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

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

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).
[Crossref]

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.
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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).
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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).
[Crossref]

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).
[Crossref]

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).
[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).
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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).
[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]

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

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Lee, S. Y.

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Li, B.

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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 ) .