted. The 95% CI was derived from the 2.5 and 97.5 percentiles of the bootstrap distribution of 1,000 estimated correction factors.

Finally, we applied this hematocrit correction factor to the measured data. We performed a secondary linear mixed effects model for the data obtained from each vessel size to confirm independence of ${\left\langle D \right\rangle _{Hct - corrected}}$ from hematocrit (i.e., ${\left\langle D \right\rangle _{Hct - corrected}} = {\beta _0} + {\beta _1} \times Hct + {\beta _2} \times {v_{avg}} + {\beta _3} \times Hct \times {v_{avg}}$). The goal of this model was to demonstrate that ${\beta _0},{\beta _1}$ and ${\beta _3}$ were not statistically different from 0. All regression analyses were performed in Matlab (Mathworks, Natick, MA) using the fitlme function. Goodness of fit was determined by visual inspection of fit residuals and quantification of model R2. Statistical significance was declared for p-values < 0.05.

3. Results

3.1 Verification of DCS measurements in the microfluidic phantom using Intralipid

To demonstrate the accuracy of DCS in this platform, we first used Intralipid at multiple flow rates. Representative ${g_2}(\tau )$ curves from Intralipid along with the best fit lines using the diffusive and convective models for the mean-square displacement in the microfluidic layer are shown in Fig. 2(a). The convective model provided the best fit to the data, as indicated by the reduction in the sum of squares fit residual (SS). Using this model, the DCS-measured average flow velocities agreed well with the known average velocities across all concentrations of Intralipid tested (all R2>0.99, p<0.01, CCC > 0.95, Fig. 2(b)). This strong agreement between the measured and known velocities was also seen for all vessel sizes tested (all R2>0.98, p<0.001, CCC > 0.95, Fig. 2(c)).

 

Fig. 2. Verification with Intralipid. (a) Representative ${g_2}(\tau )$ data measured when flowing Intralipid through the 30 × 28 µm microfluidic phantom. The gray dashed line represents the cutoff used to fit the data to minimize the effects of non-ergodicity. The convective model (red) resulted in the lower sum of squares (SS) fit residual than the diffusive model (blue). (b-c) The DCS-measured velocity (vmeas) versus the known average velocity (vavg) in the (b) 30 × 28 µm microchannels for five concentrations of Intralipid (7-20%, all R2>0.99, CCC>0.95, and p<0.01) and (c) in four different microchannel phantoms using 20% Intralipid (10-100 μm, all R2>0.98, CCC>0.95, and p<0.001). In these subplots, the black dashed line denotes the line of unity, and the colored dots are the mean/standard error values across 36 frames of data.

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3.2 Verification of DCS measurements in the microfluidic phantom using whole blood

 

Fig. 3. Verification with whole blood. (a) Representative ${g_2}(\tau )$ curve measured when flowing whole blood (hematocrit = 45%) through the microfluidic phantom. The gray dashed line represents the cutoff used when fitting the data to minimize the effects of non-ergodicity. The diffusive model (blue) resulted in the lower sum of squares (SS) fit residual than the convective model (red). (b-c) Mean/standard error DCS-measured blood flow index (BFI) versus b) the known average velocity (vavg) and c) the known flow rate in the microchannels for 30×28 µm (n=4) and 100×85 µm (n=3) vessel sizes (salmon pink and green, respectively).

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Representative ${g_2}(\tau )$ curves from whole blood (hematocrit = 45%) along with best fit lines using the diffusive and convective models for the mean-square displacement in the microfluidic layer are shown in Fig. 3(a). When flowing whole blood, the diffusive model provided the best fit to the data, as shown by the lower SS. Using this model, the DCS-measured BFI was strongly linearly correlated with the known average velocity in the channels, such that a 4× increase in velocity from 0.155 to 0.62 cm/s resulted in a similar 4× increase in BFI. This linear trend persisted across both vessel sizes studied (Fig. 3(b), model R2=0.98, p<0.001, no significant interaction term between velocity and vessel size, p=0.64). BFI was also strongly correlated with the average flow rate in the channels, although the slope of this trend was dependent on vessel size, with smaller vessels having a steeper slope (Fig. 3(c), model R2=0.98, p<0.001, significant interaction term between average flow rate and vessel size, p<0.001).

3.3 DCS blood flow index is inversely proportional to hematocrit

 

Fig. 4. Effects of hematocrit. DCS-measured blood flow index (BFI) versus hematocrit at five fixed flow velocities for (a) 30×28 µm and (b) 100×85 µm microchannels. (c-d) The DCS-measured spatially-weighted average diffusion coefficient ($\left\langle D \right\rangle = BFI/{P_2}$), where P2 is the known probability of scattering off of a moving red blood cell within the microfluidic layer) versus hematocrit across five fixed velocities for (c) 30×28 µm (n=4) and (d) 100×85 µm (n=3) microchannels. All data are reported as mean/standard error across 3-4 blood samples; solid lines connect data points obtained at a fixed flow velocity.

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Next, we varied the hematocrit of the flowing blood within the microfluidic layer. At a fixed flow velocity, as hematocrit increased, the DCS-measured BFI decreased (Fig. 4(a)-(b)). This confounding effect was observed for all vessel sizes and flow velocities tested. Further, we found that the hematocrit-dependence of BFI was not solely attributable to the fraction of scattering events that occur from moving scatterers (P2), which is known for our model systems. The BFI-derived effective diffusion coefficient, $\left\langle D \right\rangle = BFI/{P_2}$, was inversely linearly proportional to hematocrit and varied by as much as ∼70% as hematocrit dropped from 45% to 20% (Fig. 4(c)-(d)). Modeling $\left\langle D \right\rangle $ as a function of hematocrit and vavg, we found that the overall effect of hematocrit was not significant (p = 0.934). However, both the interaction between hematocrit and vavg and the overal effect of vavg were signficant (p < 0.001), indicating a signficant relationship for hematocrit that depended on vavg (p < 0.001), i.e., $\left\langle D \right\rangle = {\beta _2} \times {v_{avg}} + {\beta _3} \times {v_{avg}} \times Hct$, where ${\beta _2}$ and ${\beta _3}$ are the model coefficients for the overall effect of vavg and the interaction between vavg and hematocrit, respectively.

Using this empirical model, we derived a hematocrit correction factor, $1/(1 - \gamma \times Hct)$, where $\gamma \equiv {\beta _3}/{\beta _2}$ and ${\left\langle D \right\rangle _{Hct - corrected}} = \left\langle D \right\rangle /(1 - \gamma \times Hct)$. We found $\gamma = 1.8$ with a 95% CI of (1.786, 1.792). Figure 5 shows the results of applying this correction factor to the data from Fig. 4(c)-(d). Once corrected, neither the overall effect of hematocrit nor the interaction between hematocrit and vavg were statistically significant (all p>0.05 for both vessel sizes tested).

 

Fig. 5. Correcting for the effects of hematocrit. The hematocrit-corrected diffusion coefficient (${\left\langle D \right\rangle _{Hct - corrected}}$) versus hematocrit for (a) 30×28 µm (n=4) and (b) 100×85 µm (n=3) microchannels. All data are reported as mean/standard error across 3-4 blood samples; dashed lines connect data points obtained at a fixed flow velocity.

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4. Discussion

In this study, we characterize the confounding influence of hematocrit on the DCS-measured blood flow index using a novel in vitro microfluidic phantom platform. This in vitro model presents an alternative to bulk liquid phantoms commonly used by the DCS community in that it better mimics the tissue microvasculature. The slab geometry of the phantom enables us to utilize an analytical solution to the correlation diffusion equation to measure flow dynamics within the microchannels. Furthermore, the phantom is biologically inert, thereby enabling us to probe a particular biological interaction of interest, e.g., hematocrit and BFI, without unintended confounding variables (e.g., vessel dilation in response to lowered hematocrit). These interactions are often challenging to isolate in vivo wherein hemodynamics are regulated by complex physiological processes.

