Abstract

Light propagation in biological tissues is frequently modeled by the Monte Carlo (MC) method, which requires processing of many photon packets to obtain adequate quality of the observed backscattered signal. The computation times further increase for detection schemes with small acceptance angles and hence small fraction of the collected backscattered photon packets. In this paper, we investigate the use of a virtually increased acceptance angle for efficient MC simulation of spatially resolved reflectance and estimation of optical properties by an inverse model. We devise a robust criterion for approximation of the maximum virtual acceptance angle and evaluate the proposed methodology for a wide range of tissue-like optical properties and various source configurations.

© 2017 Optical Society of America

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References

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    [Crossref]
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    [Crossref] [PubMed]
  3. J. J. Bravo, K. D. Paulsen, D. W. Roberts, and S. C. Kanick, “Sub-diffuse optical biomarkers characterize localized microstructure and function of cortex and malignant tumor,” Opt. Lett. 41, 781 (2016).
    [Crossref] [PubMed]
  4. P. Usenik, M. Bürmen, A. Fidler, F. Pernuš, and B. Likar, “Automated Classification and Visualization of Healthy and Diseased Hard Dental Tissues by Near-Infrared Hyperspectral Imaging,” Appl. Spectrosc. 66, 1067–1074 (2012).
    [Crossref]
  5. 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, 495–503 (2003).
    [Crossref] [PubMed]
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    [Crossref]
  7. 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, 40–53 (2013).
    [Crossref]
  8. T. Y. Tseng, C. Y. Chen, Y. S. Li, and K. B. Sung, “Quantification of the optical properties of two-layered turbid media by simultaneously analyzing the spectral and spatial information of steady-state diffuse reflectance spectroscopy,” Biomed. Opt. Express 2, 901 (2011).
    [Crossref] [PubMed]
  9. P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance,” Biomed. Opt. Express 6, 3973 (2015).
    [Crossref]
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    [Crossref] [PubMed]
  11. M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
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  18. P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21, 095003 (2016).
    [Crossref]
  19. B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
    [Crossref]
  20. K. W. Calabro and I. J. Bigio, “Influence of the phase function in generalized diffuse reflectance models: Review of current formalisms and novel observations,” J. Biomed. Opt. 19, 075005 (2014).
    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
  24. 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, 587 (2011).
    [Crossref] [PubMed]
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    [Crossref] [PubMed]
  26. 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, 246 (1997).
    [Crossref]
  27. P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
    [Crossref]
  28. E. L. Hull and T. H. Foster, “Steady-state reflectance spectroscopy in the P3 approximation,” J. Opt. Soc. Am. A 18, 584 (2001).
    [Crossref]
  29. P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Collection efficiency of a single optical fiber in turbid media,” Appl. Opt. 42, 3187–3197 (2003).
    [Crossref] [PubMed]
  30. P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Optical properties effects upon the collection efficiency of optical fibers in different probe configurations,” IEEE J. Sel. Top. Quantum Electron. 9, 314–321 (2003).
    [Crossref]
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    [Crossref]
  34. B. Majaron, M. Milanič, and J. Premru, “Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries,” J. Biomed. Opt. 20, 015002 (2015).
    [Crossref] [PubMed]
  35. J. E. Stone, D. Gohara, and G. Shi, “OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems,” IEEE Des. Test 12, 66–73 (2010).
  36. L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131–146 (1995).
    [Crossref]
  37. 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, 060504 (2008).
    [Crossref]
  38. 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, 037003 (2013).
    [Crossref] [PubMed]
  39. 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, 1062 (2006).
    [Crossref] [PubMed]
  40. B. S. Nichols, N. Rajaram, and J. W. Tunnell, “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt. 17, 057001 (2012).
    [Crossref] [PubMed]
  41. N. Rajaram, T. H. Nguyen, and J. W. Tunnell, “Lookup table–based inverse model for determining optical properties of turbid media,” J. Biomed. Opt. 13, 050501 (2008).
    [Crossref]
  42. P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
    [Crossref]
  43. I. Fredriksson, M. Larsson, and T. Strömberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17, 047004 (2012).
    [Crossref] [PubMed]
  44. L. O. Reynolds and N. J. McCormick, “Approximate two-parameter phase function for light scattering,” J. Opt. Soc. Am. 70, 1206 (1980).
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  45. A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: A review,” J. Innov. Opt. Heal. Sci. 04, 9–38 (2011).
    [Crossref]
  46. S. L. Jacques, “Optical properties of biological tissues: A review,” Phys. Med. Biol. 58, 5007 (2013).
    [Crossref]
  47. B. W. Pogue and M. S. Patterson, “Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry,” J. Biomed. Opt. 11, 041102 (2006).
    [Crossref] [PubMed]
  48. R. Michels, F. Foschum, and A. Kienle, “Optical properties of fat emulsions,” Opt. Express 16, 5907 (2008).
    [Crossref] [PubMed]
  49. B. Yu, A. Shah, V. K. Nagarajan, and D. G. Ferris, “Diffuse reflectance spectroscopy of epithelial tissue with a smart fiber-optic probe,” Biomed. Opt. Express 5, 675–689 (2014).
    [Crossref] [PubMed]
  50. A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
    [Crossref]
  51. L. Wang, S. L. Jacques, and L. Zheng, “Conv—convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).
    [Crossref]

