Abstract

This paper presents a new approach to estimate optical properties (absorption and scattering coefficients µa and µs) of biological tissues from spatially-resolved spectroscopy measurements. A Particle Swarm Optimization (PSO)-based algorithm was implemented and firstly modified to deal with spatial and spectral resolutions of the data, and to solve the corresponding inverse problem. Secondly, the optimization was improved by fitting exponential decays to the two best points among all clusters of the “particles” randomly distributed all over the parameter space (µs, µa) of possible solutions. The consequent acceleration of all the groups of particles to the “best” curve leads to significant error decrease in the optical property estimation. The study analyzes the estimated optical property error as a function of the various PSO parameter combinations, and several performance criteria such as the cost-function error and the number of iterations in the algorithms proposed. The final one led to error values between ground truth and estimated values of µs and µa less than 6%.

© 2016 Optical Society of America

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2015 (1)

S. Rogalla and C. H. Contag, “Early cancer detection at the epithelial surface,” Cancer J. 21(3), 179–187 (2015).

2014 (4)

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Y.-F. Dong, Q.-P. Lu, H.-Q. Ding, and H.-Z. Gao, “Study on the best detector-distance of noninvasive biochemical examination by Monte Carlo simulation,” Spectrosc. Spect. Anal. 34(4), 942–946 (2014).

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

2013 (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(1), 40–53 (2013).

I. Boussaid, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristic,” Inform. Sciences 237, 81–117 (2013).

M. Juger, F. Florian, and K. Alwin, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 1–9 (2013).

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

D. Tian, “A Review of Convergence Analysis of Particle Swarm Optimization,” Int. J. Grid. Distr. Comput. 6(6), 117–128 (2013).

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

2012 (6)

Q. Wang, D. Le, J. Ramella-Roman, and J. Pfefer, “Broadband ultraviolet-visible optical property measurement in layered turbid media,” Biomed. Opt. Express 3(6), 1226–1240 (2012).

D. J. Evers, B. H. W. Hendriks, G. W. Lucassen, and T. J. M. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Q. Wang, D. Le, J. Ramella-Roman, and J. Pfefer, “Broadband ultraviolet-visible optical property measurement in layered turbid media,” Biomed. Opt. Express 3(6), 1226–1240 (2012).

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

A. El Dor, M. Clerc, and P. Siarry, “A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization,” Comput. Optim. Appl.,  53, 271–295 (2012).

2011 (2)

R. H. Wilson and M.-A. Mycek, “Models of light propagation in human tissue applied to cancer diagnostics,” Technol. Cancer Res. Treat. 10(2), 121–134 (2011).

A. N. Bashkatov, A N. E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues–a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).

2010 (1)

2009 (1)

E. Pery, W. C. P. M. Blondel, C. Thomas, and F. Guillemin, “Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation,” J. Biomed. Opt. 14(2), 024048 (2009).

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(6), 060504 (2008).

R. R. Allison and C. H. Sibata, “Photodiagnosis for cutaneous malignancy: A brief clinical and technical review,” Photodiag. Photodyn. Ther. 5(4), 247–250 (2008).

E. Borisova, P. Troyanova, P. Pavlova, and L. Avramov, “Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy,” Quantum Electron. 38(6), 597–605 (2008).

2006 (2)

D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, “Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements,” J. Biomed. Opt. 11(6), 1–16 (2006).

Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006).

2005 (2)

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

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

2004 (2)

I. J. Bigio and S. G. Bown, “Spectroscopic sensing of cancer and cancer therapy–current status of translational research,” Cancer Biol. Ther. 3(3), 259–267 (2004).

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

2003 (1)

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), 237–247 (2003).

2001 (1)

D. K. Sardar, M. L. Mayo, and R. D. Glickman, “Optical characterization of melanin,” J. Biomed. Opt. 6(4), 404–411 (2001).

2000 (1)

M. Canpolat and R. M. Judith, “High-angle scattering events strongly affect light collection in clinically relevant measurement geometries for light transport through tissue,” Phys. Med. Biol. 45(5), 1127 (2000).