We first validated this phantom platform using Intralipid, a highly scattering fatty emulsion that consists of spherical soybean oil droplets within a lipid membrane (∼0.5µm in diameter [51]) suspended in glycerin and water. As expected for the case of small spherical particles in a shear flow under low Reynolds number, we show that convective motion dominates the decay of the intensity autocorrelation due to the relatively small influence of shear-induced diffusion. Given this dominance of convective motion, we demonstrate that DCS can accurately measure the average velocity within the micron-sized channels of the phantom. We observed strong agreement between the DCS-measured velocity and known average velocity across all channel sizes and Intralipid concentrations tested (Fig. 2).

In the case of whole blood, we observed that the diffusive model provided the best fit to the intensity autocorrelation data (Fig. 3(a)). This observation is in good agreement with the plethora of in vivo DCS studies that show the diffusive model best fits experimental data obtained on a wide-range of tissue types. Further, it agrees well with recent in silico work by Boas et al. simulating a dense volume of tissue with embedded dynamic blood vessels that suggests the correlation decay is dominated by shear-induced diffusion [21]. Moreover, our measured diffusion coefficient is the same order of magnitude as Tang et al. measured in vivo with dynamic light scattering optical coherence tomography (∼0.5e-6 cm2/s) [52]. Through dimensional analysis, it can be inferred that the shear-induced diffusion coefficient should be proportional to the shear rate [3739]. Thus, as Boas et al. derive, the blood flow index measured by DCS should be proportional to the average flow rate in the vessels [21]. Indeed, we observed a strong positive correlation between BFI and both flow velocity and flow rate, where the slope of the latter linear relationship (Fig. 3(b)-(c)). The consistency of these results with the work of Boas et al. further validate our platform and support the notion that DCS is sensitive to shear-induced diffusion of red blood cells.

Importantly, for both vessel sizes studied, we found that BFI decreases as hematocrit increases, despite a constant flow rate (Fig. 4(a)-(b)). This result confirms that hematocrit is a significant factor that can confound estimations of blood flow made through DCS. We show that the dependence of BFI on hematocrit is driven by both P2, the fraction of scattering events that occurs off moving red blood cells (which is known for our phantom), as well by $\left\langle D \right\rangle $ (Fig. 4(c)-(d)). Assuming the DCS-measured $\left\langle D \right\rangle $ reflects the spatially-weighted average of the shear-induced diffusion coefficient, there are several possible explanations for the inverse linear relationship between hematocrit and $\left\langle D \right\rangle $. While an increase in hematocrit increases the chances of interparticle interactions, it also limits the degree of lateral movement that can be experienced before interaction with another blood cell and thereby may reduce the magnitude of the diffusion coefficient. Alternatively, or possibly additionally, the Fåhraeues-Lindqvist effect may play a significant role [53,54]. This effect dictates that for vessel sizes less than ∼300 µm, increasing hematocrit leads to an increase in apparent viscosity, which in turn may significantly reduce lateral displacements of red blood cells. Although the hematocrit-dependence of the shear-induced diffusion coefficient has been investigated previously by a handful of studies [40,42,43,55], none have been performed with relevant microvascular geometries, concentrations, and/or on time scales relevant to DCS measurements, preventing direct comparison to our results.

The observed inverse relationship between BFI and hematocrit agrees well with recent in vivo data from our laboratory wherein we found that DCS appeared to significantly overestimate cerebral blood flow in moderately/severely anemic patients with sickle cell disease compared to healthy controls [56]. While studies with MRI and PET have demonstrated ∼1.5× increases in cortical cerebral blood flow in sickle versus controls [5759], we observed a markedly more pronounced 3× difference in DCS-measured BFI between sickle and control groups on average. After applying the empirically derived correction factor described herein, the median difference in BFI between sickle and control groups was reduced to 1.7×, which is much closer to literature reports. While this result is promising, more work is needed to validate this hematocrit correction factor in vivo, as the results obtained from the simplified vessel geometry of our in vitro phantom may not directly translate to the complex vascular networks found in tissue.

Finally, we note several limitations of this novel microfluidic DCS phantom. The device consists of hundreds of uniformly sized channels and flow is only permitted in one direction, whereas in vivo capillary networks contain an array of vessel sizes that are not planar in geometry. Moreover, the channels are rigid in size, whereas in vivo blood vessels exhibit a range of compliance. Vessel compliance may permit additional diffusive motion than observed in our rigid channel structure. Despite these limitations, this microfluidic platform certainly provides a better recapitulation of the microvascular environment than existing DCS phantom platforms. Finally, the correlation diffusion equation that we used to fit our experimental data relies on the assumption that the motion of the scatterers is uncorrelated, i.e., that the photon scattering direction is randomized between dynamic scattering events. This assumption is not valid when flowing blood through our microfluidic platform, as outlined in [21], although Monte Carlo simulations suggest that the correlation diffusion equation is still a good model for the system. Future work that independently measures the shear-induced diffusion coefficient within the microchannels is warranted to confirm the accuracy of the model.

5. Conclusion

In summary, we develop and validate a novel phantom platform for diffuse correlation spectroscopy measurements. We use this platform to determine that hematocrit significantly influences the DCS-measured blood flow index, and we derive a hematocrit correction factor to minimize this influence. These results suggest that without correcting for the effects of hematocrit, DCS BFI will overestimate blood flow in anemic patients. Future work will validate the hematocrit correction factor in vivo and will use this platform to investigate other parameters that may confound the blood flow index, including vessel size, vessel density, and red blood cell morphology.

Appendix 1

To examine the effect of sensor orientation on the measured data, we flowed 20% Intralipid through the 30×28 µm microchannel device at four velocities and varied the DCS sensor positioning to be either parallel or perpendicular to the microfluidic channels. Sensor orientation did not significantly influence the measured channel velocity (Fig. 6(a)). Thus, for all experiments described herein, the DCS sensor was oriented parallel to the microfluidic channels for consistency.

 

Fig. 6. Effect of sensor orientation and measurement repeatability. (a) The DCS-measured velocity (vmeas) versus the known average velocity (vavg) of 20% Intralipid in the 30×28 µm microchannel phantom device when the sensor was positioned parallel (magenta) or perpendicular (brown) to the channel orientation. Data are plotted as mean and standard error across 36 frames of data. (b) The DCS-measured blood flow index (BFI) versus the known average velocity of blood obtained from a healthy volunteer and diluted with PBS to 20% (blue) and 30% hematocrit (orange). Data are reported as mean and standard error across two repetitions of this experiment from the same donor, spaced nine months apart.

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To examine the repeatability of these measurements we obtained two blood samples from a healthy volunteer taken nine months apart. Measurements of DCS blood flow index were acquired within eight hours of sample collection at multiple flow velocities and two different hematocrits. Data were found to be highly repeatable with an intra-class correlation coefficient (ICC) = 0.95 (Fig. 6(b)) [60,61].

Funding

National Institutes of Health (R21-HL138062); American Heart Association (19POST34380337).

Acknowledgements

We thank Drs. Stefan Carp, Peter Yunker, and Robert Mannino for fruitful discussions. We also thank Dr. David Myers for assistance in obtaining blood samples. We also acknowledge the Center for Pediatric Innovation, Children’s Heart Research and Outcomes Center, and Children’s Healthcare of Atlanta.

Disclosures

The authors declare no conflicts of interest.

