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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 2, Iss. 7 — Jul. 1, 2011
  • pp: 2047–2054
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Due to intravascular multiple sequential scattering, Diffuse Correlation Spectroscopy of tissue primarily measures relative red blood cell motion within vessels

Stefan A. Carp, Nadàege Roche-Labarbe, Maria-Angela Franceschini, Vivek J. Srinivasan, Sava Sakadžić, and David A. Boas  »View Author Affiliations


Biomedical Optics Express, Vol. 2, Issue 7, pp. 2047-2054 (2011)
http://dx.doi.org/10.1364/BOE.2.002047


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Abstract

We suggest that Diffuse Correlation Spectroscopy (DCS) measurements of tissue blood flow primarily probe relative red blood cell (RBC) motion, due to the occurrence of multiple sequential scattering events within blood vessels. The magnitude of RBC shear-induced diffusion is known to correlate with flow velocity, explaining previous reports of linear scaling of the DCS “blood flow index” with tissue perfusion despite the observed diffusion-like auto-correlation decay. Further, by modeling RBC mean square displacement using a formulation that captures the transition from ballistic to diffusive motion, we improve the fit to experimental data and recover effective diffusion coefficients and velocity de-correlation time scales in the range expected from previous blood rheology studies.

© 2011 OSA

1. Introduction

Light scattering methods have been used to probe the motion of suspended particles for the last several decades, in either single [1

1. B. J. Berne and R. Pecora, Dynamic Light Scattering : with Applications to Chemistry, Biology, and Physics, Dover Ed. (Dover Publications, 2000).

] or multiple scattering regimes [2

2. D. J. Pine, D. A. Weitz, P. M. Chaikin, and E. Herbolzheimer, “Diffusing wave spectroscopy,” Phys. Rev. Lett. 60, 1134–1137 (1988). [CrossRef] [PubMed]

]. The latter technique, known as diffusing wave spectroscopy (DWS) has been extended to heterogeneous multiple-scattering media by Boas et al. [3

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

, 4

4. D. A. Boas and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A 14, 192–215 (1997). [CrossRef]

] and has gained acceptance as a method to measure perfusion in bulk tissue under the name of Diffuse Correlation Spectroscopy (DCS) [5

5. C. Cheung, J. P. Culver, K. Takahashi, J. H. Greenberg, and A. G. Yodh, “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol. 46, 2053–2065 (2001). [CrossRef] [PubMed]

]. By measuring the intensity fluctuations of light diffusely reflected from tissue, DCS can offer a measure of microvascular blood flow and has been successfully validated against other blood flow measurement techniques, such as arterial spin labeling (ASL) magnetic resonance imaging (MRI) [6

6. 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, 037004 (2010). [CrossRef] [PubMed]

8

8. G. Yu, T. F. Floyd, T. Durduran, C. Zhou, J. Wang, J. A. Detre, and A. G. Yodh, “Validation of diffuse correlation spectroscopy for muscle blood flow with concurrent arterial spin labeled perfusion MRI,” Opt. Express 15, 1064–1075 (2007). [CrossRef] [PubMed]

], Doppler Ultrasound [9

9. E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Schultz, C. M. Sehgal, D. J. Licht, P. H. Arger, M. E. Putt, H. H. Hurt, and A. G. Yodh, “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Express 17, 12571–12581 (2009). [CrossRef] [PubMed]

, 10

10. N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Human Brain Mapp. 31, 341–352 (2009). [CrossRef]

], Xenon-CT [11

11. 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,” Neurocritical Care 12, 173–180 (2010). [CrossRef]

] and fluorescent microspheres [12

12. C. Zhou, S. A. Eucker, T. Durduran, G. Yu, J. Ralston, S. H. Friess, R. N. Ichord, S. S. Margulies, and A. G. Yodh, “Diffuse optical monitoring of hemodynamic changes in piglet brain with closed head injury,” J. Biomed. Opt. 14, 034015 (2009). [CrossRef] [PubMed]