2016 (8)

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

D. M. McClatchy, E. J. Rizzo, W. A. Wells, P. P. Cheney, J. C. Hwang, K. D. Paulsen, B. W. Pogue, and S. C. Kanick, “Wide-field quantitative imaging of tissue microstructure using sub-diffuse spatial frequency domain imaging,” Optica 3, 613 (2016).
[Crossref] [PubMed]

J. J. Bravo, K. D. Paulsen, D. W. Roberts, and S. C. Kanick, “Sub-diffuse optical biomarkers characterize localized microstructure and function of cortex and malignant tumor,” Opt. Lett. 41, 781 (2016).
[Crossref] [PubMed]

M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

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

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

A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
[Crossref]

2015 (3)

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
[Crossref]

B. Majaron, M. Milanič, and J. Premru, “Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries,” J. Biomed. Opt. 20, 015002 (2015).
[Crossref] [PubMed]

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

2014 (4)

B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
[Crossref]

K. W. Calabro and I. J. Bigio, “Influence of the phase function in generalized diffuse reflectance models: Review of current formalisms and novel observations,” J. Biomed. Opt. 19, 075005 (2014).
[Crossref]

M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
[Crossref]

B. Yu, A. Shah, V. K. Nagarajan, and D. G. Ferris, “Diffuse reflectance spectroscopy of epithelial tissue with a smart fiber-optic probe,” Biomed. Opt. Express 5, 675–689 (2014).
[Crossref] [PubMed]

2013 (6)

S. L. Jacques, “Optical properties of biological tissues: A review,” Phys. Med. Biol. 58, 5007 (2013).
[Crossref]

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, 40–53 (2013).
[Crossref]

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18, 050902 (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, 037003 (2013).
[Crossref] [PubMed]

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

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[Crossref]

2012 (3)

P. Usenik, M. Bürmen, A. Fidler, F. Pernuš, and B. Likar, “Automated Classification and Visualization of Healthy and Diseased Hard Dental Tissues by Near-Infrared Hyperspectral Imaging,” Appl. Spectrosc. 66, 1067–1074 (2012).
[Crossref]

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

B. S. Nichols, N. Rajaram, and J. W. Tunnell, “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt. 17, 057001 (2012).
[Crossref] [PubMed]

2011 (5)

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

T. Y. Tseng, C. Y. Chen, Y. S. Li, and K. B. Sung, “Quantification of the optical properties of two-layered turbid media by simultaneously analyzing the spectral and spatial information of steady-state diffuse reflectance spectroscopy,” Biomed. Opt. Express 2, 901 (2011).
[Crossref] [PubMed]