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References

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  1. E. M. Buckley, A. B. Parthasarathy, P. E. Grant, A. G. Yodh, and M. A. Franceschini, “Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects,” Neurophotonics 1(1), 011009 (2014).
    [Crossref]
  2. T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” NeuroImage 85 Pt 1(01), 51–63 (2014).
    [Crossref]
  3. T. Durduran, C. Zhou, B. L. Edlow, G. Yu, R. Choe, M. N. Kim, B. L. Cucchiara, M. E. Putt, Q. Shah, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Express 17(5), 3884 (2009).
    [Crossref]
  4. J. Selb, K.-C. Wu, J. Sutin, P.-Y. Lin, P. Farzam, S. Bechek, A. Shenoy, A. B. Patel, D. A. Boas, M. A. Franceschini, and E. S. Rosenthal, “Prolonged monitoring of cerebral blood flow and autoregulation in subarachnoid hemorrhage and stroke patients with diffuse correlation spectroscopy,” Neurophotonics 5(04), 1 (2018).
    [Crossref]
  5. M. T. Mullen, A. B. Parthasarathy, A. Zandieh, W. B. Baker, R. C. Mesquita, C. Loomis, J. Torres, W. Guo, C. G. Favilla, S. R. Messé, A. G. Yodh, J. A. Detre, and S. E. Kasner, “Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke,” J. Stroke Cerebrovasc. Dis. 28(11), 104294 (2019).
    [Crossref]
  6. C. G. Favilla, R. C. Mesquita, M. Mullen, T. Durduran, X. Lu, M. N. Kim, D. L. Minkoff, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Optical bedside monitoring of cerebral blood flow in acute ischemic stroke patients during head-of-bed manipulation,” Stroke 45(5), 1269–1274 (2014).
    [Crossref]
  7. W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).
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2020 (1)

S. Fantini and A. Sassaroli, “Frequency-Domain Techniques for Cerebral and Functional Near-Infrared Spectroscopy,” Front. Neurosci. 14(April), 1–18 (2020).
[Crossref]

2019 (3)

M. T. Mullen, A. B. Parthasarathy, A. Zandieh, W. B. Baker, R. C. Mesquita, C. Loomis, J. Torres, W. Guo, C. G. Favilla, S. R. Messé, A. G. Yodh, J. A. Detre, and S. E. Kasner, “Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke,” J. Stroke Cerebrovasc. Dis. 28(11), 104294 (2019).
[Crossref]

D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
[Crossref]

S. Y. Lee, K. R. Cowdrick, B. Sanders, E. Sathialingam, C. E. McCracken, W. A. Lam, C. H. Joiner, and E. M. Buckley, “Noninvasive optical assessment of resting-state cerebral blood flow in children with sickle cell disease,” Neurophotonics 6(03), 1 (2019).
[Crossref]

2018 (6)

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadzic, S. A. Carp, J. Lee, and D. A. Boas, “Shear-induced diffusion of red blood cells measured with dynamic light scattering-optical coherence tomography,” J. Biophotonics 11(2), e201700070 (2018).
[Crossref]

J. Selb, K.-C. Wu, J. Sutin, P.-Y. Lin, P. Farzam, S. Bechek, A. Shenoy, A. B. Patel, D. A. Boas, M. A. Franceschini, and E. S. Rosenthal, “Prolonged monitoring of cerebral blood flow and autoregulation in subarachnoid hemorrhage and stroke patients with diffuse correlation spectroscopy,” Neurophotonics 5(04), 1 (2018).
[Crossref]

J. M. Lynch, T. Ko, D. R. Busch, J. J. Newland, M. E. Winters, K. Mensah-Brown, T. W. Boorady, R. Xiao, S. C. Nicolson, L. M. Montenegro, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Preoperative cerebral hemodynamics from birth to surgery in neonates with critical congenital heart disease,” J. Thorac. Cardiovasc. Surg. 156(4), 1657–1664 (2018).
[Crossref]

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadžić, S. A. Carp, J. Lee, and D. A. Boas, “Measurement of shear-induced diffusion of red blood cells using dynamic light scattering-optical coherence tomography,” Proc. SPIE 10481, 57 (2018).
[Crossref]

L. Cortese, G. Lo Presti, M. Pagliazzi, D. Contini, A. D. Mora, A. Pifferi, S. K. V. Sekar, L. Spinelli, P. Taroni, M. Zanoletti, U. M. Weigel, S. de Fraguier, A. Nguyen-Dihn, B. Rosinski, and T. Durduran, “Liquid phantoms for near-infrared and diffuse correlation spectroscopies with tunable optical and dynamic properties,” Biomed. Opt. Express 9(5), 2068 (2018).
[Crossref]

E. Sathialingam, S. Y. Lee, B. Sanders, J. Park, C. McCracken, L. Bryan, and E. M. Buckley, “Small separation diffuse correlation spectroscopy for measurement of cerebral blood flow in rodents,” Biomed. Opt. Express 9(11), 5719–5734 (2018).
[Crossref]

2017 (1)

E. M. Buckley, M. Platt, and W. Lam, “Novel in vivo and in vitro techniques to image and model the cerebral vasculature in sickle cell disease,” Blood Cells, Mol., Dis. 67(20), 114 (2017).
[Crossref]

2016 (2)

D. A. Boas, S. Sakadžic, J. Selb, P. Farzam, M. A. Franceschini, and S. A. Carp, “Establishing the diffuse correlation spectroscopy signal relationship with blood flow,” Neurophotonics 3(3), 031412 (2016).
[Crossref]

T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med. 15(2), 155–163 (2016).
[Crossref]

2015 (1)

2014 (6)

M. N. Kim, B. L. Edlow, T. Durduran, S. Frangos, R. C. Mesquita, J. M. Levine, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Continuous optical monitoring of cerebral hemodynamics during head-of-bed manipulation in brain-injured adults,” Neurocrit. Care 20(3), 443–453 (2014).
[Crossref]

J. M. Lynch, E. M. Buckley, P. J. Schwab, A. L. McCarthy, M. E. Winters, D. R. Busch, R. Xiao, D. A. Goff, S. C. Nicolson, L. M. Montenegro, S. Fuller, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Time to surgery and preoperative cerebral hemodynamics predict postoperative white matter injury in neonates with hypoplastic left heart syndrome,” J. Thorac. Cardiovasc. Surg. 148(5), 2181–2188 (2014).
[Crossref]

V. Jain, E. M. Buckley, D. J. Licht, J. M. Lynch, P. J. Schwab, M. Y. Naim, N. A. Lavin, S. C. Nicolson, L. M. Montenegro, A. G. Yodh, and F. W. Wehrli, “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab. 34(3), 380–388 (2014).
[Crossref]

C. G. Favilla, R. C. Mesquita, M. Mullen, T. Durduran, X. Lu, M. N. Kim, D. L. Minkoff, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Optical bedside monitoring of cerebral blood flow in acute ischemic stroke patients during head-of-bed manipulation,” Stroke 45(5), 1269–1274 (2014).
[Crossref]

E. M. Buckley, A. B. Parthasarathy, P. E. Grant, A. G. Yodh, and M. A. Franceschini, “Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects,” Neurophotonics 1(1), 011009 (2014).
[Crossref]

T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” NeuroImage 85 Pt 1(01), 51–63 (2014).
[Crossref]

2013 (3)

E. M. Buckley, M. Y. Naim, J. M. Lynch, D. A. Goff, P. J. Schwab, L. K. Diaz, S. C. Nicolson, L. M. Montenegro, N. A. Lavin, T. Durduran, T. L. Spray, J. W. Gaynor, M. E. Putt, A. G. Yodh, M. A. Fogel, and D. J. Licht, “Sodium bicarbonate causes dose-dependent increases in cerebral blood flow in infants and children with single-ventricle physiology,” Pediatr. Res. 73(5), 668–673 (2013).
[Crossref]

E. M. Buckley, J. M. Lynch, D. A. Goff, P. J. Schwab, W. B. Baker, T. Durduran, D. R. Busch, S. C. Nicolson, L. M. Montenegro, M. Y. Naim, R. Xiao, T. L. Spray, A. G. Yodh, J. W. Gaynor, and D. J. Licht, “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: Effects of surgical duration,” J. Thorac. Cardiovasc. Surg. 145(1), 196–205.e1 (2013).
[Crossref]

T. Omori, T. Ishikawa, Y. Imai, and T. Yamaguchi, “Shear-induced diffusion of red blood cells in a semi-dilute suspension,” J. Fluid Mech. 724, 154–174 (2013).
[Crossref]

2012 (3)

J. Dupire, M. Socol, and A. Viallat, “Full dynamics of a red blood cell in shear flow,” Proc. Natl. Acad. Sci. U. S. A. 109(51), 20808–20813 (2012).
[Crossref]

D. R. Myers, Y. Sakurai, R. Tran, B. Ahn, E. T. Hardy, R. Mannino, A. Kita, M. Tsai, and W. A. Lam, “Endothelialized Microfluidics for Studying Microvascular Interactions in Hematologic Diseases,” J. Visualized Exp. 643958(64), 3958 (2012).
[Crossref]