]. Given the three dimensional micro-topography of vasculature, red blood cell motion has been expected to have the characteristics of ballistic random flow with a uniform spatial velocity distribution [13

13. R. Bonner and R. Nossal, “Model for laser Doppler measurements of blood flow in tissue,” Appl. Opt. 20, 2097–2107 (1981). [CrossRef] [PubMed]

]. Surprisingly though, the good agreement seen in the validation studies cited above requires modeling particle movement as a diffusion random-walk. Two recent studies have probed the characteristics of DCS signals more deeply, looking at whether the pulsatile nature of blood flow and/or the presence of extravascular tissue shearing contribute to the observed diffusive scatterer dynamics. Using parallel detection of independent speckles Dietsche et al. [14

14. G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. 46, 8506–8514 (2007). [CrossRef] [PubMed]

] have measured the auto-correlation of light intensity fluctuations with 26 ms temporal resolution, and noted up to 240% variation in the auto-correlation decay time during one heart pulsation, as well as a somewhat stronger curvature of the decay curve at the systolic maximum flow compared to the diastolic minimum flow. While, this “super-diffusive” decay appears to have a slight ballistic flow quality, the data presented by the authors in Fig. 7 of Ref. [14

14. G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. 46, 8506–8514 (2007). [CrossRef] [PubMed]

] indicates the scatterer motion remains predominantly diffusive throughout the pulsation cycle. Further, Ninck et al. [15

15. M. Ninck, M. Untenberger, and T. Gisler, “Diffusing-wave spectroscopy with dynamic contrast variation: disentangling the effects of blood flow and extravascular tissue shearing on signals from deep tissue,” Biomed. Opt. Express 1, 1502–1513 (2010). [CrossRef]

], using an ex-vivo artificially perfused porcine kidney model has shown that, in the absence of blood, the DCS signal carries the signature of extravascular tissue shearing. Nevertheless, during pulsatile blood perfusion, the correlation decay curves are approximately described by diffusion even as they vary during the pulsation cycle because the contribution of extravascular tissue shearing is small. Taking into account the results of these studies, Ninck et al. [15

15. M. Ninck, M. Untenberger, and T. Gisler, “Diffusing-wave spectroscopy with dynamic contrast variation: disentangling the effects of blood flow and extravascular tissue shearing on signals from deep tissue,” Biomed. Opt. Express 1, 1502–1513 (2010). [CrossRef]

] conclude in their discussion that the discrepancy between expected red blood cell ballistic flow and diffusion dynamics measured by DCS remains unexplained, but perhaps DCS signals might reflect erythrocyte diffusion in the direction perpendicular to flow, as has been observed through particle tracking experiments [16

16. H. L. Goldsmith and J. Marlow, “Flow behavior of erythrocytes: II. particle motions in concentrated suspensions of ghost cells,” J. Colloid Interface Sci. 71, 383–407 (1979). [CrossRef]

], with a magnitude proportional to the flow shear rate.

In this paper we revisit the assumptions made in obtaining blood flow estimates from DCS data. In particular we argue that the occurrence of multiple sequential scattering within blood vessels would render the DCS measurements sensitive to relative red blood cell motions. As noted above, these motions are diffusive in nature, and their magnitude scales nearly linearly with blood flow velocity [16

16. H. L. Goldsmith and J. Marlow, “Flow behavior of erythrocytes: II. particle motions in concentrated suspensions of ghost cells,” J. Colloid Interface Sci. 71, 383–407 (1979). [CrossRef]

], in good agreement with published DCS studies. We also show that an effective hydrodynamic diffusion model capturing the transition between early ballistic and subsequent diffusive motion results in a modest, but significant improvement in the fit to experimental data. In addition to an effective diffusion coefficient proportional to blood flow velocity, this model also provides a measure of the particle velocity randomization time scale, a potentially useful tool for blood rheology studies addressing dynamics faster than the millisecond range currently accessible using video microscopy methods.

