Abstract

In general, diffuse reflectance spectroscopy (DRS) systems work with photon diffusion models to determine the absorption coefficient μa and reduced scattering coefficient μs' of turbid samples. However, in some DRS measurement scenarios, such as using short source-detector separations to investigate superficial tissues with comparable μa and μs', photon diffusion models might be invalid or might not have analytical solutions. In this study, a systematic workflow of constructing a rapid, accurate photon transport model that is valid at short source-detector separations (SDSs) and at a wide range of sample albedo is revealed. To create such a model, we first employed a GPU (Graphic Processing Unit) based Monte Carlo model to calculate the reflectance at various sample optical property combinations and established a database at high speed. The database was then utilized to train an artificial neural network (ANN) for determining the sample absorption and reduced scattering coefficients from the reflectance measured at several SDSs without applying spectral constraints. The robustness of the produced ANN model was rigorously validated. We evaluated the performance of a successfully trained ANN using tissue simulating phantoms. We also determined the 500-1000 nm absorption and reduced scattering spectra of in-vivo skin using our ANN model and found that the values agree well with those reported in several independent studies.

© 2015 Optical Society of America

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References

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    [Crossref] [PubMed]
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2013 (1)

M. Jäger, F. Foschum, and A. Kienle, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 057005 (2013).
[Crossref] [PubMed]

2012 (1)

2010 (1)

2009 (2)

S. H. Tseng, P. Bargo, A. Durkin, and N. Kollias, “Chromophore concentrations, absorption and scattering properties of human skin in-vivo,” Opt. Express 17(17), 14599–14617 (2009).
[Crossref] [PubMed]

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] [PubMed]

2008 (2)

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

S. H. Tseng, A. Grant, and A. J. Durkin, “In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy,” J. Biomed. Opt. 13(1), 014016 (2008).
[Crossref] [PubMed]

2006 (1)

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

2005 (1)

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm,” J. Phys. D Appl. Phys. 38(15), 2543–2555 (2005).
[Crossref]

2004 (1)

F. Bevilacqua, J. S. You, C. K. Hayakawa, and V. Venugopalan, “Sampling tissue volumes using frequency-domain photon migration,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(5), 051908 (2004).
[Crossref] [PubMed]

2001 (1)

1999 (2)

1998 (1)

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique,” Phys. Med. Biol. 43(9), 2465–2478 (1998).
[Crossref] [PubMed]

1996 (2)

1995 (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47(2), 131–146 (1995).
[Crossref]

1994 (1)

1993 (1)

1992 (1)

T. J. Farrell, B. C. Wilson, and M. S. Patterson, “The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements,” Phys. Med. Biol. 37(12), 2281–2286 (1992).
[Crossref] [PubMed]

Alerstam, E.

E. Alerstam, W. C. Lo, T. D. Han, J. Rose, S. Andersson-Engels, and L. Lilge, “Next-generation acceleration and code optimization for light transport in turbid media using GPUs,” Biomed. Opt. Express 1(2), 658–675 (2010).
[PubMed]

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

Andersson-Engels, S.

E. Alerstam, W. C. Lo, T. D. Han, J. Rose, S. Andersson-Engels, and L. Lilge, “Next-generation acceleration and code optimization for light transport in turbid media using GPUs,” Biomed. Opt. Express 1(2), 658–675 (2010).
[PubMed]

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

Bargo, P.

Bashkatov, A. N.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm,” J. Phys. D Appl. Phys. 38(15), 2543–2555 (2005).
[Crossref]

Bevilacqua, F.

Butler, J.

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Cerussi, A.

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Cope, M.

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique,” Phys. Med. Biol. 43(9), 2465–2478 (1998).
[Crossref] [PubMed]

Depeursinge, C.

Durkin, A.

S. H. Tseng, P. Bargo, A. Durkin, and N. Kollias, “Chromophore concentrations, absorption and scattering properties of human skin in-vivo,” Opt. Express 17(17), 14599–14617 (2009).
[Crossref] [PubMed]

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Durkin, A. J.

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] [PubMed]

S. H. Tseng, A. Grant, and A. J. Durkin, “In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy,” J. Biomed. Opt. 13(1), 014016 (2008).
[Crossref] [PubMed]

Essenpreis, M.

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique,” Phys. Med. Biol. 43(9), 2465–2478 (1998).
[Crossref] [PubMed]

Farrell, T. J.

T. J. Farrell, B. C. Wilson, and M. S. Patterson, “The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements,” Phys. Med. Biol. 37(12), 2281–2286 (1992).
[Crossref] [PubMed]

Feng, T. C.

