Abstract

Millimeter-depth sensitivity with frequency domain near-infrared spectroscopy has been challenging due to the breakdown of the diffusion equation for source-detection separations < 1cm. To overcome this challenge, we employ a Monte-Carlo lookup table-based inverse algorithm to fit small separation (3-6 mm) frequency-domain near-infrared spectroscopy (FDNIRS) data for absorption and reduced scattering coefficients. We verify this small separation FDNIRS method through a series of in vitro and in vivo studies. In vitro, we observed a root mean squared percent error (RMSE) in estimation of the reduced scattering coefficient and absorption coefficient of 2.8% and 7.6%, respectively, in liquid phantoms consisting of Intralipid and Indian ink, and a RMSE in estimation of oxygen saturation and total hemoglobin concentrations of 7.8 and 11.2%, respectively, in blood-mixed liquid phantoms. Next, we demonstrate one particularly valuable in vivo application of this technique wherein we non-invasively measure the optical properties of the mouse brain (n = 4). We find that the measured resting state cerebral oxygen saturation and hemoglobin concentration are consistent with literature reported values, and we observe expected trends during a hyper-/hypoxia challenge that qualitatively mimic changes in partial pressure of oxygen (pO2) measured simultaneously with an invasive pO2 sensor. Further, through simulations of the mouse head geometry, we demonstrate that the skull and scalp exert minimal influence on the estimate oxygen saturation, while leading to small but systematic underestimation of total hemoglobin concentration. In total, these results demonstrate the robustness of small separation FDNIRS to assess tissue optical properties at millimeter depth resolution.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  39. S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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2018 (5)

S. Y. Lee and M. A. Mycek, “Hybrid Monte Carlo simulation with ray tracing for fluorescence measurements in turbid media,” Opt. Lett. 43(16), 3846–3849 (2018).
[Crossref]

C. K. Hayakawa, K. Karrobi, V. Pera, D. Roblyer, and V. Venugopalan, “Optical sampling depth in the spatial frequency domain,” J. Biomed. Opt. 24(07), 1–14 (2018).
[Crossref]

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 90(13), e1134–e1142 (2018).
[Crossref]

E. Sathialingam, S. Y. Lee, B. Sanders, J. Park, C. E. 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]

S. Dadgar, J. R. Troncoso, and N. Rajaram, “Optical spectroscopic sensing of tumor hypoxia,” J. Biomed. Opt. 23(06), 1 (2018).
[Crossref]

2017 (1)

S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
[Crossref]

2016 (3)

2014 (3)

G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (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]

A. Gupta, H. Baradaran, A. D. Schweitzer, H. Kamel, A. Pandya, D. Delgado, D. Wright, S. Hurtado-Rua, Y. Wang, and P. C. Sanelli, “Oxygen extraction fraction and stroke risk in patients with carotid stenosis or occlusion: a systematic review and meta-analysis,” Am. J. Neuroradiol. 35(2), 250–255 (2014).
[Crossref]

2013 (1)

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
[Crossref]

2012 (2)

D. K. Ragan, R. McKinstry, T. Benzinger, J. Leonard, and J. A. Pineda, “Depression of whole-brain oxygen extraction fraction is associated with poor outcome in pediatric traumatic brain injury,” Pediatr. Res. 71(2), 199–204 (2012).
[Crossref]

J. Sun, S. J. Lee, L. Wu, M. Sarntinoranont, and H. Xie, “Refractive index measurement of acute rat brain tissue slices using optical coherence tomography,” Opt. Express 20(2), 1084–1095 (2012).
[Crossref]

2011 (2)

A. J. Lin, M. A. Koike, K. N. Green, J. G. Kim, A. Mazhar, T. B. Rice, F. M. LaFerla, and B. J. Tromberg, “Spatial Frequency Domain Imaging of Intrinsic Optical Property Contrast in a Mouse Model of Alzheimer's Disease,” Ann. Biomed. Eng. 39(4), 1349–1357 (2011).
[Crossref]

B. Hallacoglu, A. Sassaroli, S. Fantini, and A. M. Troen, “Cerebral perfusion and oxygenation are impaired by folate deficiency in rat: absolute measurements with noninvasive near-infrared spectroscopy,” J. Cereb. Blood Flow Metab. 31(6), 1482–1492 (2011).
[Crossref]

