Abstract

In this work, we introduce a framework for efficient and accurate Monte Carlo (MC) simulations of spatially resolved reflectance (SRR) acquired by optical fiber probes that account for all the details of the probe tip including reflectivity of the stainless steel and the properties of the epoxy fill and optical fibers. While using full details of the probe tip is essential for accurate MC simulations of SRR, the break-down of the radial symmetry in the detection scheme leads to about two orders of magnitude longer simulation times. The introduced framework mitigates this performance degradation, by an efficient reflectance regression model that maps SRR obtained by fast MC simulations based on a simplified probe tip model to SRR simulated using the full details of the probe tip. We show that a small number of SRR samples is sufficient to determine the parameters of the regression model. Finally, we use the regression model to simulate SRR for a stainless steel optical probe with six linearly placed fibers and experimentally validate the framework through the use of inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function quantifiers.

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

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. F. Bevilacqua, D. Piguet, P. Marquet, J. D. Gross, B. J. Tromberg, and C. Depeursinge, “In vivo local determination of tissue optical properties: applications to human brain,” Appl. Opt. 38(22), 4939–4950 (1999).
    [Crossref]
  2. M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A
  3. L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging (John Wiley & Sons, 2012).
  4. E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
    [Crossref]
  5. A. Liemert and A. Kienle, “Exact and efficient solution of the radiative transport equation for the semi-infinite medium,” Sci. Rep. 3(1), 2018 (2013).
    [Crossref]
  6. 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]
  7. C. Zhu, Q. Liu, and N. Ramanujam, “Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation,” J. Biomed. Opt. 8(2), 237 (2003).
    [Crossref]
  8. G. M. Palmer and N. Ramanujam, “Monte Carlo-based inverse model for calculating tissue optical properties. Part I: Theory and validation on synthetic phantoms,” Appl. Opt. 45(5), 1062–1071 (2006).
    [Crossref]
  9. R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
    [Crossref]
  10. I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17(4), 047004 (2012).
    [Crossref]
  11. P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance,” Biomed. Opt. Express 6(10), 3973–3988 (2015).
    [Crossref]
  12. D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
    [Crossref]
  13. L. Wang, S. Jacques, and L. Zheng, “Mcml - Monte-Carlo Modeling of Light Transport in Multilayered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
    [Crossref]
  14. 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]
  15. F. Bevilacqua and C. Depeursinge, “Monte Carlo study of diffuse reflectance at source-detector separations close to one transport mean free path,” J. Opt. Soc. Am. A 16(12), 2935–2945 (1999).
    [Crossref]
  16. H. J. Tian, Y. Liu, and L. J. Wang, “Influence of the third-order parameter on diffuse reflectance at small source-detector separations,” Opt. Lett. 31(7), 933–935 (2006).
    [Crossref]
  17. N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
    [Crossref]
  18. P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
    [Crossref]
  19. P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance,” Opt. Lett. 42(7), 1357–1360 (2017).
    [Crossref]
  20. J. L. Sandell and T. C. Zhu, “A review of in-vivo optical properties of human tissues and its impact on PDT,” J. Biophotonics 4(11-12), 773–787 (2011).
    [Crossref]
  21. F. Bevilacqua, A. J. Berger, A. E. Cerussi, D. Jakubowski, and B. J. Tromberg, “Broadband absorption spectroscopy in turbid media by combined frequency-domain and steady-state methods,” Appl. Opt. 39(34), 6498 (2000).
    [Crossref]
  22. P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
    [Crossref]
  23. A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innovative Opt. Health Sci. 04(01), 9–38 (2011).
    [Crossref]
  24. L. Reynolds and N. Mccormick, “Approximate 2-Parameter Phase Function for Light-Scattering,” J. Opt. Soc. Am. 70(10), 1206–1212 (1980).
    [Crossref]
  25. F. Chollet, Deep learning with python (Manning Publications Co., 2017).
  26. M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks,” Opt. Lett. 43(12), 2901–2904 (2018).
    [Crossref]
  27. I. D. Nikolov and C. D. Ivanov, “Optical plastic refractive measurements in the visible and the near-infrared regions,” Appl. Opt. 39(13), 2067–2070 (2000).
    [Crossref]
  28. P. Naglia, F. Pernua, B. Likar, and M. Burmen, “Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media,” Biomed. Opt. Express 8(3), 1895–1910 (2017).
    [Crossref]
  29. P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.
  30. G. Einstein, P. Aruna, and S. Ganesan, “Monte Carlo based model for diffuse reflectance from turbid media for the diagnosis of epithelial dysplasia,” Optik 181, 828–835 (2019).
    [Crossref]
  31. M. Sharma, R. Hennessy, M. K. Markey, and J. W. Tunnell, “Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy,” Biomed. Opt. Express 5(1), 40–53 (2014).
    [Crossref]
  32. X. Zhong, X. Wen, and D. Zhu, “Lookup-table-based inverse model for human skin reflectance spectroscopy: two-layered Monte Carlo simulations and experiments,” Opt. Express 22(2), 1852–1864 (2014).
    [Crossref]