M. Tsai, A. Kita, J. Leach, R. Rounsevell, J. N. Huang, J. Moake, R. E. Ware, D. A. Fletcher, and W. A. Lam, “In vitro modeling of the microvascular occlusion and thrombosis that occur in hematologic diseases using microfluidic technology,” J. Clin. Invest. 122(1), 408–418 (2012).
[Crossref]

2010 (3)

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref]

M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
[Crossref]

T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010).
[Crossref]

2009 (2)

2008 (1)

R. Lima, T. Ishikawa, Y. Imai, M. Takeda, S. Wada, and T. Yamaguchi, “Radial dispersion of red blood cells in blood flowing through glass capillaries: The role of hematocrit and geometry,” J. Biomech. 41(10), 2188–2196 (2008).
[Crossref]

2007 (1)

2005 (2)

J. Li, G. Dietsche, D. Iftime, S. Skipetrov, G. Maret, T. Rockstroh, and T. Gisler, “Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy,” J. Biomed. Opt. 10(4), 044002 (2005).
[Crossref]

J. J. Stickel and R. L. Powell, “Fluid Mechanics and Rheology of Dense Suspensions,” Annu. Rev. Fluid Mech. 37(1), 129–149 (2005).
[Crossref]

2002 (1)

J. J. Bishop, A. S. Popel, M. Intaglietta, and P. C. Johnson, “Effect of aggregation and shear rate on the dispersion of red blood cells flowing in venules,” Am. J. Physiol. - Hear. Circ. Physiol. 283(5), H1985–H1996 (2002).
[Crossref]

2001 (1)

W. Cha and R. L. Beissinger, “Evaluation of Shear-Induced Particle Diffusivity in Red Cell Ghosts Suspensions,” Korean J. Chem. Eng. 18(4), 479–485 (2001).
[Crossref]

1999 (1)

1997 (1)

D. A. Boas and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. 14(1), 192 (1997).
[Crossref]

1995 (1)

D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. 75(9), 1855–1858 (1995).
[Crossref]

1994 (1)

D. V. Cicchetti, “Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology,” Psychol. Assess. 6(4), 284–290 (1994).
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1993 (1)

C. Góñez, M. Donayre, A. Villena, and G. F. Gonzales, “Hematocrit levels in children at sea level and at high altitude: effect of adrenal androgens,” Hum. Biol. an Int. Rec. Res. 65(1), 49–57 (1993).

1992 (1)

A. R. Pries, D. Neuhaus, and P. Gaehtgens, “Blood viscosity in tube flow: Dependence on diameter and hematocrit,” Am. J. Physiol. - Hear. Circ. Physiol. 263(6), H1770–H1778 (1992).
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1991 (1)

1989 (2)

L. I.-K. Lin, “A Concordance Correlation Coefficient to Evaluate Reproducibility,” Biometrics 45(1), 255 (1989).
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I. Prohovnik, S. G. Pavlakis, S. Piomelli, J. Bello, J. P. Mohr, S. Hilal, and D. C. De Vivo, “Cerebral hyperemia, stroke, and transfusion in sickle cell disease,” Neurology 39(3), 344 (1989).
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1987 (2)

D. Leighton and A. Acrivos, “The Shear-Induced Migration of Particles in Concentrated Suspensions,” J. Fluid Mech. 181(-1), 415–439 (1987).
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D. Leighton and A. Acrivos, “Measurement of shear induced self diffusion in concentrated suspensions of spheres,” J. Fluid Mech. 177, 109–131 (1987).
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1986 (1)

S. Herold, M. Brozovic, J. Gibbs, A. A. Lammertsma, K. L. Leenders, D. Carr, J. S. Fleming, and T. Jones, “Measurement of regional cerebral blood flow, blood volume and oxygen metabolism in patients with sickle cell disease using positron emission tomography,” Stroke 17(4), 692–698 (1986).
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1979 (1)

H. L. Goldsmith and J. C. Marlow, “Flow Behavior of Erythrocytes,” J. Colloid Interface Sci. 71(2), 383–407 (1979).
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1977 (1)

E. C. Eckstein, D. G. Bailey, and A. H. Shapiro, “Self-diffusion of particles in shear flow of a suspension,” J. Fluid Mech. 79(1), 191–208 (1977).
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1931 (1)

R. Fåhræus and T. Lindqvist, “The Viscosity of the Blood in Narrow Capillary Tubes,” Am. J. Physiol. Content 96(3), 562–568 (1931).
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Abramson, K.

W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

Acrivos, A.

D. Leighton and A. Acrivos, “The Shear-Induced Migration of Particles in Concentrated Suspensions,” J. Fluid Mech. 181(-1), 415–439 (1987).
[Crossref]

D. Leighton and A. Acrivos, “Measurement of shear induced self diffusion in concentrated suspensions of spheres,” J. Fluid Mech. 177, 109–131 (1987).
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Ahn, B.

D. R. Myers, Y. Sakurai, R. Tran, B. Ahn, E. T. Hardy, R. Mannino, A. Kita, M. Tsai, and W. A. Lam, “Endothelialized Microfluidics for Studying Microvascular Interactions in Hematologic Diseases,” J. Visualized Exp. 643958(64), 3958 (2012).
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Amendolia, O.

D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
[Crossref]

W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

An, H.

M. E. Fields, K. P. Guilliams, D. K. Ragan, M. M. Binkley, C. Eldeniz, Y. Chen, M. L. Hulbert, R. C. McKinstry, J. S. Shimony, K. D. Vo, A. Doctor, H. An, A. L. Ford, and J.-M. Lee, “Regional oxygen extraction predicts border zone vulnerability to stroke in sickle cell disease,” Neurology (2018).

Andrew Kofke, W.

W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

Ayers, F.

F. Ayers, A. Grant, D. Kuo, D. J. Cuccia, and A. J. Durkin, “Fabrication and characterization of silicone-based tissue phantoms with tunable optical properties in the visible and near infrared domain,” in Design and Performance Validation of Phantoms Used in Conjunction with Optical Measurements of Tissue (2008), 6870, p. 687007.
[Crossref]

Bailey, D. G.

E. C. Eckstein, D. G. Bailey, and A. H. Shapiro, “Self-diffusion of particles in shear flow of a suspension,” J. Fluid Mech. 79(1), 191–208 (1977).
[Crossref]

Baker, W. B.

D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
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M. T. Mullen, A. B. Parthasarathy, A. Zandieh, W. B. Baker, R. C. Mesquita, C. Loomis, J. Torres, W. Guo, C. G. Favilla, S. R. Messé, A. G. Yodh, J. A. Detre, and S. E. Kasner, “Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke,” J. Stroke Cerebrovasc. Dis. 28(11), 104294 (2019).
[Crossref]

E. M. Buckley, J. M. Lynch, D. A. Goff, P. J. Schwab, W. B. Baker, T. Durduran, D. R. Busch, S. C. Nicolson, L. M. Montenegro, M. Y. Naim, R. Xiao, T. L. Spray, A. G. Yodh, J. W. Gaynor, and D. J. Licht, “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: Effects of surgical duration,” J. Thorac. Cardiovasc. Surg. 145(1), 196–205.e1 (2013).
[Crossref]

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref]

W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

Balu, R.

D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
[Crossref]

W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

Bechek, S.

J. Selb, K.-C. Wu, J. Sutin, P.-Y. Lin, P. Farzam, S. Bechek, A. Shenoy, A. B. Patel, D. A. Boas, M. A. Franceschini, and E. S. Rosenthal, “Prolonged monitoring of cerebral blood flow and autoregulation in subarachnoid hemorrhage and stroke patients with diffuse correlation spectroscopy,” Neurophotonics 5(04), 1 (2018).
[Crossref]

Beissinger, R. L.

W. Cha and R. L. Beissinger, “Evaluation of Shear-Induced Particle Diffusivity in Red Cell Ghosts Suspensions,” Korean J. Chem. Eng. 18(4), 479–485 (2001).
[Crossref]

Bello, J.

I. Prohovnik, S. G. Pavlakis, S. Piomelli, J. Bello, J. P. Mohr, S. Hilal, and D. C. De Vivo, “Cerebral hyperemia, stroke, and transfusion in sickle cell disease,” Neurology 39(3), 344 (1989).
[Crossref]

Bhatia, S. N.