2009 (3)

D. J. Cuccia, F. P. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009).
[Crossref]

S. H. Tseng, C. Hayakawa, J. Spanier, and A. J. Durkin, “Investigation of a probe design for facilitating the uses of the standard photon diffusion equation at short source-detector separations: Monte Carlo simulations,” J. Biomed. Opt. 14(5), 054043 (2009).
[Crossref]

C. Baltes and G. W. Faris, “Frequency domain measurements on turbid media with strong absorption using the PN approximation,” Appl. Opt. 48(16), 2991–3000 (2009).
[Crossref]

2008 (1)

E. A. Genina, A. N. Bashkatov, and V. V. Tuchin, “Optical Clearing of Cranial Bone,” Advances in Optical Technologies 2008, 1–8 (2008).
[Crossref]

2005 (2)

2004 (1)

J. Choi, M. Wolf, V. Toronov, U. Wolf, C. Polzonetti, D. Hueber, L. P. Safonova, R. Gupta, A. Michalos, W. Mantulin, and E. Gratton, “Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach,” J. Biomed. Opt. 9(1), 221–229 (2004).
[Crossref]

2003 (1)

S. Willmann, A. Terenji, J. Osterholtz, J. Meister, P. Hering, and H.-J. Schwarzmaier, “Small-volume frequency-domain oximetry: phantom experiments and first in vivo results,” J. Biomed. Opt. 8(4), 618–628 (2003).
[Crossref]

2002 (1)

S. Willmann, A. Terenji, J. Osterholz, H. J. Schwarzmaier, and P. Hering, “Absolute absorber quantification in turbid media at small source–detector separations,” Appl. Phys. B: Lasers Opt. 74(6), 589–595 (2002).
[Crossref]

1999 (2)

1998 (1)

N. Ramanujam, C. Du, H. Y. Ma, and B. Chance, “Sources of phase noise in homodyne and heterodyne phase modulation devices used for tissue oximetry studies,” Rev. Sci. Instrum. 69(8), 3042–3054 (1998).
[Crossref]

1997 (2)

A. Kienle and M. S. Patterson, “Improved solutions of the steady-state and the time-resolved diffusion equations for reflectance from a semi-infinite turbid medium,” J. Opt. Soc. Am. A 14(1), 246–254 (1997).
[Crossref]

A. Kienle and M. S. Patterson, “Determination of the optical properties of semi-infinite turbid media from frequency-domain reflectance close to the source,” Phys. Med. Biol. 42(9), 1801–1819 (1997).
[Crossref]

1995 (1)

S. Fantini, M. A. Franceschini, J. S. Maier, S. A. Walker, B. Barbieri, and E. Gratton, “Frequency-Domain Multichannel Optical-Detector for Noninvasive Tissue Spectroscopy and Oximetry,” Opt. Eng. 34(1), 32–42 (1995).
[Crossref]

1994 (1)

1993 (2)

C.-F. Cartheuser, “Standard and pH-affected hemoglobin-O2 binding curves of Sprague-Dawley rats under normal and shifted P50 conditions,” Comp. Biochem. Physiol., Part A: Mol. Integr. Physiol. 106(4), 775–782 (1993).
[Crossref]

M. Firbank, M. Hiraoka, M. Essenpreis, and D. T. Delpy, “Measurement of the optical properties of the skull in the wavelength range 650-950 nm,” Phys. Med. Biol. 38(4), 503–510 (1993).
[Crossref]

1989 (1)

I. K. L. Lawrence, “A Concordance Correlation Coefficient to Evaluate Reproducibility,” Biometrics 45(1), 255–268 (1989).
[Crossref]

1986 (1)

J. Martin Bland and D. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 327(8476), 307–310 (1986).
[Crossref]

1968 (1)

H. C. Agrawal, J. M. Davis, and W. A. Himwich, “Developmental changes in mouse brain: weight, water content and free amino acids,” J. Neurochem. 15(9), 917–923 (1968).
[Crossref]

Agrawal, H. C.