S. C. Kanick, U. A. Gamm, M. Schouten, H. J. C. M. Sterenborg, D. J. Robinson, and A. Amelink, “Measurement of the reduced scattering coefficient of turbid media using single fiber reflectance spectroscopy: Fiber diameter and phase function dependence,” Biomed. Opt. Express 2, 1687–1702 (2011).
[Crossref] [PubMed]

F. Foschum, M. Jäger, and A. Kienle, “Fully automated spatially resolved reflectance spectrometer for the determination of the absorption and scattering in turbid media,” Rev. Sci. Instrum. 82, 103104 (2011).
[Crossref] [PubMed]

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, 587 (2011).
[Crossref] [PubMed]

2010 (2)

J. E. Stone, D. Gohara, and G. Shi, “OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems,” IEEE Des. Test 12, 66–73 (2010).

H. Cen and R. Lu, “Optimization of the hyperspectral imaging-based spatially-resolved system for measuring the optical properties of biological materials,” Opt. Express 18, 17412 (2010).
[Crossref] [PubMed]

2008 (3)

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, 060504 (2008).
[Crossref]

R. Michels, F. Foschum, and A. Kienle, “Optical properties of fat emulsions,” Opt. Express 16, 5907 (2008).
[Crossref] [PubMed]

N. Rajaram, T. H. Nguyen, and J. W. Tunnell, “Lookup table–based inverse model for determining optical properties of turbid media,” J. Biomed. Opt. 13, 050501 (2008).
[Crossref]

2006 (3)

B. W. Pogue and M. S. Patterson, “Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry,” J. Biomed. Opt. 11, 041102 (2006).
[Crossref] [PubMed]

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, 1062 (2006).
[Crossref] [PubMed]

E. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11, 064026 (2006).
[Crossref]

2003 (4)

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Collection efficiency of a single optical fiber in turbid media,” Appl. Opt. 42, 3187–3197 (2003).
[Crossref] [PubMed]

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Optical properties effects upon the collection efficiency of optical fibers in different probe configurations,” IEEE J. Sel. Top. Quantum Electron. 9, 314–321 (2003).
[Crossref]

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8, 121–147 (2003).
[Crossref] [PubMed]

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, 495–503 (2003).
[Crossref] [PubMed]

2001 (1)

1999 (1)

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, 246 (1997).
[Crossref]

L. Wang, S. L. Jacques, and L. Zheng, “Conv—convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).
[Crossref]

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, 131–146 (1995).
[Crossref]

1992 (1)

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19, 879–888 (1992).
[Crossref] [PubMed]

1989 (1)

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” “SPIE Series Vol.  5, 102–111” (1989).

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, 060504 (2008).
[Crossref]

Aljancic, U.

M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
[Crossref]

Amelink, A.

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, 060504 (2008).
[Crossref]

Backman, V.

A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
[Crossref]

Bargo, P. R.

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Collection efficiency of a single optical fiber in turbid media,” Appl. Opt. 42, 3187–3197 (2003).
[Crossref] [PubMed]

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Optical properties effects upon the collection efficiency of optical fibers in different probe configurations,” IEEE J. Sel. Top. Quantum Electron. 9, 314–321 (2003).
[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. Innov. Opt. Heal. Sci. 04, 9–38 (2011).
[Crossref]

Bevilacqua, F.

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, 495–503 (2003).
[Crossref] [PubMed]

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, 2935 (1999).
[Crossref]

Bigio, I. J.

K. W. Calabro and I. J. Bigio, “Influence of the phase function in generalized diffuse reflectance models: Review of current formalisms and novel observations,” J. Biomed. Opt. 19, 075005 (2014).
[Crossref]

Bodenschatz, N.

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

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
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Bregar, M.

