Abstract

Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.

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

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    [Crossref]
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    [Crossref]
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  23. 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(1), 192–215 (1997).
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    [Crossref]
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  27. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).
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    [Crossref]
  29. F. Ferri and D. Magatti, “Hardware simulator for photon correlation spectroscopy,” Rev. Sci. Instrum. 74(10), 4273–4279 (2003).
    [Crossref]

2020 (1)

L. Xu, Y. Liu, J. Yu, X. Li, X. Yu, H. Cheng, and J. Li, “Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy,” J. Neurosci. Methods 331, 108538 (2020).
[Crossref]

2019 (4)

R. Rosas-Romero, E. Guevara, K. Peng, D. K. Nguyen, F. Lesage, P. Pouliot, and W. E. Lima-Saad, “Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals,” Comput. Biol. Med. 111, 103355 (2019).
[Crossref]

J. T. Smith, R. Yao, N. Sinsuebphon, A. Rudkouskaya, N. Un, J. Mazurkiewicz, M. Barroso, P. Yan, and X. Intes, “Fast fit-free analysis of fluorescence lifetime imaging via deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(48), 24019–24030 (2019).
[Crossref]

R. Yao, M. Ochoa, P. Yan, and X. Intes, “Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach,” Light: Sci. Appl. 8(1), 26 (2019).
[Crossref]

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[Crossref]

2018 (4)

F. Long, “Deep learning-based mesoscopic fluorescence molecular tomography: an in silico study,” J. Med. Imaging 5(3), 036001 (2018).
[Crossref]

J. Li, C.-S. Poon, J. Kress, D. J. Rohrbach, and U. Sunar, “Resting-state functional connectivity measured by diffuse correlation spectroscopy,” J. Biophotonics 11(2), e201700165 (2018).
[Crossref]

Y. Zhao, Y. Deng, F. Bao, H. Peterson, R. Istfan, and D. Roblyer, “Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging,” Opt. Lett. 43(22), 5669 (2018).
[Crossref]

J. Li, C.-S. Poon, J. Kress, D. J. Rohrbach, and U. Sunar, “Resting-State Functional Connectivity measured by Diffuse Correlation Spectroscopy,” J. Biophotonics 11(2), e201700165 (2018).
[Crossref]

2017 (1)

Y. Shang, T. Li, and G. Yu, “Clinical applications of near-infrared diffuse correlation spectroscopy and tomography for tissue blood flow monitoring and imaging,” Physiol. Meas. 38(4), R1–R26 (2017).
[Crossref]

2016 (1)

2015 (1)

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

2014 (2)

T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, microvascular cerebral blood flow measurement,” NeuroImage 85, 51–63 (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]

2013 (1)

Y. Shang, K. Gurley, and G. Yu, “Diffuse Correlation Spectroscopy (DCS) for Assessment of Tissue Blood Flow in Skeletal Muscle: Recent Progress,” Anat. Physiol. 3(2), 128 (2013).
[Crossref]

2012 (1)

Y. Shang, K. Gurley, B. Symons, D. Long, R. Srikuea, L. J. Crofford, C. A. Peterson, and G. Yu, “Noninvasive optical characterization of muscle blood flow, oxygenation, and metabolism in women with fibromyalgia,” Arthritis Res. Ther. 14(6), R236 (2012).
[Crossref]

2011 (1)

R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A 369(1955), 4390–4406 (2011).
[Crossref]

2010 (1)

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse Optics for Tissue Monitoring and Tomography T,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref]

2007 (1)

2005 (1)

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

2004 (1)

2003 (1)

F. Ferri and D. Magatti, “Hardware simulator for photon correlation spectroscopy,” Rev. Sci. Instrum. 74(10), 4273–4279 (2003).
[Crossref]

1997 (1)

1995 (1)

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

Adam, H.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).

Anderson, P.

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[Crossref]

Andreetto, M.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).

Baker, W. B.

Bao, F.

Barroso, M.