H. C. Agrawal, J. M. Davis, and W. A. Himwich, “Developmental changes in mouse brain: weight, water content and free amino acids,” J. Neurochem. 15(9), 917–923 (1968).
[Crossref]

Altman, D.

J. Martin Bland and D. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet 327(8476), 307–310 (1986).
[Crossref]

Altman, D. G.

J. M. Bland and D. G. Altman, “Measuring agreement in method comparison studies,” Stat. Methods Med. Res. 8(2), 135–160 (1999).
[Crossref]

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 90(13), e1134–e1142 (2018).
[Crossref]

Andreasson, K. I.

G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
[Crossref]

Andresen, B.

Antaris, A. L.

G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
[Crossref]

Atochin, D. N.

G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
[Crossref]

Ayers, F. R.

D. J. Cuccia, F. P. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009).
[Crossref]

Baltes, C.

Baradaran, H.

A. Gupta, H. Baradaran, A. D. Schweitzer, H. Kamel, A. Pandya, D. Delgado, D. Wright, S. Hurtado-Rua, Y. Wang, and P. C. Sanelli, “Oxygen extraction fraction and stroke risk in patients with carotid stenosis or occlusion: a systematic review and meta-analysis,” Am. J. Neuroradiol. 35(2), 250–255 (2014).
[Crossref]

Barbieri, B.

S. Fantini, M. A. Franceschini, J. S. Maier, S. A. Walker, B. Barbieri, and E. Gratton, “Frequency-Domain Multichannel Optical-Detector for Noninvasive Tissue Spectroscopy and Oximetry,” Opt. Eng. 34(1), 32–42 (1995).
[Crossref]

S. Fantini, M. A. Franceschini, J. B. Fishkin, B. Barbieri, and E. Gratton, “Quantitative determination of the absorption spectra of chromophores in strongly scattering media: a light-emitting-diode based technique,” Appl. Opt. 33(22), 5204–5213 (1994).
[Crossref]

Bashkatov, A. N.

E. A. Genina, A. N. Bashkatov, and V. V. Tuchin, “Optical Clearing of Cranial Bone,” Advances in Optical Technologies 2008, 1–8 (2008).
[Crossref]

Benzinger, T.

D. K. Ragan, R. McKinstry, T. Benzinger, J. Leonard, and J. A. Pineda, “Depression of whole-brain oxygen extraction fraction is associated with poor outcome in pediatric traumatic brain injury,” Pediatr. Res. 71(2), 199–204 (2012).
[Crossref]

Bevilacqua, F. P.

D. J. Cuccia, F. P. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009).
[Crossref]

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 90(13), e1134–e1142 (2018).
[Crossref]

Bland, J. M.

J. M. Bland and D. G. Altman, “Measuring agreement in method comparison studies,” Stat. Methods Med. Res. 8(2), 135–160 (1999).
[Crossref]

Bryan, L.

Buckley, E. M.

E. Sathialingam, S. Y. Lee, B. Sanders, J. Park, C. E. 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]

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]

Cartheuser, C.-F.

C.-F. Cartheuser, “Standard and pH-affected hemoglobin-O2 binding curves of Sprague-Dawley rats under normal and shifted P50 conditions,” Comp. Biochem. Physiol., Part A: Mol. Integr. Physiol. 106(4), 775–782 (1993).
[Crossref]

Chance, B.

N. Ramanujam, C. Du, H. Y. Ma, and B. Chance, “Sources of phase noise in homodyne and heterodyne phase modulation devices used for tissue oximetry studies,” Rev. Sci. Instrum. 69(8), 3042–3054 (1998).
[Crossref]

Chang, J.

G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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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 90(13), e1134–e1142 (2018).
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Choi, J.