1995 (2)

S. Feng, F.-A. Zeng, and B. Chance, “Photon migration in the presence of a single defect: a perturbation analysis,” Appl. Opt. 34(19), 3826–3837 (1995).

L. Wang, S. L. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).

1989 (1)

M. J. C. V. Gemert, S. L. Jacques, H. J. C. M. Sterenborg, and W. M. Star, “Skin optics,” IEEE Trans. Biomed. Eng. 36(12), 1146–1154 (1989).

1908 (1)

G. Mie, “Contributions to the optics of turbid media, particularly of colloidal metal solutions (Beitrage zur Optik truber Medien, speziell kolloidaler Metallosungen),” Annalen der Physik 330(3), 377–445 (1908) (In German).

Alerstam, E.

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

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).

Allison, R. R.

R. R. Allison and C. H. Sibata, “Photodiagnosis for cutaneous malignancy: A brief clinical and technical review,” Photodiag. Photodyn. Ther. 5(4), 247–250 (2008).

Alwin, K.

M. Juger, F. Florian, and K. Alwin, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 1–9 (2013).

Andersson-Engels, S.

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

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).

Antoniou, C.

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

Arifler, D.

D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, “Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements,” J. Biomed. Opt. 11(6), 1–16 (2006).

Avramov, L.

E. Borisova, P. Troyanova, P. Pavlova, and L. Avramov, “Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy,” Quantum Electron. 38(6), 597–605 (2008).

Backman, V.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Badizadegan, K.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Bashkatov, A. N.

A. N. Bashkatov, A N. E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues–a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).

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

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Bashkatova, T. A.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Bigio, I. J.

I. J. Bigio and S. G. Bown, “Spectroscopic sensing of cancer and cancer therapy–current status of translational research,” Cancer Biol. Ther. 3(3), 259–267 (2004).

Blondel, W.

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. Blondel, “Pre-processing method to improve optical parameters estimation in Monte Carlo-based inverse problem solving,” in Biophotonics: Photonic Solutions for Better Health Care IV, J. Popp, V. V. Tuchin, D. L. Matthews, F. S. Pavone, and P. Garside, eds., Proc. SPIE9129, 91291Q (2014)

Blondel, W. C. P. M.

E. Pery, W. C. P. M. Blondel, C. Thomas, and F. Guillemin, “Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation,” J. Biomed. Opt. 14(2), 024048 (2009).

M. N. Kholodtsova, I. S. Samsonova, W. C. P. M. Blondel, and V. B. Loschenov, “Metal nanoparticles of different shapes influence on optical properties of multilayered biological tissues,” in Medical Laser Applications and Laser-Tissue Interactions VII, L.D. Lilge and R. Sroka, eds., Proc. SPIE9542, 954205 (2015).

Blondel, W. C.P.M.

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. C.P.M. Blondel, “Particle swarm optimisation algorithm for Monte Carlo-based inverse problem solving,” in Proceedings of IEEE conference on Laser Optics, (Institute of Electrical and Electronics Engineers, 2014), p. 1.

Boone, C.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Borisova, E.

E. Borisova, P. Troyanova, P. Pavlova, and L. Avramov, “Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy,” Quantum Electron. 38(6), 597–605 (2008).

Boussaid, I.

I. Boussaid, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristic,” Inform. Sciences 237, 81–117 (2013).

Bown, S. G.

I. J. Bigio and S. G. Bown, “Spectroscopic sensing of cancer and cancer therapy–current status of translational research,” Cancer Biol. Ther. 3(3), 259–267 (2004).

Calin, M. A.

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

Calin, M. R.

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

Canpolat, M.

M. Canpolat and R. M. Judith, “High-angle scattering events strongly affect light collection in clinically relevant measurement geometries for light transport through tissue,” Phys. Med. Biol. 45(5), 1127 (2000).

Chance, B.

Clay, C. D.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Clerc, M.

A. El Dor, M. Clerc, and P. Siarry, “A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization,” Comput. Optim. Appl.,  53, 271–295 (2012).

Contag, C. H.