J. M. Higgins, D. T. Eddington, S. N. Bhatia, and L. Mahadevan, “Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing,” PLoS Comput. Biol. 5(2), e1000288 (2009).
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Binkley, M. M.

M. E. Fields, K. P. Guilliams, D. K. Ragan, M. M. Binkley, C. Eldeniz, Y. Chen, M. L. Hulbert, R. C. McKinstry, J. S. Shimony, K. D. Vo, A. Doctor, H. An, A. L. Ford, and J.-M. Lee, “Regional oxygen extraction predicts border zone vulnerability to stroke in sickle cell disease,” Neurology (2018).

Bishop, J. J.

J. J. Bishop, A. S. Popel, M. Intaglietta, and P. C. Johnson, “Effect of aggregation and shear rate on the dispersion of red blood cells flowing in venules,” Am. J. Physiol. - Hear. Circ. Physiol. 283(5), H1985–H1996 (2002).
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Boas, D.

D. Boas, “Diffuse photon probes of structural and dynamical properties of turbid media,” (1996).

Boas, D. A.

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadzic, S. A. Carp, J. Lee, and D. A. Boas, “Shear-induced diffusion of red blood cells measured with dynamic light scattering-optical coherence tomography,” J. Biophotonics 11(2), e201700070 (2018).
[Crossref]

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadžić, S. A. Carp, J. Lee, and D. A. Boas, “Measurement of shear-induced diffusion of red blood cells using dynamic light scattering-optical coherence tomography,” Proc. SPIE 10481, 57 (2018).
[Crossref]

J. Selb, K.-C. Wu, J. Sutin, P.-Y. Lin, P. Farzam, S. Bechek, A. Shenoy, A. B. Patel, D. A. Boas, M. A. Franceschini, and E. S. Rosenthal, “Prolonged monitoring of cerebral blood flow and autoregulation in subarachnoid hemorrhage and stroke patients with diffuse correlation spectroscopy,” Neurophotonics 5(04), 1 (2018).
[Crossref]

D. A. Boas, S. Sakadžic, J. Selb, P. Farzam, M. A. Franceschini, and S. A. Carp, “Establishing the diffuse correlation spectroscopy signal relationship with blood flow,” Neurophotonics 3(3), 031412 (2016).
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D. A. Boas and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. 14(1), 192 (1997).
[Crossref]

D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. 75(9), 1855–1858 (1995).
[Crossref]

Boorady, T. W.

J. M. Lynch, T. Ko, D. R. Busch, J. J. Newland, M. E. Winters, K. Mensah-Brown, T. W. Boorady, R. Xiao, S. C. Nicolson, L. M. Montenegro, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Preoperative cerebral hemodynamics from birth to surgery in neonates with critical congenital heart disease,” J. Thorac. Cardiovasc. Surg. 156(4), 1657–1664 (2018).
[Crossref]

Brozovic, M.

S. Herold, M. Brozovic, J. Gibbs, A. A. Lammertsma, K. L. Leenders, D. Carr, J. S. Fleming, and T. Jones, “Measurement of regional cerebral blood flow, blood volume and oxygen metabolism in patients with sickle cell disease using positron emission tomography,” Stroke 17(4), 692–698 (1986).
[Crossref]

Bryan, L.

Buckley, E. M.

S. Y. Lee, K. R. Cowdrick, B. Sanders, E. Sathialingam, C. E. McCracken, W. A. Lam, C. H. Joiner, and E. M. Buckley, “Noninvasive optical assessment of resting-state cerebral blood flow in children with sickle cell disease,” Neurophotonics 6(03), 1 (2019).
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E. Sathialingam, S. Y. Lee, B. Sanders, J. Park, C. McCracken, L. Bryan, and E. M. Buckley, “Small separation diffuse correlation spectroscopy for measurement of cerebral blood flow in rodents,” Biomed. Opt. Express 9(11), 5719–5734 (2018).
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E. M. Buckley, M. Platt, and W. Lam, “Novel in vivo and in vitro techniques to image and model the cerebral vasculature in sickle cell disease,” Blood Cells, Mol., Dis. 67(20), 114 (2017).
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M. Dehaes, H. H. Cheng, E. M. Buckley, P.-Y. Lin, S. Ferradal, K. Williams, R. Vyas, K. Hagan, D. Wigmore, E. McDavitt, J. S. Soul, M. A. Franceschini, J. W. Newburger, and P. Ellen Grant, “Perioperative cerebral hemodynamics and oxygen metabolism in neonates with single-ventricle physiology,” Biomed. Opt. Express 6(12), 4749 (2015).
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J. M. Lynch, E. M. Buckley, P. J. Schwab, A. L. McCarthy, M. E. Winters, D. R. Busch, R. Xiao, D. A. Goff, S. C. Nicolson, L. M. Montenegro, S. Fuller, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Time to surgery and preoperative cerebral hemodynamics predict postoperative white matter injury in neonates with hypoplastic left heart syndrome,” J. Thorac. Cardiovasc. Surg. 148(5), 2181–2188 (2014).
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V. Jain, E. M. Buckley, D. J. Licht, J. M. Lynch, P. J. Schwab, M. Y. Naim, N. A. Lavin, S. C. Nicolson, L. M. Montenegro, A. G. Yodh, and F. W. Wehrli, “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab. 34(3), 380–388 (2014).
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E. M. Buckley, A. B. Parthasarathy, P. E. Grant, A. G. Yodh, and M. A. Franceschini, “Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects,” Neurophotonics 1(1), 011009 (2014).
[Crossref]

E. M. Buckley, J. M. Lynch, D. A. Goff, P. J. Schwab, W. B. Baker, T. Durduran, D. R. Busch, S. C. Nicolson, L. M. Montenegro, M. Y. Naim, R. Xiao, T. L. Spray, A. G. Yodh, J. W. Gaynor, and D. J. Licht, “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: Effects of surgical duration,” J. Thorac. Cardiovasc. Surg. 145(1), 196–205.e1 (2013).
[Crossref]

E. M. Buckley, M. Y. Naim, J. M. Lynch, D. A. Goff, P. J. Schwab, L. K. Diaz, S. C. Nicolson, L. M. Montenegro, N. A. Lavin, T. Durduran, T. L. Spray, J. W. Gaynor, M. E. Putt, A. G. Yodh, M. A. Fogel, and D. J. Licht, “Sodium bicarbonate causes dose-dependent increases in cerebral blood flow in infants and children with single-ventricle physiology,” Pediatr. Res. 73(5), 668–673 (2013).
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T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010).
[Crossref]

M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
[Crossref]

Busch, D. R.

D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
[Crossref]

J. M. Lynch, T. Ko, D. R. Busch, J. J. Newland, M. E. Winters, K. Mensah-Brown, T. W. Boorady, R. Xiao, S. C. Nicolson, L. M. Montenegro, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Preoperative cerebral hemodynamics from birth to surgery in neonates with critical congenital heart disease,” J. Thorac. Cardiovasc. Surg. 156(4), 1657–1664 (2018).
[Crossref]

J. M. Lynch, E. M. Buckley, P. J. Schwab, A. L. McCarthy, M. E. Winters, D. R. Busch, R. Xiao, D. A. Goff, S. C. Nicolson, L. M. Montenegro, S. Fuller, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Time to surgery and preoperative cerebral hemodynamics predict postoperative white matter injury in neonates with hypoplastic left heart syndrome,” J. Thorac. Cardiovasc. Surg. 148(5), 2181–2188 (2014).
[Crossref]

E. M. Buckley, J. M. Lynch, D. A. Goff, P. J. Schwab, W. B. Baker, T. Durduran, D. R. Busch, S. C. Nicolson, L. M. Montenegro, M. Y. Naim, R. Xiao, T. L. Spray, A. G. Yodh, J. W. Gaynor, and D. J. Licht, “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: Effects of surgical duration,” J. Thorac. Cardiovasc. Surg. 145(1), 196–205.e1 (2013).
[Crossref]

W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

Campbell, L. E.

D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. 75(9), 1855–1858 (1995).
[Crossref]

Carlson, B. M.

B. M. Carlson, “The Circulatory System,” in The Human Body 271–301 (2019).

Caro, C. G.