J. Choi, M. Wolf, V. Toronov, U. Wolf, C. Polzonetti, D. Hueber, L. P. Safonova, R. Gupta, A. Michalos, W. Mantulin, and E. Gratton, “Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach,” J. Biomed. Opt. 9(1), 221–229 (2004).
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S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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D. J. Cuccia, F. P. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009).
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S. Dadgar, J. R. Troncoso, and N. Rajaram, “Optical spectroscopic sensing of tumor hypoxia,” J. Biomed. Opt. 23(06), 1 (2018).
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G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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H. C. Agrawal, J. M. Davis, and W. A. Himwich, “Developmental changes in mouse brain: weight, water content and free amino acids,” J. Neurochem. 15(9), 917–923 (1968).
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A. Gupta, H. Baradaran, A. D. Schweitzer, H. Kamel, A. Pandya, D. Delgado, D. Wright, S. Hurtado-Rua, Y. Wang, and P. C. Sanelli, “Oxygen extraction fraction and stroke risk in patients with carotid stenosis or occlusion: a systematic review and meta-analysis,” Am. J. Neuroradiol. 35(2), 250–255 (2014).
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M. Firbank, M. Hiraoka, M. Essenpreis, and D. T. Delpy, “Measurement of the optical properties of the skull in the wavelength range 650-950 nm,” Phys. Med. Biol. 38(4), 503–510 (1993).
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G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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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 90(13), e1134–e1142 (2018).
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N. Ramanujam, C. Du, H. Y. Ma, and B. Chance, “Sources of phase noise in homodyne and heterodyne phase modulation devices used for tissue oximetry studies,” Rev. Sci. Instrum. 69(8), 3042–3054 (1998).
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S. H. Tseng, C. Hayakawa, J. Spanier, and A. J. Durkin, “Investigation of a probe design for facilitating the uses of the standard photon diffusion equation at short source-detector separations: Monte Carlo simulations,” J. Biomed. Opt. 14(5), 054043 (2009).
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S. H. Tseng, C. Hayakawa, B. J. Tromberg, J. Spanier, and A. J. Durkin, “Quantitative spectroscopy of superficial turbid media,” Opt. Lett. 30(23), 3165–3167 (2005).
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M. Firbank, M. Hiraoka, M. Essenpreis, and D. T. Delpy, “Measurement of the optical properties of the skull in the wavelength range 650-950 nm,” Phys. Med. Biol. 38(4), 503–510 (1993).
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Farkas, D.

S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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Ford, A. L.

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 90(13), e1134–e1142 (2018).
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S. Fantini, M. A. Franceschini, J. S. Maier, S. A. Walker, B. Barbieri, and E. Gratton, “Frequency-Domain Multichannel Optical-Detector for Noninvasive Tissue Spectroscopy and Oximetry,” Opt. Eng. 34(1), 32–42 (1995).
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J. Choi, M. Wolf, V. Toronov, U. Wolf, C. Polzonetti, D. Hueber, L. P. Safonova, R. Gupta, A. Michalos, W. Mantulin, and E. Gratton, “Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach,” J. Biomed. Opt. 9(1), 221–229 (2004).
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S. Fantini, M. A. Franceschini, J. S. Maier, S. A. Walker, B. Barbieri, and E. Gratton, “Frequency-Domain Multichannel Optical-Detector for Noninvasive Tissue Spectroscopy and Oximetry,” Opt. Eng. 34(1), 32–42 (1995).
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S. Fantini, M. A. Franceschini, J. B. Fishkin, B. Barbieri, and E. Gratton, “Quantitative determination of the absorption spectra of chromophores in strongly scattering media: a light-emitting-diode based technique,” Appl. Opt. 33(22), 5204–5213 (1994).
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A. J. Lin, M. A. Koike, K. N. Green, J. G. Kim, A. Mazhar, T. B. Rice, F. M. LaFerla, and B. J. Tromberg, “Spatial Frequency Domain Imaging of Intrinsic Optical Property Contrast in a Mouse Model of Alzheimer's Disease,” Ann. Biomed. Eng. 39(4), 1349–1357 (2011).
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S. H. Tseng, C. Hayakawa, J. Spanier, and A. J. Durkin, “Investigation of a probe design for facilitating the uses of the standard photon diffusion equation at short source-detector separations: Monte Carlo simulations,” J. Biomed. Opt. 14(5), 054043 (2009).
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C. K. Hayakawa, K. Karrobi, V. Pera, D. Roblyer, and V. Venugopalan, “Optical sampling depth in the spatial frequency domain,” J. Biomed. Opt. 24(07), 1–14 (2018).
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S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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M. Firbank, M. Hiraoka, M. Essenpreis, and D. T. Delpy, “Measurement of the optical properties of the skull in the wavelength range 650-950 nm,” Phys. Med. Biol. 38(4), 503–510 (1993).
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G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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Huang, P. L.