J. T. Smith, R. Yao, N. Sinsuebphon, A. Rudkouskaya, N. Un, J. Mazurkiewicz, M. Barroso, P. Yan, and X. Intes, “Fast fit-free analysis of fluorescence lifetime imaging via deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(48), 24019–24030 (2019).
[Crossref]

Bengio, Y.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

Boas, D. A.

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(1), 192–215 (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]

Buckley, E. M.

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]

R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A 369(1955), 4390–4406 (2011).
[Crossref]

Burnett, M. G.

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]

Carp, S. A.

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[Crossref]

Chen, B.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).

Chen, L. C.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018).

Cheng, H.

L. Xu, Y. Liu, J. Yu, X. Li, X. Yu, H. Cheng, and J. Li, “Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy,” J. Neurosci. Methods 331, 108538 (2020).
[Crossref]

Choe, R.

R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A 369(1955), 4390–4406 (2011).
[Crossref]

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse Optics for Tissue Monitoring and Tomography T,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref]

Crofford, L. J.

Y. Shang, K. Gurley, B. Symons, D. Long, R. Srikuea, L. J. Crofford, C. A. Peterson, and G. Yu, “Noninvasive optical characterization of muscle blood flow, oxygenation, and metabolism in women with fibromyalgia,” Arthritis Res. Ther. 14(6), R236 (2012).
[Crossref]

Deng, Y.

Detre, J. A.

Dietsche, G.

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

Durduran, T.

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

R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A 369(1955), 4390–4406 (2011).
[Crossref]

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse Optics for Tissue Monitoring and Tomography T,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref]

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(3), 1064–1075 (2007).
[Crossref]

T. Durduran, G. Yu, M. G. Burnett, J. A. Detre, J. H. Greenberg, J. Wang, C. Zhou, and A. G. Yodh, “Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation,” Opt. Lett. 29(15), 1766–1768 (2004).
[Crossref]

Elbert, T.

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

Farzam, P.

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[Crossref]

Farzam, P. Y.

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[Crossref]

Ferrari, M.

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[Crossref]

Ferri, F.

F. Ferri and D. Magatti, “Hardware simulator for photon correlation spectroscopy,” Rev. Sci. Instrum. 74(10), 4273–4279 (2003).
[Crossref]

Floyd, T. F.

Franceschini, M. A.

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
[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]

Gannon, K.

Gisler, T.

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

Grant, P. E.

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]

Greenberg, J. H.

Guevara, E.

R. Rosas-Romero, E. Guevara, K. Peng, D. K. Nguyen, F. Lesage, P. Pouliot, and W. E. Lima-Saad, “Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals,” Comput. Biol. Med. 111, 103355 (2019).
[Crossref]

Gui, Z.

P. Zhang, Z. Gui, L. Hao, X. Zhang, C. Liu, and Y. Shang, “Signal processing for diffuse correlation spectroscopy with recurrent neural network of deep learning,” in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) (IEEE, 2019), pp. 328–332.

Gurley, K.

Y. Shang, K. Gurley, and G. Yu, “Diffuse Correlation Spectroscopy (DCS) for Assessment of Tissue Blood Flow in Skeletal Muscle: Recent Progress,” Anat. Physiol. 3(2), 128 (2013).
[Crossref]

Y. Shang, K. Gurley, B. Symons, D. Long, R. Srikuea, L. J. Crofford, C. A. Peterson, and G. Yu, “Noninvasive optical characterization of muscle blood flow, oxygenation, and metabolism in women with fibromyalgia,” Arthritis Res. Ther. 14(6), R236 (2012).
[Crossref]

Hao, L.

P. Zhang, Z. Gui, L. Hao, X. Zhang, C. Liu, and Y. Shang, “Signal processing for diffuse correlation spectroscopy with recurrent neural network of deep learning,” in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) (IEEE, 2019), pp. 328–332.

Heger, D.

J. Hennrich, C. Herff, D. Heger, and T. Schultz, “Investigating deep learning for fNIRS based BCI,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2015).