Foschum, F.

M. Jäger, F. Foschum, and A. Kienle, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 057005 (2013).
[Crossref] [PubMed]

Foster, T. H.

Genina, E. A.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm,” J. Phys. D Appl. Phys. 38(15), 2543–2555 (2005).
[Crossref]

Grant, A.

S. H. Tseng, A. Grant, and A. J. Durkin, “In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy,” J. Biomed. Opt. 13(1), 014016 (2008).
[Crossref] [PubMed]

Gross, J. D.

Han, T. D.

Haskell, R. C.

Hayakawa, C.

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] [PubMed]

Hayakawa, C. K.

F. Bevilacqua, J. S. You, C. K. Hayakawa, and V. Venugopalan, “Sampling tissue volumes using frequency-domain photon migration,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(5), 051908 (2004).
[Crossref] [PubMed]

Hibst, R.

Hsiang, D.

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Hull, E. L.

Jacques, S. L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47(2), 131–146 (1995).
[Crossref]

Jäger, M.

M. Jäger, F. Foschum, and A. Kienle, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 057005 (2013).
[Crossref] [PubMed]

Kienle, A.

M. Jäger, F. Foschum, and A. Kienle, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 057005 (2013).
[Crossref] [PubMed]

A. Kienle and M. S. Patterson, “Determination of the optical properties of turbid media from a single Monte Carlo simulation,” Phys. Med. Biol. 41(10), 2221–2227 (1996).
[Crossref] [PubMed]

A. Kienle, L. Lilge, M. S. Patterson, R. Hibst, R. Steiner, and B. C. Wilson, “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt. 35(13), 2304–2314 (1996).
[Crossref] [PubMed]

Kochubey, V. I.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm,” J. Phys. D Appl. Phys. 38(15), 2543–2555 (2005).
[Crossref]

Kohl, M.

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique,” Phys. Med. Biol. 43(9), 2465–2478 (1998).
[Crossref] [PubMed]

Kollias, N.

Le, D.

Lilge, L.

Lo, W. C.

Marquet, P.

McAdams, M. S.

Patterson, M. S.

A. Kienle, L. Lilge, M. S. Patterson, R. Hibst, R. Steiner, and B. C. Wilson, “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt. 35(13), 2304–2314 (1996).
[Crossref] [PubMed]

A. Kienle and M. S. Patterson, “Determination of the optical properties of turbid media from a single Monte Carlo simulation,” Phys. Med. Biol. 41(10), 2221–2227 (1996).
[Crossref] [PubMed]

T. J. Farrell, B. C. Wilson, and M. S. Patterson, “The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements,” Phys. Med. Biol. 37(12), 2281–2286 (1992).
[Crossref] [PubMed]

Pfefer, J.

Piguet, D.

Prahl, S. A.

Ramella-Roman, J.

Rose, J.

Shah, N.

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Simpson, C. R.

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique,” Phys. Med. Biol. 43(9), 2465–2478 (1998).
[Crossref] [PubMed]

Spanier, J.

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] [PubMed]

Steiner, R.

Svaasand, L. O.

Svensson, T.

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

Tromberg, B. J.

Tsay, T. T.

Tseng, S. H.

S. H. Tseng, P. Bargo, A. Durkin, and N. Kollias, “Chromophore concentrations, absorption and scattering properties of human skin in-vivo,” Opt. Express 17(17), 14599–14617 (2009).
[Crossref] [PubMed]

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] [PubMed]

S. H. Tseng, A. Grant, and A. J. Durkin, “In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy,” J. Biomed. Opt. 13(1), 014016 (2008).
[Crossref] [PubMed]

Tuchin, V. V.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm,” J. Phys. D Appl. Phys. 38(15), 2543–2555 (2005).
[Crossref]

van Gemert, M. J. C.

Venugopalan, V.

F. Bevilacqua, J. S. You, C. K. Hayakawa, and V. Venugopalan, “Sampling tissue volumes using frequency-domain photon migration,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(5), 051908 (2004).
[Crossref] [PubMed]

Wang, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47(2), 131–146 (1995).
[Crossref]

Wang, Q.

Welch, A. J.

Wilson, B. C.

A. Kienle, L. Lilge, M. S. Patterson, R. Hibst, R. Steiner, and B. C. Wilson, “Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue,” Appl. Opt. 35(13), 2304–2314 (1996).
[Crossref] [PubMed]

T. J. Farrell, B. C. Wilson, and M. S. Patterson, “The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements,” Phys. Med. Biol. 37(12), 2281–2286 (1992).
[Crossref] [PubMed]

You, J. S.