G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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J. Choi, M. Wolf, V. Toronov, U. Wolf, C. Polzonetti, D. Hueber, L. P. Safonova, R. Gupta, A. Michalos, W. Mantulin, and E. Gratton, “Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach,” J. Biomed. Opt. 9(1), 221–229 (2004).
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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 90(13), e1134–e1142 (2018).
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A. Gupta, H. Baradaran, A. D. Schweitzer, H. Kamel, A. Pandya, D. Delgado, D. Wright, S. Hurtado-Rua, Y. Wang, and P. C. Sanelli, “Oxygen extraction fraction and stroke risk in patients with carotid stenosis or occlusion: a systematic review and meta-analysis,” Am. J. Neuroradiol. 35(2), 250–255 (2014).
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A. Gupta, H. Baradaran, A. D. Schweitzer, H. Kamel, A. Pandya, D. Delgado, D. Wright, S. Hurtado-Rua, Y. Wang, and P. C. Sanelli, “Oxygen extraction fraction and stroke risk in patients with carotid stenosis or occlusion: a systematic review and meta-analysis,” Am. J. Neuroradiol. 35(2), 250–255 (2014).
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C. K. Hayakawa, K. Karrobi, V. Pera, D. Roblyer, and V. Venugopalan, “Optical sampling depth in the spatial frequency domain,” J. Biomed. Opt. 24(07), 1–14 (2018).
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A. J. Lin, M. A. Koike, K. N. Green, J. G. Kim, A. Mazhar, T. B. Rice, F. M. LaFerla, and B. J. Tromberg, “Spatial Frequency Domain Imaging of Intrinsic Optical Property Contrast in a Mouse Model of Alzheimer's Disease,” Ann. Biomed. Eng. 39(4), 1349–1357 (2011).
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S. Y. Lee, J. M. Pakela, M. C. Helton, K. Vishwanath, Y. G. Chung, N. J. Kolodziejski, C. J. Stapels, D. R. McAdams, D. E. Fernandez, J. F. Christian, J. O’Reilly, D. Farkas, B. B. Ward, S. E. Feinberg, and M. A. Mycek, “Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps,” J. Biomed. Opt. 22(12), 1–14 (2017).
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G. Hong, S. Diao, J. Chang, A. L. Antaris, C. Chen, B. Zhang, S. Zhao, D. N. Atochin, P. L. Huang, K. I. Andreasson, C. J. Kuo, and H. Dai, “Through-skull fluorescence imaging of the brain in a new near-infrared window,” Nat. Photonics 8(9), 723–730 (2014).
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A. J. Lin, M. A. Koike, K. N. Green, J. G. Kim, A. Mazhar, T. B. Rice, F. M. LaFerla, and B. J. Tromberg, “Spatial Frequency Domain Imaging of Intrinsic Optical Property Contrast in a Mouse Model of Alzheimer's Disease,” Ann. Biomed. Eng. 39(4), 1349–1357 (2011).
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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 90(13), e1134–e1142 (2018).
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Lee, S. Y.

S. Y. Lee and M. A. Mycek, “Hybrid Monte Carlo simulation with ray tracing for fluorescence measurements in turbid media,” Opt. Lett. 43(16), 3846–3849 (2018).
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A. J. Lin, M. A. Koike, K. N. Green, J. G. Kim, A. Mazhar, T. B. Rice, F. M. LaFerla, and B. J. Tromberg, “Spatial Frequency Domain Imaging of Intrinsic Optical Property Contrast in a Mouse Model of Alzheimer's Disease,” Ann. Biomed. Eng. 39(4), 1349–1357 (2011).
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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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Ma, H. Y.

N. Ramanujam, C. Du, H. Y. Ma, and B. Chance, “Sources of phase noise in homodyne and heterodyne phase modulation devices used for tissue oximetry studies,” Rev. Sci. Instrum. 69(8), 3042–3054 (1998).
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Maier, J. S.