2. Methods

2.1. Dynamic light scattering in tissue

Red blood cell dynamics have been the subject of numerous blood rheology studies. As also noted by Ninck et al. [15

15. M. Ninck, M. Untenberger, and T. Gisler, “Diffusing-wave spectroscopy with dynamic contrast variation: disentangling the effects of blood flow and extravascular tissue shearing on signals from deep tissue,” Biomed. Opt. Express 1, 1502–1513 (2010). [CrossRef]

], video microscopy has been used ex vivo to track the motion of hemoglobin-depleted ghost RBCs [16

16. H. L. Goldsmith and J. Marlow, “Flow behavior of erythrocytes: II. particle motions in concentrated suspensions of ghost cells,” J. Colloid Interface Sci. 71, 383–407 (1979). [CrossRef]

] and whole blood [21

21. 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, e1000288 (2009). [CrossRef] [PubMed]

] in microchannels, as well as in vivo in rat venules [22

22. 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. Heart Circ. Physiol. 283, H1985–H1996 (2002).

]. It was found that RBCs undergo shear-induced displacements in the bulk flow frame of reference that can be characterized by an effective diffusion coefficient D eff on the order of 10−5 mm2/s, much higher than the Brownian diffusion coefficient expected for the RBCs in plasma ~ 5 × 10−8 mm2/s [22

22. 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. Heart Circ. Physiol. 283, H1985–H1996 (2002).

]. Most importantly, D eff appears to scale linearly with the shear rate. We postulate that this mechanism underlies the measurement of tissue blood flow using Diffuse Correlation Spectroscopy.

While the simplified Brownian displacement formulation used in DCS literature (〈Δr 2(τ)〉 = 6Dbτ) appears to work well, it assumes that the ballistic to random-walk hydrodynamic transition in the RBC diffusion occurs at time scales shorter than those probed by DCS measurements. Since there is no data to support this assumption, we remove it by using the Langevin formulation for RBC mean squared displacement [1

1. B. J. Berne and R. Pecora, Dynamic Light Scattering : with Applications to Chemistry, Biology, and Physics, Dover Ed. (Dover Publications, 2000).

]:
Δr2(τ)=6Deff(ττc(1exp(τ/τc)))
(2)
where D eff is the effective diffusion coefficient and τc is the time scale for the randomization of velocity vectors associated with RBC scattering events. By Taylor expanding Eq. (2), it can be shown that this formulation of the displacement term describes ballistic motion at short delay times, and diffusive motion at long delay times. Note that we are referring here to the short time scale ballistic motion contained within any diffusive process (including that of erythrocytes in the bulk flow frame of reference), and not to the bulk ballistic motion of erythrocytes in vasculature as seen in the laboratory frame of reference.

2.2. Experimental approach and data analysis

To evaluate the performance of the proposed model, we have applied it to the infant data previously reported by our group in Ref. [10

10. N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Human Brain Mapp. 31, 341–352 (2009). [CrossRef]

]. Briefly, 11 premature infants, between 28 and 34.5 weeks gestational age were measured several times at weekly intervals, for a total of 66 study visits. At each visit, measurements were obtained in up to seven areas of the head using a handheld multi-distance probe with source detector distances of 1–2.5 cm. Both frequency domain diffuse reflectance as well as diffuse correlation spectroscopy data was acquired. A frequency domain instrument emitting 110 MHz modulated light at 8 wavelengths between 659 and 825 nm was used to estimate absolute optical properties, while the DCS system employed a long-coherence length laser operating at 785 nm (CrystaLaser, Reno, NV, USA) and four photon-counting avalanche photodiodes (Perkin-Elmer, Quebec, Canada) connected to a digital auto-correlator operating at delay times from 200 ns to 1 s (Correlator.com, Bridgewater, NJ, USA). Additional experimental details can be found in Ref. [10

10. N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Human Brain Mapp. 31, 341–352 (2009). [CrossRef]

].