2019 (1)

G. Einstein, P. Aruna, and S. Ganesan, “Monte Carlo based model for diffuse reflectance from turbid media for the diagnosis of epithelial dysplasia,” Optik 181, 828–835 (2019).
[Crossref]

2018 (1)

2017 (2)

2016 (2)

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
[Crossref]

2015 (1)

2014 (2)

2013 (3)

D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
[Crossref]

A. Liemert and A. Kienle, “Exact and efficient solution of the radiative transport equation for the semi-infinite medium,” Sci. Rep. 3(1), 2018 (2013).
[Crossref]

R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
[Crossref]

2012 (1)

I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17(4), 047004 (2012).
[Crossref]

2011 (3)

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

J. L. Sandell and T. C. Zhu, “A review of in-vivo optical properties of human tissues and its impact on PDT,” J. Biophotonics 4(11-12), 773–787 (2011).
[Crossref]

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innovative Opt. Health Sci. 04(01), 9–38 (2011).
[Crossref]

2008 (1)

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]

2006 (2)

2003 (2)

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

C. Zhu, Q. Liu, and N. Ramanujam, “Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation,” J. Biomed. Opt. 8(2), 237 (2003).
[Crossref]

2000 (2)

1999 (2)

1997 (1)

1995 (1)

L. Wang, S. Jacques, and L. Zheng, “Mcml - Monte-Carlo Modeling of Light Transport in Multilayered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref]

1980 (1)

Alerstam, E.

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]

Andersson-Engels, S.

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]

Aruna, P.

G. Einstein, P. Aruna, and S. Ganesan, “Monte Carlo based model for diffuse reflectance from turbid media for the diagnosis of epithelial dysplasia,” Optik 181, 828–835 (2019).
[Crossref]

Bashkatov, A. N.

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innovative Opt. Health Sci. 04(01), 9–38 (2011).
[Crossref]

Berger, A. J.

Bevilacqua, F.

Bodenschatz, N.

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
[Crossref]

Buermen, M.

Burmen, M.

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks,” Opt. Lett. 43(12), 2901–2904 (2018).
[Crossref]

P. Naglia, F. Pernua, B. Likar, and M. Burmen, “Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media,” Biomed. Opt. Express 8(3), 1895–1910 (2017).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
[Crossref]

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A

P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.

Cappon, D. J.

D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
[Crossref]

Cerussi, A. E.

Charvet, I.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

Chollet, F.

F. Chollet, Deep learning with python (Manning Publications Co., 2017).

Depeursinge, C.

Einstein, G.

G. Einstein, P. Aruna, and S. Ganesan, “Monte Carlo based model for diffuse reflectance from turbid media for the diagnosis of epithelial dysplasia,” Optik 181, 828–835 (2019).
[Crossref]

Fang, Q.

D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
[Crossref]

Farrell, T. J.

D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
[Crossref]

Fredriksson, I.

I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17(4), 047004 (2012).
[Crossref]

Ganesan, S.

G. Einstein, P. Aruna, and S. Ganesan, “Monte Carlo based model for diffuse reflectance from turbid media for the diagnosis of epithelial dysplasia,” Optik 181, 828–835 (2019).
[Crossref]

Genina, E. A.

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innovative Opt. Health Sci. 04(01), 9–38 (2011).
[Crossref]

Gross, J. D.

Guo, L.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Hanlon, E. B.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Hayward, J. E.

D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
[Crossref]

Hennessy, R.

M. Sharma, R. Hennessy, M. K. Markey, and J. W. Tunnell, “Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy,” Biomed. Opt. Express 5(1), 40–53 (2014).
[Crossref]

R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
[Crossref]

Itzkan, I.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Ivancic, M.

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks,” Opt. Lett. 43(12), 2901–2904 (2018).
[Crossref]

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A

P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.

Ivanov, C. D.

Jacques, S.

L. Wang, S. Jacques, and L. Zheng, “Mcml - Monte-Carlo Modeling of Light Transport in Multilayered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref]

Jakubowski, D.

Kienle, A.