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
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M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
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B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
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P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime,” J. Biomed. Opt. 21, 095003 (2016).
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M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
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P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance,” Biomed. Opt. Express 6, 3973 (2015).
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M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
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B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
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P. Usenik, M. Bürmen, A. Fidler, F. Pernuš, and B. Likar, “Automated Classification and Visualization of Healthy and Diseased Hard Dental Tissues by Near-Infrared Hyperspectral Imaging,” Appl. Spectrosc. 66, 1067–1074 (2012).
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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, 495–503 (2003).
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M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
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B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
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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, 495–503 (2003).
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T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19, 879–888 (1992).
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Fidler, A.

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F. Foschum, M. Jäger, and A. Kienle, “Fully automated spatially resolved reflectance spectrometer for the determination of the absorption and scattering in turbid media,” Rev. Sci. Instrum. 82, 103104 (2011).
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I. Fredriksson, M. Larsson, and T. Strömberg, “Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy,” J. Biomed. Opt. 17, 047004 (2012).
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Genina, E. A.

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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, 587 (2011).
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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, 037003 (2013).
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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, 40–53 (2013).
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Hwang, J. C.

Ishizuka, T.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

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, 587 (2011).
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Ivancic, M.

M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

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P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Collection efficiency of a single optical fiber in turbid media,” Appl. Opt. 42, 3187–3197 (2003).
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Jäger, M.

F. Foschum, M. Jäger, and A. Kienle, “Fully automated spatially resolved reflectance spectrometer for the determination of the absorption and scattering in turbid media,” Rev. Sci. Instrum. 82, 103104 (2011).
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Jiang, B.

E. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11, 064026 (2006).
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Kanick, S. C.

Kawauchi, S.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

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Kienle, A.

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

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

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

F. Foschum, M. Jäger, and A. Kienle, “Fully automated spatially resolved reflectance spectrometer for the determination of the absorption and scattering in turbid media,” Rev. Sci. Instrum. 82, 103104 (2011).
[Crossref] [PubMed]

R. Michels, F. Foschum, and A. Kienle, “Optical properties of fat emulsions,” Opt. Express 16, 5907 (2008).
[Crossref] [PubMed]

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, 246 (1997).
[Crossref]

Korbelik, J.

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

Krauter, P.

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

Lam, S.

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

Larsson, M.

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

Lee, M.

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

Li, Y. S.

Liemert, A.

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21, 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, 2018 (2013).
[Crossref] [PubMed]

Likar, B.

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

M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
[Crossref]

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

M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
[Crossref]

B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
[Crossref]

P. Usenik, M. Bürmen, A. Fidler, F. Pernuš, and B. Likar, “Automated Classification and Visualization of Healthy and Diseased Hard Dental Tissues by Near-Infrared Hyperspectral Imaging,” Appl. Spectrosc. 66, 1067–1074 (2012).
[Crossref]

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, 037003 (2013).
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Liu, Q.

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18, 050902 (2013).
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Lu, R.

MacAulay, C.

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

Majaron, B.

B. Majaron, M. Milanič, and J. Premru, “Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries,” J. Biomed. Opt. 20, 015002 (2015).
[Crossref] [PubMed]

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[Crossref]

Markey, M. K.

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, 037003 (2013).
[Crossref] [PubMed]

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, 40–53 (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, 495–503 (2003).
[Crossref] [PubMed]

McAlpine, J. N.

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

McClatchy, D. M.

McCormick, N. J.

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, 495–503 (2003).
[Crossref] [PubMed]

Michels, R.

Milanic, M.

B. Majaron, M. Milanič, and J. Premru, “Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries,” J. Biomed. Opt. 20, 015002 (2015).
[Crossref] [PubMed]

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[Crossref]

Miller, D. M.

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

Mustari, A.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

Nagarajan, V. K.

Naglic, P.

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

M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
[Crossref]

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

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[Crossref]

Nguyen, T. H.

N. Rajaram, T. H. Nguyen, and J. W. Tunnell, “Lookup table–based inverse model for determining optical properties of turbid media,” J. Biomed. Opt. 13, 050501 (2008).
[Crossref]

Nguyen, T.-Q.