3. Results and discussion

Fig. 1 Comparison of data fit errors using the Brownian diffusion (dotted, FVU=0.48%), hydrodynamic diffusion (solid, FVU=0.04%) and random flow (dashed, FVU=0.76%) mean square displacement models.

When Eq. (2) is substituted into Eqs. (3) and (4), a product forms between the probability of scattering from a red blood cell α and the diffusion coefficient. This quantity has been used as a “blood flow index” in DCS studies, because the value of α generally cannot be estimated independently. Figure 2 shows a scatter plot of the αD eff vs. the αDb values obtained from each of our measurements. We observe an approximate relationship of αD eff = 1.07αDb + 2.1 × 10−7(mm2/s). α D eff has a substantially linear relationship with αDb, with a nearly-zero intercept, indicating both parameters can serve as relative blood flow indices, but αD eff is expected to provide a more accurate absolute measure. Encouragingly, by assuming α = 0.1 [23

23. A. G. Hudetz, “Blood flow in the cerebral capillary network: a review emphasizing observations with intravital microscopy,” Microcirculation 4, 233–252 (1997). [CrossRef] [PubMed]

] for brain tissue, our estimated effective diffusion coefficients fall within the same range as those determined from video microscopy blood flow studies (10−5 – 10−4 mm2/s) [16

16. H. L. Goldsmith and J. Marlow, “Flow behavior of erythrocytes: II. particle motions in concentrated suspensions of ghost cells,” J. Colloid Interface Sci. 71, 383–407 (1979). [CrossRef]

, 21

21. 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, e1000288 (2009). [CrossRef] [PubMed]

, 22

22. 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. Heart Circ. Physiol. 283, H1985–H1996 (2002).

].

Fig. 2 Scatter plot of αD eff vs. αDb across the entire data set. The dotted line indicates the diagonal of the plot (ratio=1).

With respect to τc, we observed a range between 0.06 and 7.7 μs, with an average of 1.3 μs (however values lower than ~ 0.4μ s are not reliable because of the limited time resolution of our correlator). A rough estimation of expected τc values may be obtained from hydrodynamic diffusion theory. For a rigid spherical particle the velocity decorrelation characteristic time is on the order of τv = ρa 2 / η, where a is the particle size, ρ is the fluid density and η is the fluid viscosity. Assuming a red blood cell diameter of a = 4 μm, η = 1.2 cP [22

22. 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. Heart Circ. Physiol. 283, H1985–H1996 (2002).

] and the density of water, τv = 13 μs, within an order of magnitude of our measurements. While the deformable nature of RBCs and the complexity of blood flow make this comparison less meaningful, our results do indicate the ballistic motion time scale (τ < τc) of the RBC hydrodynamic diffusion process is observable in most DCS measurements. Thus the full Eq. (2) should be used to reduce variance in the obtained blood flow velocity estimates and to characterize the diffusive transition time scale. To further characterize τc we plot in Fig. 3: a) τc vs. flow velocity, represented by αD eff and b) τc vs. inter-RBC distance (expected to be proportional to the inverse cube root of the blood hemoglobin concentration HGB −1/3), for all the measurements where R 2 of the hydrodynamic fit was greater than 0.999 (giving us confidence in the estimation of τc). We observe a weak but statistically significant decrease in τc with increased blood flow, as well a weak decrease with increased inter-particle distance that does not reach a p<0.01 significance level. The inverse proportionality between τc and D eff (and hence blood flow) could be explained as an acceleration of the interaction time scale. It is also expected from the short τ Taylor expansion of the mean square displacement expression (Eq. (2)): Δr2(τ)ττc=6(Deff/τc)τ2. Assuming the short τ ballistic displacement takes the form 〈Δr 2(τ)〉 = v 2 τ 2, the early ballistic velocity v is proportional to Deff/τc. As a first approximation, one could expect v to be fairly constant (i.e. dependent on blood viscosity and temperature, but not on the speed of the bulk flow), suggesting an inverse-proportional relationship between τc and D eff. A similar intuitive explanation is not apparent for the observed decrease in τc with increased inter-particle distance. This trend is likely due to complex blood flow mechanisms. Note though that τc is known to be affected by particle deformability in colloids [24