S. Fantini, M. A. Franceschini, J. S. Maier, S. A. Walker, B. Barbieri, and E. Gratton, “Frequency-Domain Multichannel Optical-Detector for Noninvasive Tissue Spectroscopy and Oximetry,” Opt. Eng. 34(1), 32–42 (1995).
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Advances in Optical Technologies (1)

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Am. J. Neuroradiol. (1)

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D. J. Cuccia, F. P. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009).
[Crossref]

C. K. Hayakawa, K. Karrobi, V. Pera, D. Roblyer, and V. Venugopalan, “Optical sampling depth in the spatial frequency domain,” J. Biomed. Opt. 24(07), 1–14 (2018).
[Crossref]

J. Choi, M. Wolf, V. Toronov, U. Wolf, C. Polzonetti, D. Hueber, L. P. Safonova, R. Gupta, A. Michalos, W. Mantulin, and E. Gratton, “Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach,” J. Biomed. Opt. 9(1), 221–229 (2004).
[Crossref]

S. Willmann, A. Terenji, J. Osterholtz, J. Meister, P. Hering, and H.-J. Schwarzmaier, “Small-volume frequency-domain oximetry: phantom experiments and first in vivo results,” J. Biomed. Opt. 8(4), 618–628 (2003).
[Crossref]

S. H. Tseng, C. Hayakawa, J. Spanier, and A. J. Durkin, “Investigation of a probe design for facilitating the uses of the standard photon diffusion equation at short source-detector separations: Monte Carlo simulations,” J. Biomed. Opt. 14(5), 054043 (2009).
[Crossref]

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Figures (8)