A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
[Crossref]

Nichols, B. S.

B. S. Nichols, N. Rajaram, and J. W. Tunnell, “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt. 17, 057001 (2012).
[Crossref] [PubMed]

Nishidate, I.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

Novak, J.

E. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11, 064026 (2006).
[Crossref]

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, 495–503 (2003).
[Crossref] [PubMed]

Palmer, G. M.

Patterson, M. S.

B. W. Pogue and M. S. Patterson, “Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry,” J. Biomed. Opt. 11, 041102 (2006).
[Crossref] [PubMed]

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, 246 (1997).
[Crossref]

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19, 879–888 (1992).
[Crossref] [PubMed]

Paulsen, K. D.

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, 587 (2011).
[Crossref] [PubMed]

Pernuš, F.

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

M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
[Crossref]

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

M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
[Crossref]

B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
[Crossref]

P. Usenik, M. Bürmen, A. Fidler, F. Pernuš, and B. Likar, “Automated Classification and Visualization of Healthy and Diseased Hard Dental Tissues by Near-Infrared Hyperspectral Imaging,” Appl. Spectrosc. 66, 1067–1074 (2012).
[Crossref]

Pogue, B. W.

Prahl, S. A.

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Collection efficiency of a single optical fiber in turbid media,” Appl. Opt. 42, 3187–3197 (2003).
[Crossref] [PubMed]

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Optical properties effects upon the collection efficiency of optical fibers in different probe configurations,” IEEE J. Sel. Top. Quantum Electron. 9, 314–321 (2003).
[Crossref]

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” “SPIE Series Vol.  5, 102–111” (1989).

Premru, J.

B. Majaron, M. Milanič, and J. Premru, “Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries,” J. Biomed. Opt. 20, 015002 (2015).
[Crossref] [PubMed]

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, 587 (2011).
[Crossref] [PubMed]

Radosevich, A. J.

A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
[Crossref]

Rajaram, N.

B. S. Nichols, N. Rajaram, and J. W. Tunnell, “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt. 17, 057001 (2012).
[Crossref] [PubMed]

N. Rajaram, T. H. Nguyen, and J. W. Tunnell, “Lookup table–based inverse model for determining optical properties of turbid media,” J. Biomed. Opt. 13, 050501 (2008).
[Crossref]

Ramanujam, N.

Randeberg, L. L.

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[Crossref]

Reynolds, L. O.

Richards-Kortum, R. R.

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8, 121–147 (2003).
[Crossref] [PubMed]

Rizzo, E. J.

Roberts, D. W.

Robinson, D. J.

Salomatina, E.

E. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11, 064026 (2006).
[Crossref]

Sato, M.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

Sato, S.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

Schouten, M.

Shah, A.

Sharma, M.

Shi, G.

J. E. Stone, D. Gohara, and G. Shi, “OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems,” IEEE Des. Test 12, 66–73 (2010).

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, 495–503 (2003).
[Crossref] [PubMed]

Sterenborg, H. J. C. M.

Stone, J. E.

J. E. Stone, D. Gohara, and G. Shi, “OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems,” IEEE Des. Test 12, 66–73 (2010).

Strömberg, T.

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

Sung, K. B.

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, 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, 495–503 (2003).
[Crossref] [PubMed]

Tseng, T. Y.

Tuchin, V. V.

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

Tunnell, J. W.

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, 037003 (2013).
[Crossref] [PubMed]

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, 40–53 (2013).
[Crossref]

B. S. Nichols, N. Rajaram, and J. W. Tunnell, “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt. 17, 057001 (2012).
[Crossref] [PubMed]

N. Rajaram, T. H. Nguyen, and J. W. Tunnell, “Lookup table–based inverse model for determining optical properties of turbid media,” J. Biomed. Opt. 13, 050501 (2008).
[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, 587 (2011).
[Crossref] [PubMed]

Usenik, P.