24. S. Roldan-Vargas, M. Pelaez-Fernandez, R. Barnadas-Rodriguez, M. Quesada-Perez, J. Estelrich, and J. Callejas-Fernandez, “Nondiffusive Brownian motion of deformable particles: breakdown of the “long-time tail”,” Phys. Rev. E 80, 021403 (2009). [CrossRef]

], thus it may become a useful tool to monitor RBC mechanical properties. Such changes can occur due to physiological and pathological mechanism, such as the fetal-to-adult hemoglobin replacement, and disease states such as sickle-cell anemia.

Fig. 3 Dependence of τc on physiological parameters. (a) dependence on the flow velocity, assumed to be proportional to αD eff ; (b) dependence on collision length scale, assumed to be inversely proportional to the cubed root of the hemoglobin concentration.

4. Conclusion

In conclusion we have provided a mechanistic explanation for the diffusion-like scatterer dynamics observed in diffuse correlation spectroscopy measurements of biological tissue based on multiple-scattering within individual blood vessels. We have also shown that experimental data is best fit with a hydrodynamic diffusion formulation that includes both ballistic flow at short times and diffusive motion at later times. Both the effective diffusion coefficients and the transition time scale are within an order of magnitude of expected values. Finally, our results imply that, in addition to tissue perfusion measurements, DCS may be a useful tool for blood rheology studies.

Acknowledgments

The authors acknowledge funding from NIH grants K99EB011889, R01HD042908, P41RR14075 and K99NS067050.

References and links

1.

B. J. Berne and R. Pecora, Dynamic Light Scattering : with Applications to Chemistry, Biology, and Physics, Dover Ed. (Dover Publications, 2000).

2.

D. J. Pine, D. A. Weitz, P. M. Chaikin, and E. Herbolzheimer, “Diffusing wave spectroscopy,” Phys. Rev. Lett. 60, 1134–1137 (1988). [CrossRef] [PubMed]

3.

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

4.

D. A. Boas and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A 14, 192–215 (1997). [CrossRef]

5.

C. Cheung, J. P. Culver, K. Takahashi, J. H. Greenberg, and A. G. Yodh, “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol. 46, 2053–2065 (2001). [CrossRef] [PubMed]

6.

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, 037004 (2010). [CrossRef] [PubMed]

7.

S. A. Carp, G. P. Dai, D. A. Boas, M. A. Franceschini, and Y. R. Kim, “Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring,” Biomed. Opt. Express 1, 553–565 (2010). [CrossRef]

8.

G. Yu, T. F. Floyd, T. Durduran, C. Zhou, J. Wang, J. A. Detre, and A. G. Yodh, “Validation of diffuse correlation spectroscopy for muscle blood flow with concurrent arterial spin labeled perfusion MRI,” Opt. Express 15, 1064–1075 (2007). [CrossRef] [PubMed]

9.

E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Schultz, C. M. Sehgal, D. J. Licht, P. H. Arger, M. E. Putt, H. H. Hurt, and A. G. Yodh, “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Express 17, 12571–12581 (2009). [CrossRef] [PubMed]

10.

N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Human Brain Mapp. 31, 341–352 (2009). [CrossRef]

11.

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,” Neurocritical Care 12, 173–180 (2010). [CrossRef]

12.

C. Zhou, S. A. Eucker, T. Durduran, G. Yu, J. Ralston, S. H. Friess, R. N. Ichord, S. S. Margulies, and A. G. Yodh, “Diffuse optical monitoring of hemodynamic changes in piglet brain with closed head injury,” J. Biomed. Opt. 14, 034015 (2009). [CrossRef] [PubMed]

13.