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
[Crossref]

A. Liemert and A. Kienle, “Exact and efficient solution of the radiative transport equation for the semi-infinite medium,” Sci. Rep. 3(1), 2018 (2013).
[Crossref]

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]

Krauter, P.

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
[Crossref]

Larsson, M.

I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17(4), 047004 (2012).
[Crossref]

Liemert, A.

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
[Crossref]

A. Liemert and A. Kienle, “Exact and efficient solution of the radiative transport equation for the semi-infinite medium,” Sci. Rep. 3(1), 2018 (2013).
[Crossref]

Likar, B.

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks,” Opt. Lett. 43(12), 2901–2904 (2018).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance,” Opt. Lett. 42(7), 1357–1360 (2017).
[Crossref]

P. Naglia, F. Pernua, B. Likar, and M. Burmen, “Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media,” Biomed. Opt. Express 8(3), 1895–1910 (2017).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance,” Biomed. Opt. Express 6(10), 3973–3988 (2015).
[Crossref]

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A

P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.

Lim, S. L.

R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
[Crossref]

Liu, Q.

C. Zhu, Q. Liu, and N. Ramanujam, “Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation,” J. Biomed. Opt. 8(2), 237 (2003).
[Crossref]

Liu, Y.

Markey, M. K.

M. Sharma, R. Hennessy, M. K. Markey, and J. W. Tunnell, “Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy,” Biomed. Opt. Express 5(1), 40–53 (2014).
[Crossref]

R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
[Crossref]

Marquet, P.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

F. Bevilacqua, D. Piguet, P. Marquet, J. D. Gross, B. J. Tromberg, and C. Depeursinge, “In vivo local determination of tissue optical properties: applications to human brain,” Appl. Opt. 38(22), 4939–4950 (1999).
[Crossref]

Mccormick, N.

Meda, P.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

Naglia, P.

Naglic, P.

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks,” Opt. Lett. 43(12), 2901–2904 (2018).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance,” Opt. Lett. 42(7), 1357–1360 (2017).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance,” Biomed. Opt. Express 6(10), 3973–3988 (2015).
[Crossref]

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A

P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.

Nikolov, I. D.

Ory, G.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

Palmer, G. M.

Patterson, M. S.

Perelman, L. T.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Pernua, F.

Pernus, F.

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks,” Opt. Lett. 43(12), 2901–2904 (2018).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance,” Opt. Lett. 42(7), 1357–1360 (2017).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Buermen, “Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance,” Biomed. Opt. Express 6(10), 3973–3988 (2015).
[Crossref]

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A

P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.

Piguet, D.

Qiu, L.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Ramanujam, N.

G. M. Palmer and N. Ramanujam, “Monte Carlo-based inverse model for calculating tissue optical properties. Part I: Theory and validation on synthetic phantoms,” Appl. Opt. 45(5), 1062–1071 (2006).
[Crossref]

C. Zhu, Q. Liu, and N. Ramanujam, “Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation,” J. Biomed. Opt. 8(2), 237 (2003).
[Crossref]

Reynolds, L.

Sandell, J. L.

J. L. Sandell and T. C. Zhu, “A review of in-vivo optical properties of human tissues and its impact on PDT,” J. Biophotonics 4(11-12), 773–787 (2011).
[Crossref]

Sharma, M.

St. Ghislain, M.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

Stromberg, T.

I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17(4), 047004 (2012).
[Crossref]

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]

Thueler, P.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

Tian, H. J.

Tromberg, B. J.

Tuchin, V. V.

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innovative Opt. Health Sci. 04(01), 9–38 (2011).
[Crossref]

Tunnell, J. W.

M. Sharma, R. Hennessy, M. K. Markey, and J. W. Tunnell, “Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy,” Biomed. Opt. Express 5(1), 40–53 (2014).
[Crossref]

R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
[Crossref]

Turzhitsky, V.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Vermeulen, B.

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

Vitkin, E.

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Wang, L.

L. Wang, S. Jacques, and L. Zheng, “Mcml - Monte-Carlo Modeling of Light Transport in Multilayered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref]

Wang, L. J.

Wang, L. V.

L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging (John Wiley & Sons, 2012).

Wen, X.

Wu, H.-i.

L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging (John Wiley & Sons, 2012).

Zheng, L.

L. Wang, S. Jacques, and L. Zheng, “Mcml - Monte-Carlo Modeling of Light Transport in Multilayered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref]

Zhong, X.

Zhu, C.