Utzinger, U.

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8, 121–147 (2003).
[Crossref] [PubMed]

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, 495–503 (2003).
[Crossref] [PubMed]

Vidovic, L.

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[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, 587 (2011).
[Crossref] [PubMed]

Wang, L.

L. Wang, S. L. Jacques, and L. Zheng, “Conv—convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).
[Crossref]

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

L. Wang and S. L. Jacques, “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston (1992).

Wang, L. V.

L. V. Wang and H. I. Wu, Biomedical Optics: Principles and Imaging, 1st ed. (Wiley-Interscience, Hoboken, New Jersey, USA, 2007).

Welch, A. J.

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” “SPIE Series Vol.  5, 102–111” (1989).

Wells, W. A.

Wilson, B.

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19, 879–888 (1992).
[Crossref] [PubMed]

Wu, H. I.

L. V. Wang and H. I. Wu, Biomedical Optics: Principles and Imaging, 1st ed. (Wiley-Interscience, Hoboken, New Jersey, USA, 2007).

Wu, W.

A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
[Crossref]

Yaroslavsky, A. N.

E. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11, 064026 (2006).
[Crossref]

Yoshida, K.

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

Yu, B.

Zheng, L.

L. Wang, S. L. Jacques, and L. Zheng, “Conv—convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).
[Crossref]

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

Zhu, C.

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18, 050902 (2013).
[Crossref]

Appl. Opt. (2)

Appl. Spectrosc. (2)

P. Usenik, M. Bürmen, A. Fidler, F. Pernuš, and B. Likar, “Automated Classification and Visualization of Healthy and Diseased Hard Dental Tissues by Near-Infrared Hyperspectral Imaging,” Appl. Spectrosc. 66, 1067–1074 (2012).
[Crossref]

I. Nishidate, T. Ishizuka, A. Mustari, K. Yoshida, S. Kawauchi, S. Sato, and M. Sato, “Evaluation of Cerebral Hemodynamics and Tissue Morphology of In Vivo Rat Brain Using Spectral Diffuse Reflectance Imaging,” Appl. Spectrosc. 69, 03702816657569 (2016).

Biomed. Opt. Express (5)

Comput. Meth. Prog. Bio. (2)

L. Wang, S. L. Jacques, and L. Zheng, “Conv—convolution for responses to a finite diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).
[Crossref]

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

IEEE Des. Test (1)

J. E. Stone, D. Gohara, and G. Shi, “OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems,” IEEE Des. Test 12, 66–73 (2010).

IEEE J. Sel. Top. Quantum Electron. (1)

P. R. Bargo, S. A. Prahl, and S. L. Jacques, “Optical properties effects upon the collection efficiency of optical fibers in different probe configurations,” IEEE J. Sel. Top. Quantum Electron. 9, 314–321 (2003).
[Crossref]

J. Biomed. Opt. (17)

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18, 050902 (2013).
[Crossref]

B. Majaron, M. Milanič, and J. Premru, “Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries,” J. Biomed. Opt. 20, 015002 (2015).
[Crossref] [PubMed]

M. Bregar, M. Bürmen, U. Aljančič, B. Cugmas, F. Pernuš, and B. Likar, “Contact pressure–aided spectroscopy,” J. Biomed. Opt. 19, 020501 (2014).
[Crossref]

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8, 121–147 (2003).
[Crossref] [PubMed]

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

B. Cugmas, M. Bregar, M. Bürmen, F. Pernuš, and B. Likar, “Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: Problems and solutions,” J. Biomed. Opt. 19, 037002 (2014).
[Crossref]

K. W. Calabro and I. J. Bigio, “Influence of the phase function in generalized diffuse reflectance models: Review of current formalisms and novel observations,” J. Biomed. Opt. 19, 075005 (2014).
[Crossref]