C. G. Caro, T. J. Pedley, and W. A. Schroter, The Mechanics of the Circulation (2012).

Carp, S. A.

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadžić, S. A. Carp, J. Lee, and D. A. Boas, “Measurement of shear-induced diffusion of red blood cells using dynamic light scattering-optical coherence tomography,” Proc. SPIE 10481, 57 (2018).
[Crossref]

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadzic, S. A. Carp, J. Lee, and D. A. Boas, “Shear-induced diffusion of red blood cells measured with dynamic light scattering-optical coherence tomography,” J. Biophotonics 11(2), e201700070 (2018).
[Crossref]

D. A. Boas, S. Sakadžic, J. Selb, P. Farzam, M. A. Franceschini, and S. A. Carp, “Establishing the diffuse correlation spectroscopy signal relationship with blood flow,” Neurophotonics 3(3), 031412 (2016).
[Crossref]

Carr, D.

S. Herold, M. Brozovic, J. Gibbs, A. A. Lammertsma, K. L. Leenders, D. Carr, J. S. Fleming, and T. Jones, “Measurement of regional cerebral blood flow, blood volume and oxygen metabolism in patients with sickle cell disease using positron emission tomography,” Stroke 17(4), 692–698 (1986).
[Crossref]

Cha, W.

W. Cha and R. L. Beissinger, “Evaluation of Shear-Induced Particle Diffusivity in Red Cell Ghosts Suspensions,” Korean J. Chem. Eng. 18(4), 479–485 (2001).
[Crossref]

Chen, Y.

M. E. Fields, K. P. Guilliams, D. K. Ragan, M. M. Binkley, C. Eldeniz, Y. Chen, M. L. Hulbert, R. C. McKinstry, J. S. Shimony, K. D. Vo, A. Doctor, H. An, A. L. Ford, and J.-M. Lee, “Regional oxygen extraction predicts border zone vulnerability to stroke in sickle cell disease,” Neurology (2018).

Cheng, H. H.

Choe, R.

T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010).
[Crossref]

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref]

M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
[Crossref]

T. Durduran, C. Zhou, B. L. Edlow, G. Yu, R. Choe, M. N. Kim, B. L. Cucchiara, M. E. Putt, Q. Shah, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Express 17(5), 3884 (2009).
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R. Choe, “Diffuse optical tomography and spectroscopy of breast cancer and fetal brain,” (2005).

Cicchetti, D. V.

D. V. Cicchetti, “Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology,” Psychol. Assess. 6(4), 284–290 (1994).
[Crossref]

Contini, D.

Cortese, L.

Cowdrick, K. R.

S. Y. Lee, K. R. Cowdrick, B. Sanders, E. Sathialingam, C. E. McCracken, W. A. Lam, C. H. Joiner, and E. M. Buckley, “Noninvasive optical assessment of resting-state cerebral blood flow in children with sickle cell disease,” Neurophotonics 6(03), 1 (2019).
[Crossref]

Cucchiara, B. L.

Cuccia, D. J.

F. Ayers, A. Grant, D. Kuo, D. J. Cuccia, and A. J. Durkin, “Fabrication and characterization of silicone-based tissue phantoms with tunable optical properties in the visible and near infrared domain,” in Design and Performance Validation of Phantoms Used in Conjunction with Optical Measurements of Tissue (2008), 6870, p. 687007.
[Crossref]

de Fraguier, S.

De Vivo, D. C.

I. Prohovnik, S. G. Pavlakis, S. Piomelli, J. Bello, J. P. Mohr, S. Hilal, and D. C. De Vivo, “Cerebral hyperemia, stroke, and transfusion in sickle cell disease,” Neurology 39(3), 344 (1989).
[Crossref]

Dehaes, M.

Detre, J. A.

M. T. Mullen, A. B. Parthasarathy, A. Zandieh, W. B. Baker, R. C. Mesquita, C. Loomis, J. Torres, W. Guo, C. G. Favilla, S. R. Messé, A. G. Yodh, J. A. Detre, and S. E. Kasner, “Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke,” J. Stroke Cerebrovasc. Dis. 28(11), 104294 (2019).
[Crossref]

C. G. Favilla, R. C. Mesquita, M. Mullen, T. Durduran, X. Lu, M. N. Kim, D. L. Minkoff, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Optical bedside monitoring of cerebral blood flow in acute ischemic stroke patients during head-of-bed manipulation,” Stroke 45(5), 1269–1274 (2014).
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M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
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M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
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M. E. Fields, K. P. Guilliams, D. K. Ragan, M. M. Binkley, C. Eldeniz, Y. Chen, M. L. Hulbert, R. C. McKinstry, J. S. Shimony, K. D. Vo, A. Doctor, H. An, A. L. Ford, and J.-M. Lee, “Regional oxygen extraction predicts border zone vulnerability to stroke in sickle cell disease,” Neurology (2018).

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J. Li, G. Dietsche, D. Iftime, S. Skipetrov, G. Maret, T. Rockstroh, and T. Gisler, “Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy,” J. Biomed. Opt. 10(4), 044002 (2005).
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J. J. Bishop, A. S. Popel, M. Intaglietta, and P. C. Johnson, “Effect of aggregation and shear rate on the dispersion of red blood cells flowing in venules,” Am. J. Physiol. - Hear. Circ. Physiol. 283(5), H1985–H1996 (2002).
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S. Y. Lee, K. R. Cowdrick, B. Sanders, E. Sathialingam, C. E. McCracken, W. A. Lam, C. H. Joiner, and E. M. Buckley, “Noninvasive optical assessment of resting-state cerebral blood flow in children with sickle cell disease,” Neurophotonics 6(03), 1 (2019).
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S. Herold, M. Brozovic, J. Gibbs, A. A. Lammertsma, K. L. Leenders, D. Carr, J. S. Fleming, and T. Jones, “Measurement of regional cerebral blood flow, blood volume and oxygen metabolism in patients with sickle cell disease using positron emission tomography,” Stroke 17(4), 692–698 (1986).
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M. T. Mullen, A. B. Parthasarathy, A. Zandieh, W. B. Baker, R. C. Mesquita, C. Loomis, J. Torres, W. Guo, C. G. Favilla, S. R. Messé, A. G. Yodh, J. A. Detre, and S. E. Kasner, “Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke,” J. Stroke Cerebrovasc. Dis. 28(11), 104294 (2019).
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C. G. Favilla, R. C. Mesquita, M. Mullen, T. Durduran, X. Lu, M. N. Kim, D. L. Minkoff, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Optical bedside monitoring of cerebral blood flow in acute ischemic stroke patients during head-of-bed manipulation,” Stroke 45(5), 1269–1274 (2014).
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T. Durduran, C. Zhou, B. L. Edlow, G. Yu, R. Choe, M. N. Kim, B. L. Cucchiara, M. E. Putt, Q. Shah, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Express 17(5), 3884 (2009).
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D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
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W. B. Baker, R. Balu, L. He, V. C. Kavuri, D. R. Busch, O. Amendolia, F. Quattrone, S. Frangos, E. Maloney-Wilensky, K. Abramson, E. Mahanna Gabrielli, A. G. Yodh, and W. Andrew Kofke, “Continuous non-invasive optical monitoring of cerebral blood flow and oxidative metabolism after acute brain injury,” J. Cereb. Blood Flow Metab. (2019).

Kim, M. N.

C. G. Favilla, R. C. Mesquita, M. Mullen, T. Durduran, X. Lu, M. N. Kim, D. L. Minkoff, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Optical bedside monitoring of cerebral blood flow in acute ischemic stroke patients during head-of-bed manipulation,” Stroke 45(5), 1269–1274 (2014).
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M. N. Kim, B. L. Edlow, T. Durduran, S. Frangos, R. C. Mesquita, J. M. Levine, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Continuous optical monitoring of cerebral hemodynamics during head-of-bed manipulation in brain-injured adults,” Neurocrit. Care 20(3), 443–453 (2014).
[Crossref]

M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
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T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010).
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T. Durduran, C. Zhou, B. L. Edlow, G. Yu, R. Choe, M. N. Kim, B. L. Cucchiara, M. E. Putt, Q. Shah, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Express 17(5), 3884 (2009).
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M. Tsai, A. Kita, J. Leach, R. Rounsevell, J. N. Huang, J. Moake, R. E. Ware, D. A. Fletcher, and W. A. Lam, “In vitro modeling of the microvascular occlusion and thrombosis that occur in hematologic diseases using microfluidic technology,” J. Clin. Invest. 122(1), 408–418 (2012).
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D. R. Myers, Y. Sakurai, R. Tran, B. Ahn, E. T. Hardy, R. Mannino, A. Kita, M. Tsai, and W. A. Lam, “Endothelialized Microfluidics for Studying Microvascular Interactions in Hematologic Diseases,” J. Visualized Exp. 643958(64), 3958 (2012).
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Kofke, W. A.