C. Zhu, Q. Liu, and N. Ramanujam, “Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation,” J. Biomed. Opt. 8(2), 237 (2003).
[Crossref]

Zhu, D.

Zhu, T. C.

J. L. Sandell and T. C. Zhu, “A review of in-vivo optical properties of human tissues and its impact on PDT,” J. Biophotonics 4(11-12), 773–787 (2011).
[Crossref]

Appl. Opt. (4)

Biomed. Opt. Express (3)

Comput. Methods Programs Biomed. (1)

L. Wang, S. Jacques, and L. Zheng, “Mcml - Monte-Carlo Modeling of Light Transport in Multilayered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref]

J. Biomed. Opt. (8)

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]

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21(3), 035002 (2016).
[Crossref]

P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21(9), 095003 (2016).
[Crossref]

P. Thueler, I. Charvet, F. Bevilacqua, M. St. Ghislain, G. Ory, P. Marquet, P. Meda, B. Vermeulen, and C. Depeursinge, “In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties,” J. Biomed. Opt. 8(3), 495 (2003).
[Crossref]

C. Zhu, Q. Liu, and N. Ramanujam, “Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation,” J. Biomed. Opt. 8(2), 237 (2003).
[Crossref]

R. Hennessy, S. L. Lim, M. K. Markey, and J. W. Tunnell, “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt. 18(3), 037003 (2013).
[Crossref]

I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17(4), 047004 (2012).
[Crossref]

D. J. Cappon, T. J. Farrell, Q. Fang, and J. E. Hayward, “Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue,” J. Biomed. Opt. 18(10), 107004 (2013).
[Crossref]

J. Biophotonics (1)

J. L. Sandell and T. C. Zhu, “A review of in-vivo optical properties of human tissues and its impact on PDT,” J. Biophotonics 4(11-12), 773–787 (2011).
[Crossref]

J. Innovative Opt. Health Sci. (1)

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innovative Opt. Health Sci. 04(01), 9–38 (2011).
[Crossref]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (2)

Nat. Commun. (1)

E. Vitkin, V. Turzhitsky, L. Qiu, L. Guo, I. Itzkan, E. B. Hanlon, and L. T. Perelman, “Photon diffusion near the point-of-entry in anisotropically scattering turbid media,” Nat. Commun. 2(1), 587 (2011).
[Crossref]

Opt. Express (1)

Opt. Lett. (3)

Optik (1)

G. Einstein, P. Aruna, and S. Ganesan, “Monte Carlo based model for diffuse reflectance from turbid media for the diagnosis of epithelial dysplasia,” Optik 181, 828–835 (2019).
[Crossref]

Sci. Rep. (1)

A. Liemert and A. Kienle, “Exact and efficient solution of the radiative transport equation for the semi-infinite medium,” Sci. Rep. 3(1), 2018 (2013).
[Crossref]

Other (4)

M. Ivancic, P. Naglic, F. Pernus, B. Likar, and M. Burmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” in Optical Interactions with Tissue and Cells Xxvii, vol. 9706E. D. Jansen, ed. (Spie-Int Soc Optical Engineering, Bellingham, 2016), p. 97061A

L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging (John Wiley & Sons, 2012).

F. Chollet, Deep learning with python (Manning Publications Co., 2017).

P. Naglic, M. Ivancic, F. Pernus, B. Likar, and M. Burmen, “Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime,” Design and Quality for Biomedical Technologies Xi, vol. 10486R. Raghavachari and R. Liang, eds. (Spie-Int Soc Optical Engineering, Bellingham, 2018), p. UNSP 1048618.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (10)