F. Bevilacqua, J. S. You, C. K. Hayakawa, and V. Venugopalan, “Sampling tissue volumes using frequency-domain photon migration,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(5), 051908 (2004).
[Crossref] [PubMed]

Zheng, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47(2), 131–146 (1995).
[Crossref]

Appl. Opt. (3)

Biomed. Opt. Express (2)

Comput. Meth. Prog. Bio. (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47(2), 131–146 (1995).
[Crossref]

J. Biomed. Opt. (5)

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

S. H. Tseng, A. Grant, and A. J. Durkin, “In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy,” J. Biomed. Opt. 13(1), 014016 (2008).
[Crossref] [PubMed]

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] [PubMed]

M. Jäger, F. Foschum, and A. Kienle, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 057005 (2013).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (3)

J. Phys. D Appl. Phys. (1)

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm,” J. Phys. D Appl. Phys. 38(15), 2543–2555 (2005).
[Crossref]

Opt. Express (1)

Phys. Med. Biol. (3)

A. Kienle and M. S. Patterson, “Determination of the optical properties of turbid media from a single Monte Carlo simulation,” Phys. Med. Biol. 41(10), 2221–2227 (1996).
[Crossref] [PubMed]

T. J. Farrell, B. C. Wilson, and M. S. Patterson, “The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements,” Phys. Med. Biol. 37(12), 2281–2286 (1992).
[Crossref] [PubMed]

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique,” Phys. Med. Biol. 43(9), 2465–2478 (1998).
[Crossref] [PubMed]

Phys. Rev. E Stat. Nonlin. Soft Matter Phys. (1)

F. Bevilacqua, J. S. You, C. K. Hayakawa, and V. Venugopalan, “Sampling tissue volumes using frequency-domain photon migration,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(5), 051908 (2004).
[Crossref] [PubMed]

Other (2)

Oregon Medical Laser Center, http://omlc.org/spectra/ .

V. V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis (SPIE Press, 2007).

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Figures (12)

Fig. 1
Fig. 1 The concept of ANN training and the function of a trained ANN model.
Fig. 2
Fig. 2 The diffuse reflectance spectroscopy (DRS) system and the top view of diffusing probe.
Fig. 3
Fig. 3 The number of indistinguishable reflectance sets at various μa and μs' combinations. Each point in the plot indicates the number of reflectance sets in the database that have difference within 0.5% from a certain reflectance set corresponding to a certain μa and μs' combination. Each set of reflectance consists of reflectance collected at source to detector separations of 2 and 3 mm.
Fig. 4
Fig. 4 Maximal percent variation of (a) μa and (b) μs' of the reflectance sets that have difference within 0.5%. Each set of reflectance consists of reflectance collected at source to detector separations of 2 and 3 mm.
Fig. 5
Fig. 5 Maximal percent variation of (a) μa and (b) μs' of the reflectance sets that have difference within 0.5% at a reduced optical property range. Each set of reflectance consists of reflectance collected at source to detector separations of 2 and 3 mm.
Fig. 6
Fig. 6 Maximal percent variation of (a) μa and (b) μs' of the reflectance sets that have difference within 0.5%. Each set of reflectance consists of reflectance collected at source to detector separations of 1 and 2 mm.
Fig. 7
Fig. 7 Percent error of the ANN recovered (a) μa and (b) μs' of the 6100 data sets in the training database. The source to detector separations are 1 and 2 mm.
Fig. 8
Fig. 8 Percent error of the ANN recovered (a) μa and (b) μs' of the 1000 randomly selected data sets in the validation database. The source to detector separations are 1 and 2 mm.
Fig. 9
Fig. 9 Flowchart of GPU-MCML-based ANN model construction.
Fig. 10
Fig. 10 (a) μa and (b) μs' spectra of the liquid phantom LP2 recovered using the ANN (black lines) and the SDE (green lines). The benchmark spectra (red lines) were recovered using the IAD method.
Fig. 11
Fig. 11 (a) μa and (b) μs' spectra of the outer thigh skin recovered using the ANN (black lines) and the SDE (green lines). Error bars indicate the standard deviation of five measurements.
Fig. 12
Fig. 12 (a) μa and (b) μs' spectra of the upper inner arm skin recovered using the ANN (black lines) and the SDE (green lines). Error bars indicate the standard deviation of five measurements.

Tables (2)

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Table 1 The recipe of the two liquid phantoms

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Table 2 Chromophore concentrations of outer thigh and upper inner arm skin determined using the absorption spectra derived from the ANN and the SDE.

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