D. R. Busch, R. Balu, W. B. Baker, W. Guo, L. He, M. Diop, D. Milej, V. Kavuri, O. Amendolia, K. St. Lawrence, A. G. Yodh, and W. A. Kofke, “Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy,” Neurocrit. Care 30(1), 72–80 (2019).
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M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
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T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med. 15(2), 155–163 (2016).
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Lam, W. A.

S. Y. Lee, K. R. Cowdrick, B. Sanders, E. Sathialingam, C. E. McCracken, W. A. Lam, C. H. Joiner, and E. M. Buckley, “Noninvasive optical assessment of resting-state cerebral blood flow in children with sickle cell disease,” Neurophotonics 6(03), 1 (2019).
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M. Tsai, A. Kita, J. Leach, R. Rounsevell, J. N. Huang, J. Moake, R. E. Ware, D. A. Fletcher, and W. A. Lam, “In vitro modeling of the microvascular occlusion and thrombosis that occur in hematologic diseases using microfluidic technology,” J. Clin. Invest. 122(1), 408–418 (2012).
[Crossref]

D. R. Myers, Y. Sakurai, R. Tran, B. Ahn, E. T. Hardy, R. Mannino, A. Kita, M. Tsai, and W. A. Lam, “Endothelialized Microfluidics for Studying Microvascular Interactions in Hematologic Diseases,” J. Visualized Exp. 643958(64), 3958 (2012).
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Lammertsma, A. A.

S. Herold, M. Brozovic, J. Gibbs, A. A. Lammertsma, K. L. Leenders, D. Carr, J. S. Fleming, and T. Jones, “Measurement of regional cerebral blood flow, blood volume and oxygen metabolism in patients with sickle cell disease using positron emission tomography,” Stroke 17(4), 692–698 (1986).
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V. Jain, E. M. Buckley, D. J. Licht, J. M. Lynch, P. J. Schwab, M. Y. Naim, N. A. Lavin, S. C. Nicolson, L. M. Montenegro, A. G. Yodh, and F. W. Wehrli, “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab. 34(3), 380–388 (2014).
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E. M. Buckley, M. Y. Naim, J. M. Lynch, D. A. Goff, P. J. Schwab, L. K. Diaz, S. C. Nicolson, L. M. Montenegro, N. A. Lavin, T. Durduran, T. L. Spray, J. W. Gaynor, M. E. Putt, A. G. Yodh, M. A. Fogel, and D. J. Licht, “Sodium bicarbonate causes dose-dependent increases in cerebral blood flow in infants and children with single-ventricle physiology,” Pediatr. Res. 73(5), 668–673 (2013).
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Leach, J.

M. Tsai, A. Kita, J. Leach, R. Rounsevell, J. N. Huang, J. Moake, R. E. Ware, D. A. Fletcher, and W. A. Lam, “In vitro modeling of the microvascular occlusion and thrombosis that occur in hematologic diseases using microfluidic technology,” J. Clin. Invest. 122(1), 408–418 (2012).
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Lee, J.

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadzic, S. A. Carp, J. Lee, and D. A. Boas, “Shear-induced diffusion of red blood cells measured with dynamic light scattering-optical coherence tomography,” J. Biophotonics 11(2), e201700070 (2018).
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J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadžić, S. A. Carp, J. Lee, and D. A. Boas, “Measurement of shear-induced diffusion of red blood cells using dynamic light scattering-optical coherence tomography,” Proc. SPIE 10481, 57 (2018).
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Lee, J.-M.

M. E. Fields, K. P. Guilliams, D. K. Ragan, M. M. Binkley, C. Eldeniz, Y. Chen, M. L. Hulbert, R. C. McKinstry, J. S. Shimony, K. D. Vo, A. Doctor, H. An, A. L. Ford, and J.-M. Lee, “Regional oxygen extraction predicts border zone vulnerability to stroke in sickle cell disease,” Neurology (2018).

Lee, S. Y.

S. Y. Lee, K. R. Cowdrick, B. Sanders, E. Sathialingam, C. E. McCracken, W. A. Lam, C. H. Joiner, and E. M. Buckley, “Noninvasive optical assessment of resting-state cerebral blood flow in children with sickle cell disease,” Neurophotonics 6(03), 1 (2019).
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E. Sathialingam, S. Y. Lee, B. Sanders, J. Park, C. McCracken, L. Bryan, and E. M. Buckley, “Small separation diffuse correlation spectroscopy for measurement of cerebral blood flow in rodents,” Biomed. Opt. Express 9(11), 5719–5734 (2018).
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S. Herold, M. Brozovic, J. Gibbs, A. A. Lammertsma, K. L. Leenders, D. Carr, J. S. Fleming, and T. Jones, “Measurement of regional cerebral blood flow, blood volume and oxygen metabolism in patients with sickle cell disease using positron emission tomography,” Stroke 17(4), 692–698 (1986).
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Levine, J. M.

M. N. Kim, B. L. Edlow, T. Durduran, S. Frangos, R. C. Mesquita, J. M. Levine, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Continuous optical monitoring of cerebral hemodynamics during head-of-bed manipulation in brain-injured adults,” Neurocrit. Care 20(3), 443–453 (2014).
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M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
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Li, B.

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadzic, S. A. Carp, J. Lee, and D. A. Boas, “Shear-induced diffusion of red blood cells measured with dynamic light scattering-optical coherence tomography,” J. Biophotonics 11(2), e201700070 (2018).
[Crossref]

J. Tang, S. E. Erdener, B. Li, B. Fu, S. Sakadžić, S. A. Carp, J. Lee, and D. A. Boas, “Measurement of shear-induced diffusion of red blood cells using dynamic light scattering-optical coherence tomography,” Proc. SPIE 10481, 57 (2018).
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Li, J.

J. Li, G. Dietsche, D. Iftime, S. Skipetrov, G. Maret, T. Rockstroh, and T. Gisler, “Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy,” J. Biomed. Opt. 10(4), 044002 (2005).
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Li, M. Y.

T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med. 15(2), 155–163 (2016).
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Licht, D. J.

J. M. Lynch, T. Ko, D. R. Busch, J. J. Newland, M. E. Winters, K. Mensah-Brown, T. W. Boorady, R. Xiao, S. C. Nicolson, L. M. Montenegro, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Preoperative cerebral hemodynamics from birth to surgery in neonates with critical congenital heart disease,” J. Thorac. Cardiovasc. Surg. 156(4), 1657–1664 (2018).
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J. M. Lynch, E. M. Buckley, P. J. Schwab, A. L. McCarthy, M. E. Winters, D. R. Busch, R. Xiao, D. A. Goff, S. C. Nicolson, L. M. Montenegro, S. Fuller, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Time to surgery and preoperative cerebral hemodynamics predict postoperative white matter injury in neonates with hypoplastic left heart syndrome,” J. Thorac. Cardiovasc. Surg. 148(5), 2181–2188 (2014).
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V. Jain, E. M. Buckley, D. J. Licht, J. M. Lynch, P. J. Schwab, M. Y. Naim, N. A. Lavin, S. C. Nicolson, L. M. Montenegro, A. G. Yodh, and F. W. Wehrli, “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab. 34(3), 380–388 (2014).
[Crossref]