Fig. 1.
Fig. 1. Simplified PTM (left) considers only mismatch between the refractive indices of the sample and optical fibers, while realistic PTM (right) takes into account all the details of the optical fiber probe tip.
Fig. 2.
Fig. 2. Basic workflow of the proposed methodology. SRR that can be efficiently simulated with the simplified PTM but consequently subject to significant simulation errors is rapidly regressed to accurate SRR that could be directly obtained by much slower Monte Carlo simulations utilizing the realistic PTM. The regressed reflectance is used to substantially speed-up (up to 480 fold) the preparation of inverse models for estimation of optical properties from measured and calibrated SRR.
Fig. 3.
Fig. 3. Illustration of the framework that includes training of the ANN-based RRMs that rapidly map SRR from the simplified PTM to the realistic PTM and training of the inverse models for estimation of optical properties from the SRR valid for the simplified or realistic PTMs. The $\textrm {R}_{\textrm {S}}$ and $\textrm {R}_{\textrm {R}}$ stand for reflectance at one source-detector separation, valid for the simplified and realistic PTMs, respectively. The $\textrm {SRR}_{\textrm {S}}$ and $\textrm {SRR}_{\textrm {R}}$ stand for the spatially resolved reflectance valid for the simplified and realistic PTM, respectively. The $\textrm {OP}$ stands for optical property. (left) The SRRs simulated with the simplified PTM are passed through the inverse models for estimation of optical properties from the SRR valid for the simplified PTM. (right) The SRRs simulated with the realistic PTM are passed through the inverse models for estimation of optical properties from the SRR valid for the realistic PTM. Ideally, given that the RRM does not introduce additional errors into the regressed SRR, the estimation errors of the two inverse models should be similar and hence the inverse model of the simplified PTM serves as a performance baseline.
Fig. 4.
Fig. 4. Visualization of optical properties defined by the datasets summarized in Table 1. Red line shows the valid domain of $\gamma$ and $\delta$ subdiffusive quantifiers for the GK scattering phase function.
Fig. 5.
Fig. 5. Distribution of the relative reflectance errors for the realistic PTM at 5 SDS obtained by the RRMs as a function of the training dataset size. The total number of SRR samples in the training datasets varies from 4335 (left) to 36 (right).
Fig. 6.
Fig. 6. Absolute relative error maps of reflectance that was regressed from SRR valid for the simplified PTM to SRR valid for the realistic PTM as a function of the number of SRR samples that were used to train the ANN-based RRMs. The relative errors are provided for three SDS, namely (first row) 220 µm, (second row) 660 µm and (third row) 1100 µm.
Fig. 7.
Fig. 7. Root mean square errors (RMSE) and relative root mean square errors (RRMSE) of the absorption coefficient $\mu _a$, reduced scattering coefficient $\mu ^{\prime }_{s}$ and SPF quantifier $\gamma$, estimated by inverse models for the realistic PTM as a function of the size (4335, 1215, 375, 225, 135 and 36 SRR samples) of the dataset that was used to train the underlying RRM. Green line shows the performance of the inverse model for the simplified PTM that does not depend on the properties of the RRM and thus serves as a baseline.
Fig. 8.
Fig. 8. Spread of 100 reflectance points simulated with the realistic PTM (red) and spread of 100 reflectance points simulated with the simplified PTM and regressed to the realistic PTM (blue). The reflectance points were simulated for optical properties ($\mu _a$ = 12 cm−1, $\mu ^{\prime }_{s}$ = 5 cm−1, $\gamma = 2.12$ and $\delta = 3.0$) that were found to produce the noisiest reflectance.
Fig. 9.
Fig. 9. (top) Reflectance calibration factors for five SDS calculated from reflectance simulated with the simplified and realistic PTM for three non-absorbing liquid turbid phantoms $\textrm {Mie}_{\textrm {n},1}$ , $\textrm {Mie}_{\textrm {n},2}$ and $\textrm {Mie}_{\textrm {n},3}$. (bottom) Coefficient of variation (CV) for the calibration factors at each SDS for the simplified and realistic PTM.
Fig. 10.
Fig. 10. Optical properties of the $\textrm {Mie}_{\textrm {a},1}$ and $\textrm {Mie}_{\textrm {a},2}$ turbid phantoms, predicted by $\textrm {IM}_{\textrm {R,225}}$. Red lines - optical properties estimated from SRR simulated with the Mie SPF using the realistic PTM, blue lines - optical properties estimated from the measured SRR, black lines - true/expected values of the optical properties.

Tables (4)

Tables Icon

Table 1. Summary of datasets that includes the total number of SRR samples ( N SRR ), range of the optical properties ( μ a , μ s , γ and δ ), source of the dataset (MC simulations or ANN-based reflectance regression model (RRM)) and probe tip model (PTM) complexity (simplified (S) or realistic (R)).

Tables Icon

Table 2. Summary of the decimated T RRM , S and T RRM , R datasets used to train the ANN-based RRMs that map SRR from the simplified PTM to the realistic PTM.

Tables Icon

Table 3. The range of optical properties ( μ a , μ s , γ and δ ) spanned by the five liquid turbid phantoms (TP) that were prepared to experimentally validate the inverse models and the average diameter d and standard deviation σ of polystyrene particles in the individual turbid phantoms.

Tables Icon

Table 4. Root mean square (RMSE) and relative root mean square (RRMSE) errors of optical properties estimated for the Mie a , 1 (top rows) and Mie a , 2 (bottom rows) absorbing turbid phantom from the SRR simulated with the GK and Mie SPFs using the realistic PTM, and from measured SRR.