R. Bonner and R. Nossal, “Model for laser Doppler measurements of blood flow in tissue,” Appl. Opt. 20, 2097–2107 (1981). [CrossRef] [PubMed]

14.

G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. 46, 8506–8514 (2007). [CrossRef] [PubMed]

15.

M. Ninck, M. Untenberger, and T. Gisler, “Diffusing-wave spectroscopy with dynamic contrast variation: disentangling the effects of blood flow and extravascular tissue shearing on signals from deep tissue,” Biomed. Opt. Express 1, 1502–1513 (2010). [CrossRef]

16.

H. L. Goldsmith and J. Marlow, “Flow behavior of erythrocytes: II. particle motions in concentrated suspensions of ghost cells,” J. Colloid Interface Sci. 71, 383–407 (1979). [CrossRef]

17.

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

18.

M. Meinke, G. Muller, J. Helfmann, and M. Friebel, “Empirical model functions to calculate hematocrit-dependent optical properties of human blood,” Appl. Opt. 46, 1742–1753 (2007). [CrossRef] [PubMed]

19.

C. Desjardins and B. R. Duling, “Microvessel hematocrit–measurement and implications for capillary oxygen-transport,” Am. J. Physiol. 252, H494–H503 (1987).

20.

T. Q. Duong and S. G. Kim, “In vivo MR measurements of regional arterial and venous blood volume fractions in intact rat brain,” Magn. Res. Med. 43, 393–402 (2000). [CrossRef]

21.

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, e1000288 (2009). [CrossRef] [PubMed]

22.

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. Heart Circ. Physiol. 283, H1985–H1996 (2002).

23.

A. G. Hudetz, “Blood flow in the cerebral capillary network: a review emphasizing observations with intravital microscopy,” Microcirculation 4, 233–252 (1997). [CrossRef] [PubMed]

24.

S. Roldan-Vargas, M. Pelaez-Fernandez, R. Barnadas-Rodriguez, M. Quesada-Perez, J. Estelrich, and J. Callejas-Fernandez, “Nondiffusive Brownian motion of deformable particles: breakdown of the “long-time tail”,” Phys. Rev. E 80, 021403 (2009). [CrossRef]

OCIS Codes
(170.0170) Medical optics and biotechnology : Medical optics and biotechnology
(170.1470) Medical optics and biotechnology : Blood or tissue constituent monitoring
(170.3340) Medical optics and biotechnology : Laser Doppler velocimetry
(170.6480) Medical optics and biotechnology : Spectroscopy, speckle

ToC Category:
Noninvasive Optical Diagnostics

History
Original Manuscript: April 4, 2011
Revised Manuscript: June 13, 2011
Manuscript Accepted: June 17, 2011
Published: June 24, 2011

Citation
Stefan A. Carp, Nadàege Roche-Labarbe, Maria-Angela Franceschini, Vivek J. Srinivasan, Sava Sakadžić, and David A. Boas, "Due to intravascular multiple sequential scattering, Diffuse Correlation Spectroscopy of tissue primarily measures relative red blood cell motion within vessels," Biomed. Opt. Express 2, 2047-2054 (2011)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-2-7-2047