E. M. Buckley, J. M. Lynch, D. A. Goff, P. J. Schwab, W. B. Baker, T. Durduran, D. R. Busch, S. C. Nicolson, L. M. Montenegro, M. Y. Naim, R. Xiao, T. L. Spray, A. G. Yodh, J. W. Gaynor, and D. J. Licht, “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: Effects of surgical duration,” J. Thorac. Cardiovasc. Surg. 145(1), 196–205.e1 (2013).
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E. M. Buckley, M. Y. Naim, J. M. Lynch, D. A. Goff, P. J. Schwab, L. K. Diaz, S. C. Nicolson, L. M. Montenegro, N. A. Lavin, T. Durduran, T. L. Spray, J. W. Gaynor, M. E. Putt, A. G. Yodh, M. A. Fogel, and D. J. Licht, “Sodium bicarbonate causes dose-dependent increases in cerebral blood flow in infants and children with single-ventricle physiology,” Pediatr. Res. 73(5), 668–673 (2013).
[Crossref]

T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010).
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J. M. Lynch, T. Ko, D. R. Busch, J. J. Newland, M. E. Winters, K. Mensah-Brown, T. W. Boorady, R. Xiao, S. C. Nicolson, L. M. Montenegro, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Preoperative cerebral hemodynamics from birth to surgery in neonates with critical congenital heart disease,” J. Thorac. Cardiovasc. Surg. 156(4), 1657–1664 (2018).
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J. M. Lynch, E. M. Buckley, P. J. Schwab, A. L. McCarthy, M. E. Winters, D. R. Busch, R. Xiao, D. A. Goff, S. C. Nicolson, L. M. Montenegro, S. Fuller, J. W. Gaynor, T. L. Spray, A. G. Yodh, M. Y. Naim, and D. J. Licht, “Time to surgery and preoperative cerebral hemodynamics predict postoperative white matter injury in neonates with hypoplastic left heart syndrome,” J. Thorac. Cardiovasc. Surg. 148(5), 2181–2188 (2014).
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E. M. Buckley, A. B. Parthasarathy, P. E. Grant, A. G. Yodh, and M. A. Franceschini, “Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects,” Neurophotonics 1(1), 011009 (2014).
[Crossref]

T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” NeuroImage 85 Pt 1(01), 51–63 (2014).
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T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010).
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T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
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Yu, G.

M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, H. E. Moss, C. Zhou, G. Yu, R. Choe, E. Maloney-Wilensky, R. L. Wolf, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care 12(2), 173–180 (2010).
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M. T. Mullen, A. B. Parthasarathy, A. Zandieh, W. B. Baker, R. C. Mesquita, C. Loomis, J. Torres, W. Guo, C. G. Favilla, S. R. Messé, A. G. Yodh, J. A. Detre, and S. E. Kasner, “Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke,” J. Stroke Cerebrovasc. Dis. 28(11), 104294 (2019).
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S. Zanfardino and K. Vishwanath, “Sensitivity of diffuse correlation spectroscopy to flow rates: a study with tissue simulating optical phantoms,” in Medical Imaging 2018: Physics of Medical Imaging (SPIE, 2018), 10573, p. 91.

Zanoletti, M.

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[Crossref]

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Figures (6)

Fig. 1.
Fig. 1. In vitro DCS microfluidic phantom. Schematic representation of the (a) side and (b) top view of the microfluidic tissue-simulating phantom. The phantom is comprised of a layer of hundreds of microchannels with height, h, embedded 0.3 cm below the surface of a polydimethylsiloxane (PDMS), titanium dioxide (TiO2), and India Ink substrate. Intralipid or blood is flowed at a known rate using a standard syringe pump. A 1 cm source-detector separation DCS sensor is placed at the surface the device, and the 3 layer slab solution to the correlation diffusion equation is used to extract an average flow index within the microfluidic layer (c) Image of the experimental setup represented in (a) and (b). (d) Image of the microfluidic capillary network in a transparent PDMS substrate to visualize the channel architecture.
Fig. 2.
Fig. 2. Verification with Intralipid. (a) Representative ${g_2}(\tau )$ data measured when flowing Intralipid through the 30 × 28 µm microfluidic phantom. The gray dashed line represents the cutoff used to fit the data to minimize the effects of non-ergodicity. The convective model (red) resulted in the lower sum of squares (SS) fit residual than the diffusive model (blue). (b-c) The DCS-measured velocity (vmeas) versus the known average velocity (vavg) in the (b) 30 × 28 µm microchannels for five concentrations of Intralipid (7-20%, all R2>0.99, CCC>0.95, and p<0.01) and (c) in four different microchannel phantoms using 20% Intralipid (10-100 μm, all R2>0.98, CCC>0.95, and p<0.001). In these subplots, the black dashed line denotes the line of unity, and the colored dots are the mean/standard error values across 36 frames of data.
Fig. 3.
Fig. 3. Verification with whole blood. (a) Representative ${g_2}(\tau )$ curve measured when flowing whole blood (hematocrit = 45%) through the microfluidic phantom. The gray dashed line represents the cutoff used when fitting the data to minimize the effects of non-ergodicity. The diffusive model (blue) resulted in the lower sum of squares (SS) fit residual than the convective model (red). (b-c) Mean/standard error DCS-measured blood flow index (BFI) versus b) the known average velocity (vavg) and c) the known flow rate in the microchannels for 30×28 µm (n=4) and 100×85 µm (n=3) vessel sizes (salmon pink and green, respectively).
Fig. 4.
Fig. 4. Effects of hematocrit. DCS-measured blood flow index (BFI) versus hematocrit at five fixed flow velocities for (a) 30×28 µm and (b) 100×85 µm microchannels. (c-d) The DCS-measured spatially-weighted average diffusion coefficient ($\left\langle D \right\rangle = BFI/{P_2}$), where P2 is the known probability of scattering off of a moving red blood cell within the microfluidic layer) versus hematocrit across five fixed velocities for (c) 30×28 µm (n=4) and (d) 100×85 µm (n=3) microchannels. All data are reported as mean/standard error across 3-4 blood samples; solid lines connect data points obtained at a fixed flow velocity.
Fig. 5.
Fig. 5. Correcting for the effects of hematocrit. The hematocrit-corrected diffusion coefficient (${\left\langle D \right\rangle _{Hct - corrected}}$) versus hematocrit for (a) 30×28 µm (n=4) and (b) 100×85 µm (n=3) microchannels. All data are reported as mean/standard error across 3-4 blood samples; dashed lines connect data points obtained at a fixed flow velocity.
Fig. 6.
Fig. 6. Effect of sensor orientation and measurement repeatability. (a) The DCS-measured velocity (vmeas) versus the known average velocity (vavg) of 20% Intralipid in the 30×28 µm microchannel phantom device when the sensor was positioned parallel (magenta) or perpendicular (brown) to the channel orientation. Data are plotted as mean and standard error across 36 frames of data. (b) The DCS-measured blood flow index (BFI) versus the known average velocity of blood obtained from a healthy volunteer and diluted with PBS to 20% (blue) and 30% hematocrit (orange). Data are reported as mean and standard error across two repetitions of this experiment from the same donor, spaced nine months apart.

Equations (4)

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G 1 ( ρ , τ ) = 1 2 π 0 G ~ 1 0 ( s , τ ) s J 0 ( s ρ ) d s
G ~ 1 0 ( s , τ ) = numerator denominator ,
numerator =  ( β 1 D 1 cosh ( β 1 ( Δ 1 z s ) ) ( β 2 D 2 cosh ( β 2 Δ 2 ) + β 3 D 3 sinh ( β 2 Δ 2 ) ) + β 2 D 2 ( β 3 D 3 cosh ( β 2 Δ 2 ) + β 2 D 2 sinh ( β 2 Δ 2 ) ) sinh ( β 1 ( Δ 1 z s ) ) )
denominator =  β 2 D 2 cosh ( β 2 Δ 2 ) ( β 1 ( D 1 + β 3 D 3 z 0 ) cosh ( β 1 Δ 1 ) + ( β 3 D 3 + β 1 2 D 1 z 0 ) sinh ( β 1 Δ 1 ) ) + ( β 1 ( β 3 D 1 D 3 + β 2 2 D 2 2 z 0 ) cosh ( β 1 Δ 1 ) + ( β 2 2 D 2 2 + β 1 2 β 3 D 1 D 3 z 0 ) sinh ( β 1 Δ 1 ) ) × sinh ( β 2 Δ 2 ) .

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