N. Bodenschatz, P. Krauter, A. Liemert, and A. Kienle, “Quantifying phase function influence in subdiffusively backscattered light,” J. Biomed. Opt. 21, 035002 (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, 495–503 (2003).
[Crossref] [PubMed]

E. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11, 064026 (2006).
[Crossref]

N. Bodenschatz, S. Lam, A. Carraro, J. Korbelik, D. M. Miller, J. N. McAlpine, M. Lee, A. Kienle, and C. MacAulay, “Diffuse optical microscopy for quantification of depth-dependent epithelial backscattering in the cervix,” J. Biomed. Opt. 21, 066001 (2016).
[Crossref]

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, 060504 (2008).
[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, 037003 (2013).
[Crossref] [PubMed]

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

B. S. Nichols, N. Rajaram, and J. W. Tunnell, “Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy,” J. Biomed. Opt. 17, 057001 (2012).
[Crossref] [PubMed]

N. Rajaram, T. H. Nguyen, and J. W. Tunnell, “Lookup table–based inverse model for determining optical properties of turbid media,” J. Biomed. Opt. 13, 050501 (2008).
[Crossref]

B. W. Pogue and M. S. Patterson, “Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry,” J. Biomed. Opt. 11, 041102 (2006).
[Crossref] [PubMed]

J. Innov. Opt. Heal. Sci. (1)

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

J. Opt. Soc. Am. (1)

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

Med. Phys. (1)

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19, 879–888 (1992).
[Crossref] [PubMed]

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, 587 (2011).
[Crossref] [PubMed]

Opt. Express (2)

Opt. Lett. (1)

Optica (1)

Phys. Med. Biol. (1)

S. L. Jacques, “Optical properties of biological tissues: A review,” Phys. Med. Biol. 58, 5007 (2013).
[Crossref]

Proc. SPIE (4)

A. Eshein, W. Wu, T.-Q. Nguyen, A. J. Radosevich, and V. Backman, “A fiber optic probe to measure spatially resolved diffuse reflectance in the sub-diffusion regime for in-vivo use,” Proc. SPIE 9703, 970317 (2016).
[Crossref]

P. Naglič, M. Bregar, F. Pernuš, B. Likar, and M. Bürmen, “Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy,” Proc. SPIE 9333, 933310 (2015).
[Crossref]

M. Ivančič, P. Naglič, F. Pernuš, B. Likar, and M. Bürmen, “Extraction of optical properties from hyperspectral images by Monte Carlo light propagation model,” “Proc. SPIE 9706, 97061 (2016).

P. Naglič, L. Vidovič, M. Milanič, L. L. Randeberg, and B. Majaron, “Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin,” Proc. SPIE 9032, 90320N (2013).
[Crossref]

Rev. Sci. Instrum. (1)

F. Foschum, M. Jäger, and A. Kienle, “Fully automated spatially resolved reflectance spectrometer for the determination of the absorption and scattering in turbid media,” Rev. Sci. Instrum. 82, 103104 (2011).
[Crossref] [PubMed]

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, 2018 (2013).
[Crossref] [PubMed]

SPIE Series (1)

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” “SPIE Series Vol.  5, 102–111” (1989).

Other (2)

L. Wang and S. L. Jacques, “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston (1992).

L. V. Wang and H. I. Wu, Biomedical Optics: Principles and Imaging, 1st ed. (Wiley-Interscience, Hoboken, New Jersey, USA, 2007).

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Figures (8)