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References

  1. B. J. Berne and R. Pecora, Dynamic Light Scattering : with Applications to Chemistry, Biology, and Physics , Dover Ed. (Dover Publications, 2000).
  2. D. J. Pine, D. A. Weitz, P. M. Chaikin, and E. Herbolzheimer, “Diffusing wave spectroscopy,” Phys. Rev. Lett. 60, 1134–1137 (1988). [CrossRef] [PubMed]
  3. D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. 75, 1855–1858 (1995). [CrossRef] [PubMed]
  4. D. A. Boas and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A 14, 192–215 (1997). [CrossRef]
  5. C. Cheung, J. P. Culver, K. Takahashi, J. H. Greenberg, and A. G. Yodh, “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol. 46, 2053–2065 (2001). [CrossRef] [PubMed]
  6. 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, 037004 (2010). [CrossRef] [PubMed]
  7. S. A. Carp, G. P. Dai, D. A. Boas, M. A. Franceschini, and Y. R. Kim, “Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring,” Biomed. Opt. Express 1, 553–565 (2010). [CrossRef]
  8. G. Yu, T. F. Floyd, T. Durduran, C. Zhou, J. Wang, J. A. Detre, and A. G. Yodh, “Validation of diffuse correlation spectroscopy for muscle blood flow with concurrent arterial spin labeled perfusion MRI,” Opt. Express 15, 1064–1075 (2007). [CrossRef] [PubMed]
  9. E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Schultz, C. M. Sehgal, D. J. Licht, P. H. Arger, M. E. Putt, H. H. Hurt, and A. G. Yodh, “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Express 17, 12571–12581 (2009). [CrossRef] [PubMed]
  10. N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Human Brain Mapp. 31, 341–352 (2009). [CrossRef]
  11. 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,” Neurocritical Care 12, 173–180 (2010). [CrossRef]
  12. C. Zhou, S. A. Eucker, T. Durduran, G. Yu, J. Ralston, S. H. Friess, R. N. Ichord, S. S. Margulies, and A. G. Yodh, “Diffuse optical monitoring of hemodynamic changes in piglet brain with closed head injury,” J. Biomed. Opt. 14, 034015 (2009). [CrossRef] [PubMed]
  13. R. Bonner and R. Nossal, “Model for laser Doppler measurements of blood flow in tissue,” Appl. Opt. 20, 2097–2107 (1981). [CrossRef] [PubMed]
  14. G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. 46, 8506–8514 (2007). [CrossRef] [PubMed]
  15. M. Ninck, M. Untenberger, and T. Gisler, “Diffusing-wave spectroscopy with dynamic contrast variation: disentangling the effects of blood flow and extravascular tissue shearing on signals from deep tissue,” Biomed. Opt. Express 1, 1502–1513 (2010). [CrossRef]
  16. H. L. Goldsmith and J. Marlow, “Flow behavior of erythrocytes: II. particle motions in concentrated suspensions of ghost cells,” J. Colloid Interface Sci. 71, 383–407 (1979). [CrossRef]
  17. T. Durduran, R. Choe, W. Baker, and A. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73, 076701 (2010). [CrossRef]
  18. M. Meinke, G. Muller, J. Helfmann, and M. Friebel, “Empirical model functions to calculate hematocrit-dependent optical properties of human blood,” Appl. Opt. 46, 1742–1753 (2007). [CrossRef] [PubMed]
  19. C. Desjardins and B. R. Duling, “Microvessel hematocrit–measurement and implications for capillary oxygen-transport,” Am. J. Physiol. 252, H494–H503 (1987).
  20. T. Q. Duong and S. G. Kim, “In vivo MR measurements of regional arterial and venous blood volume fractions in intact rat brain,” Magn. Res. Med. 43, 393–402 (2000). [CrossRef]
  21. 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, e1000288 (2009). [CrossRef] [PubMed]
  22. 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. Heart Circ. Physiol. 283, H1985–H1996 (2002).
  23. A. G. Hudetz, “Blood flow in the cerebral capillary network: a review emphasizing observations with intravital microscopy,” Microcirculation 4, 233–252 (1997). [CrossRef] [PubMed]
  24. S. Roldan-Vargas, M. Pelaez-Fernandez, R. Barnadas-Rodriguez, M. Quesada-Perez, J. Estelrich, and J. Callejas-Fernandez, “Nondiffusive Brownian motion of deformable particles: breakdown of the “long-time tail”,” Phys. Rev. E 80, 021403 (2009). [CrossRef]

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