Hennrich, J.

J. Hennrich, C. Herff, D. Heger, and T. Schultz, “Investigating deep learning for fNIRS based BCI,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2015).

Herff, C.

J. Hennrich, C. Herff, D. Heger, and T. Schultz, “Investigating deep learning for fNIRS based BCI,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2015).

Hinton, G.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

Howard, A.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018).

Howard, A. G.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).

Iftime, D.

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

Intes, X.

R. Yao, M. Ochoa, P. Yan, and X. Intes, “Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach,” Light: Sci. Appl. 8(1), 26 (2019).
[Crossref]

J. T. Smith, R. Yao, N. Sinsuebphon, A. Rudkouskaya, N. Un, J. Mazurkiewicz, M. Barroso, P. Yan, and X. Intes, “Fast fit-free analysis of fluorescence lifetime imaging via deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(48), 24019–24030 (2019).
[Crossref]

Istfan, R.

Kalenichenko, D.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).

Kavuri, V.

Kim, M. N.

R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A 369(1955), 4390–4406 (2011).
[Crossref]

Ko, T.

Kress, J.

J. Li, C.-S. Poon, J. Kress, D. J. Rohrbach, and U. Sunar, “Resting-State Functional Connectivity measured by Diffuse Correlation Spectroscopy,” J. Biophotonics 11(2), e201700165 (2018).
[Crossref]

J. Li, C.-S. Poon, J. Kress, D. J. Rohrbach, and U. Sunar, “Resting-state functional connectivity measured by diffuse correlation spectroscopy,” J. Biophotonics 11(2), e201700165 (2018).
[Crossref]

Lecun, Y.

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J. T. Smith, R. Yao, N. Sinsuebphon, A. Rudkouskaya, N. Un, J. Mazurkiewicz, M. Barroso, P. Yan, and X. Intes, “Fast fit-free analysis of fluorescence lifetime imaging via deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(48), 24019–24030 (2019).
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Zhang, X.

P. Zhang, Z. Gui, L. Hao, X. Zhang, C. Liu, and Y. Shang, “Signal processing for diffuse correlation spectroscopy with recurrent neural network of deep learning,” in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) (IEEE, 2019), pp. 328–332.

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Zhmoginov, A.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018).

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M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018).

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” in Computer Vision and Pattern Recognition (2009).

Anat. Physiol. (1)

Y. Shang, K. Gurley, and G. Yu, “Diffuse Correlation Spectroscopy (DCS) for Assessment of Tissue Blood Flow in Skeletal Muscle: Recent Progress,” Anat. Physiol. 3(2), 128 (2013).
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Arthritis Res. Ther. (1)

Y. Shang, K. Gurley, B. Symons, D. Long, R. Srikuea, L. J. Crofford, C. A. Peterson, and G. Yu, “Noninvasive optical characterization of muscle blood flow, oxygenation, and metabolism in women with fibromyalgia,” Arthritis Res. Ther. 14(6), R236 (2012).
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Biomed. Opt. Express (1)

Comput. Biol. Med. (1)

R. Rosas-Romero, E. Guevara, K. Peng, D. K. Nguyen, F. Lesage, P. Pouliot, and W. E. Lima-Saad, “Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals,” Comput. Biol. Med. 111, 103355 (2019).
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J. Appl. Physiol. (1)

V. Quaresima, P. Farzam, P. Anderson, P. Y. Farzam, D. Wiese, S. A. Carp, M. Ferrari, and M. A. Franceschini, “Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles,” J. Appl. Physiol. 127(5), 1328–1337 (2019).
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J. Biomed. Opt. (1)

J. Li, G. Dietsche, D. Iftime, S. E. Skipetrov, G. Maret, T. Elbert, B. 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. Biophotonics (2)