Fig. 1.
Fig. 1. Monte Carlo-based lookup table for small separation FDNIRS. Monte Carlo simulated AC amplitude at ρ = 3 mm (a), ρ = 6 mm (b), and the ratio of AC at 3 versus 6mm (c), as well as phase shift (ϕ) at ρ = 3 mm (d), ρ = 6 mm (e), and the phase difference between 3 and 6 mm (f) as a function of absorption and reduced scattering coefficient of the tissue (μa and μs, respectively). The black lines in (a-f) depict representative isolines of AC (solid) and phase (dashed) that we project onto the μs′-μa axis in (g-i). The iso-phase and iso-amplitude lines from a single separation (g,h) intersect at multiple points, whereas the iso-AC ratio and iso-phase difference contour lines form a unique intersection point (i).
Fig. 2.
Fig. 2. Experimental setup for in vivo mice validation. (a) An invasive oxygen partial pressure (pO2) sensor was implanted into the cortex (red), and non-invasive FDNIRS measurements at 3 and 6 mm source-detector separation were made on the contralateral hemisphere over the intact scalp (blue) (b) For each mouse measurement, the fraction of inspired oxygen (FiO2) was manipulated from 30 to 7.5%.
Fig. 3.
Fig. 3. Intralipid/ink phantom verification of small separation FDNIRS. (a, b) Expected vs. estimated μs′ (a) and μa (b) across all Intralipid/ink liquid phantoms. Here estimated μ′s and μa were obtained using small separation FDNIRS and expected μ′s and μa were obtained with large-separation FDNIRS. The dashed lines represent the line of unity. Error bars indicate the standard deviation of three repetitions at each phantom. (c, d) Bland-Altman plots of the difference between estimated and excepted μs′ (c) and μa (d) versus the mean of two methods. The solid horizontal line indicates the mean difference between the two methods. The black dotted lines indicate 95% limits for agreement. The blue dotted lines indicate 95% confidence interval of the limit of agreement.
Fig. 4.
Fig. 4. Blood-mixed liquid phantom verification (a-c) Representative time traces of oxyhemoglobin (HbO, red), deoxyhemoglobin (HbR, blue), total hemoglobin (HbT, black), oxygen saturation (SO2) and reduced scattering coefficients at 690 (green) and 830nm (magenta) estimated by small separation FDNIRS (solid rectangle) and by large separation FDNIRS (hollow circles) during blood phantom deoxygenation. (d-e) Comparison of small separation FDNIRS with expected large-separation FDNIRS HbT (d) and SO2 (e) across four blood phantoms with target HbT of 40 μM (orange) or 60 μM (purple) and target μs′(785 nm) of 6 cm-1 (solid rectangles) or 10 cm-1(hollow circles). The black dotted lines in (d,e) denote the line of unity, the black solid lines denotes the line of best linear fit, and the grey shaded regions denotes the confidence interval of the linear fit. (f) Four representative μs′ spectra measured by small separation FDNIRS on a blood phantom (target μs′(785 nm) of 6 cm-1) where HbT was varied from 40 to 60μM (orange and purple, respectively) and SO2 was varied from 40 to 70% (diamond and cross, respectively).
Fig. 5.
Fig. 5. In vivo validation during an inspired gas challenge. (a) Temporal changes in cerebral oxygen saturation (SO2, black) measured with non-invasive small separation FDNIRS along with concomitant changes in partial oxygen pressure (pO2, blue) measured invasively with an implanted sensor. Measurements were made during a graded hyper/hypoxia challenge wherein the fraction of inspired oxygen (FiO2) was manipulated from 30 to 7.5%. (b) Temporal changes in oxy- (HbO, blue), deoxy- (HbR, red) and total (HbT, black) hemoglobin concentration during the gas challenge. (c) Reduced scattering coefficient (μs′) at 690 nm (green) and 830 nm (magenta) during the gas challenge. In all plots, the solid lines (shaded regions) denote the mean (standard deviation) across 7 measurements in 4 mice. For visualization of these mean values, all data have been downsampled to 0.1Hz. Vertical dotted grey lines denote changes in fraction of inspired oxygen.
Fig. 6.
Fig. 6. Influence of extracerebral layers. Results of fitting simulated 3-layer data to our small separation FDNIRS algorithm that assumes a semi-infinite homogenous medium. The x-axis in (a) and (b) denotes the simulated brain (bottom layer) oxygen saturation (SO2) and total hemoglobin concentration (HbT), respectively, while the y-axis denotes the small separation FDNIRS estimated obtained assuming a semi-infinite geometry. The dashed line in (a) and (b) represents an ideal estimation. (c) Simulated brain μs′(λ) (black) and μs′(λ) measured by small separation FDNIRS (grey) as a function of wavelength for 15 total simulations (5 brain HbT x 3 brain SO2).
Fig. 7.
Fig. 7. Intralipid/ink phantom verification of small separation FDNIRS without calibration. (a, b) Expected vs. estimated μs′ (a) and μa (b) across all Intralipid/ink liquid phantoms. Here estimated μs′ and μa were obtained using small separation FDNIRS and expected μs′ and μa were obtained with large-separation FDNIRS. The solid black lines represent the best linear fit and the dashed lines represent the line of unity. Error bars indicate the standard deviation of three repetitions at each phantom. (c, d) Bland-Altman plots of the difference between estimated and excepted μs′ (c) and μa (d) versus the mean of two methods. The solid horizontal line indicates the mean difference between the two methods. The dotted lines indicate the 95% limits for agreement. The blue dotted lines indicate 95% confidence intervals of the limits of agreement.
Fig. 8.
Fig. 8. Photon interaction density for small separation FDNIRS. MC simulation was performed on a three-layer model simulating mouse scalp, skull and brain. (a,b) Each pixel in these images represents the interaction density, Sn, normalized to the maximum S within the x-z plane. Pixels with weights less than three-orders of magnitude relative to the maximum were set to black. The color bar indicates a base 10 logarithm of the normalized Sn. (c) Probability distribution of the maximum z-coordinate of each detected photon at 3 (solid line) and 6 mm (dotted line). The vertical red line denotes the typical thickness of extracerebral layers for an adult C57BL/6 mouse. For the 3 mm separation, >86% of detected photons reach a depth > 1 mm. Similarly, for 6 mm, >99% of detected photons reach a depth > 1 mm.

Tables (1)

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Table 1. Optical properties used for three-layered MC simulation

Equations (3)

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χ 2 = ( A C r a t i o M C A C r a t i o M e a s u r e d A C r a t i o M e a s u r e d ) 2 + ( ϕ d i f f M C ϕ d i f f M e a s u r e d ϕ d i f f M e a s u r e d ) 2 .
R M S E = 1 N N ( M e a s u r e d K n o w n K n o w n ) 2 × 100 ( % ) ,
S N = i = 1 N w n , j ,

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