Fig. 1
Fig. 1 Example of a typical imaging system with a narrow nominal acceptance angle θ0 (a). The nominal acceptance angle θ0 can be virtually increased in the MC simulations to capture a larger fraction of the backscattered photon packets (b). If the quotient between the R(θ0, r), captured at the nominal acceptance angle, and the R(θv, r), captured at the virtually increased acceptance angle θv, is approximately constant and independent of r, the virtual detection scheme can be used to reduce the computation time without introducing additional errors (c).
Fig. 2
Fig. 2 Reflectance error arising from the virtually increased acceptance angle θv quantified by the spatial dependence of the absolute relative SRR error ARE (a) and the relative root mean square SRR error rRMSE (b). Results are presented for a nominal acceptance angle of 3° and for a turbid sample with absorption coefficient of 2.0 cm−1, reduced scattering coefficient of 45.6 cm−1, and phase function parameter γ of 1.65. A predefined 1% threshold value for Et is marked by a horizontal black dashed line and the estimated maximum virtual acceptance angle θmax with a vertical red dashed line.
Fig. 3
Fig. 3 Maximum virtual acceptance angle θmax as a function of the absorption μa and reduced scattering μ′s coefficients at γ = 1.75 (a) and γ = 2.15 (b). The minimum values of θmax observed across the full range of μa and μ′s as a function of γ (c) and the corresponding values of μa (d) and μ′s (e).
Fig. 4
Fig. 4 Scatter plots of the true and estimated values of the absorption coefficient μa (first column) reduced scattering coefficient μ′s (second column) and γ (third column) for virtual acceptance angles θv of 3° (first row), 10° (second row) and 40° (third row) obtained for a 50 μm uniform beam and nominal acceptance angle θ0 = 3°. Ideal relation between the true and estimated values is denoted by a dashed gray line.
Fig. 5
Fig. 5 Relative root-mean-square error (rRMSE) of the estimated μa (a), μ′s (b) and γ (c) as a function of the virtual acceptance angle θv obtained for different sources. Dashed line marks 1% rRMSE.
Fig. 6
Fig. 6 Root-mean-square error (RMSE) of the estimated μa (a), μ′s (b) and γ (c) as a function of the virtual acceptance angle θv obtained for different sources.
Fig. 7
Fig. 7 Normalized distribution of the number of launched photon packets over the full range of optical properties as a function of the virtual acceptance angle θv and for a fixed total weight of the backscattered photons packets Wtot = 106. The median value of each distribution is denoted by a vertical dashed line.
Fig. 8
Fig. 8 Required computation time and relative root-mean-square error (rRMSE) of the estimated optical properties as a function of the virtual acceptance angle θv.

Tables (3)

Tables Icon

Table 1 Maximum virtual acceptance angles θmax (°) for different sources and nominal acceptance angles θ0.

Tables Icon

Table 2 Root-mean-square error (RMSE) and the relative root-mean-square error (rRMSE) of the estimated absorption μa and reduced scattering μ′s coefficients and γ as a function of the virtual acceptance angle θv for a 50 μm uniform beam and nominal acceptance angle θ0 = 3°.

Tables Icon

Table 3 The computation times required to populate a LUT as a function of the virtual acceptance angle θv.

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

W tot = i = 1 N collected w i ,
R ( r ) = f ( μ s , μ a , p ( cos θ ) , G ) ,
μ a , μ s , p ( cos θ ) = f 1 ( R ( r ) , G ) .
CF = r i = 1 N r ( log R m ( r i ) log R LUT ( r i ) ) 2 ,
R ( θ v , r ) k ( θ v ) R ( θ 0 , r ) .
k ( θ v ) = 1 N r i = 1 N r R ( θ v , r i ) R ( θ 0 , r i ) ,
ARE ( θ v , r ) = | R ( θ v , r ) 1 k ( θ v ) R ( θ 0 , r ) | R ( θ 0 , r ) .
rRMSE ( θ v ) = 1 N r i = 1 N r ( R ( θ v , r i ) 1 k ( θ v ) R ( θ 0 , r i ) R ( θ 0 , r i ) ) 2 .
k ( θ v ) = 1 N r N μ a N μ s N γ i = 1 N r j = 1 N μ a k = 1 N μ s l = 1 N γ R j , k , l ( θ v , r i ) R j , k , l ( θ 0 , r i ) ,
RMSE = 1 n i = 1 n ( y ^ i y i ) 2 , rRMSE = 1 n i = 1 n ( y ^ i y i y i ) 2 ,

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