J. Li, C.-S. Poon, J. Kress, D. J. Rohrbach, and U. Sunar, “Resting-state functional connectivity measured by diffuse correlation spectroscopy,” J. Biophotonics 11(2), e201700165 (2018).
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J. Li, C.-S. Poon, J. Kress, D. J. Rohrbach, and U. Sunar, “Resting-State Functional Connectivity measured by Diffuse Correlation Spectroscopy,” J. Biophotonics 11(2), e201700165 (2018).
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J. Med. Imaging (1)

F. Long, “Deep learning-based mesoscopic fluorescence molecular tomography: an in silico study,” J. Med. Imaging 5(3), 036001 (2018).
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J. Neurosci. Methods (1)

L. Xu, Y. Liu, J. Yu, X. Li, X. Yu, H. Cheng, and J. Li, “Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy,” J. Neurosci. Methods 331, 108538 (2020).
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J. Opt. Soc. Am. A (1)

Light: Sci. Appl. (1)

R. Yao, M. Ochoa, P. Yan, and X. Intes, “Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach,” Light: Sci. Appl. 8(1), 26 (2019).
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Nature (1)

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
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NeuroImage (1)

T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, microvascular cerebral blood flow measurement,” NeuroImage 85, 51–63 (2014).
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Neurophotonics (1)

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

Opt. Lett. (2)

Philos. Trans. R. Soc., A (1)

R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A 369(1955), 4390–4406 (2011).
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Phys. Rev. Lett. (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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Physiol. Meas. (1)

Y. Shang, T. Li, and G. Yu, “Clinical applications of near-infrared diffuse correlation spectroscopy and tomography for tissue blood flow monitoring and imaging,” Physiol. Meas. 38(4), R1–R26 (2017).
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Figures (5)

Fig. 1.
Fig. 1. The NIR laser is directed onto the forearm via a fiber. Photons injected by laser light scatter from blood cells, which introduce temporal intensity fluctuations at the single-photon counting module (SPCM) detector. The SPCM detects the photon and sends a signal to the autocorrelator, which generates the g2 curve. From g2 data, one can derive the g1 curve via Siegert relation (see text for details). The decay rate of g1 curve is related to blood flow: moving blood cells introduce endogenous flow contrast (sharper decay, higher blood flow). A cuff is also placed on the forearm and pumped to 200mmHg to restrict and perturb the blood flow in the arm muscle microvasculature (low blood flow, slower decay rate (colored red) in g1.
Fig. 2.
Fig. 2. The structure of the deep learning network (DNN). It takes in 128 points of the g2 function at a given τ and is reshaped into a 32 × 4 matrix. Convolutional Neural Network (CNN) is used to map the matrix into a 32 × 32 image before being passed into MobileNetV2. The output of the MobileNetV2 is connected to a fully connected network of two neurons with a linear activation that gives β and BFI as an output.
Fig. 3.
Fig. 3. (A) The training curve is generated from the analytical solution. This analytical model is typically used to fit experimental data such as in (C). (B) DCS noise between 10 to 100kcps is added to the training data to simulate experimental conditions. The data is fed into the deep network for training so that it can obtain the correct result even under noisy conditions. (C) This is an example of experimental data.
Fig. 4.
Fig. 4. (A) Loss function Mean Squared Error (MSE) after 10000 epochs. The final training MSE and validation MSE is at 0.00067 and 0.00263 respectively. (B) The percent error of the predicted test set as compared to the target value in β and BFI. Here, 92.3% of predicted β and 95.6% of the predicted BFI fall within 5% error from the target. (C) Linear regression of the predicted test set as compared to the target values for both β and BFI both showed good linearity with R2 close to 1.
Fig. 5.
Fig. 5. A cuff occlusion experiment was performed by placing the cuff on the forearm and pumped to 200 mmHg to restrict the blood flow. The experiment is split into three blocks of 45 s of rest (baseline), occlusion, and rest again. The analytical solution (AS) was compared to the deep learning model (DNN), which both showed a similar trend for quantified β (A) and BFI (B). The difference between the quantification using AS and DNN was also plotted for β (C) and BFI (D).