S. Rogalla and C. H. Contag, “Early cancer detection at the epithelial surface,” Cancer J. 21(3), 179–187 (2015).

Costa, P.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Crum, C.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Cui, W.

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Cunha, L.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Dasari, R.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Daul, C.

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. Blondel, “Pre-processing method to improve optical parameters estimation in Monte Carlo-based inverse problem solving,” in Biophotonics: Photonic Solutions for Better Health Care IV, J. Popp, V. V. Tuchin, D. L. Matthews, F. S. Pavone, and P. Garside, eds., Proc. SPIE9129, 91291Q (2014)

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. C.P.M. Blondel, “Particle swarm optimisation algorithm for Monte Carlo-based inverse problem solving,” in Proceedings of IEEE conference on Laser Optics, (Institute of Electrical and Electronics Engineers, 2014), p. 1.

Dessinioti, C.

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

Ding, H.-Q.

Y.-F. Dong, Q.-P. Lu, H.-Q. Ding, and H.-Z. Gao, “Study on the best detector-distance of noninvasive biochemical examination by Monte Carlo simulation,” Spectrosc. Spect. Anal. 34(4), 942–946 (2014).

Dong, Y.-F.

Y.-F. Dong, Q.-P. Lu, H.-Q. Ding, and H.-Z. Gao, “Study on the best detector-distance of noninvasive biochemical examination by Monte Carlo simulation,” Spectrosc. Spect. Anal. 34(4), 942–946 (2014).

Dontu, S.

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

Drakaki, E.

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

Eberhart, R.

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of IEEE International Conference on Evolutionary Computation (IEEE, 1998), pp. 69–73.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks (IEEE, 1995), pp. 1942–1948.

El Dor, A.

A. El Dor, M. Clerc, and P. Siarry, “A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization,” Comput. Optim. Appl.,  53, 271–295 (2012).

Evers, D. J.

D. J. Evers, B. H. W. Hendriks, G. W. Lucassen, and T. J. M. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).

Feld, M.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Feng, S.

Ferreira, S.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Florian, F.

M. Juger, F. Florian, and K. Alwin, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 1–9 (2013).

Follen, M.

D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, “Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements,” J. Biomed. Opt. 11(6), 1–16 (2006).

Fredriksson, I.

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

Gao, H.-Z.

Y.-F. Dong, Q.-P. Lu, H.-Q. Ding, and H.-Z. Gao, “Study on the best detector-distance of noninvasive biochemical examination by Monte Carlo simulation,” Spectrosc. Spect. Anal. 34(4), 942–946 (2014).

Gemert, M. J. C. V.

M. J. C. V. Gemert, S. L. Jacques, H. J. C. M. Sterenborg, and W. M. Star, “Skin optics,” IEEE Trans. Biomed. Eng. 36(12), 1146–1154 (1989).

Genina, A N. E. A.

A. N. Bashkatov, A N. E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues–a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).

Genina, E. A.

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

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Georgakoudi, I.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Glickman, R. D.

D. K. Sardar, M. L. Mayo, and R. D. Glickman, “Optical characterization of melanin,” J. Biomed. Opt. 6(4), 404–411 (2001).

Grachev, P. V.

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

Guillemin, F.

E. Pery, W. C. P. M. Blondel, C. Thomas, and F. Guillemin, “Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation,” J. Biomed. Opt. 14(2), 024048 (2009).

Han, T. D.

Heenan, P. J.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Hendriks, B. H. W.

D. J. Evers, B. H. W. Hendriks, G. W. Lucassen, and T. J. M. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).

Hennessy, R.

Horvath, I.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Ishimaru, A.

A. Ishimaru, “Wave propagation and scattering in random media and rough surfaces,” in Proceedings of IEEE Photovoltaic Specialists Conference, (IEEE, 1991), pp. 1359–1366.

Jacques, S. L.

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

L. Wang, S. L. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).

M. J. C. V. Gemert, S. L. Jacques, H. J. C. M. Sterenborg, and W. M. Star, “Skin optics,” IEEE Trans. Biomed. Eng. 36(12), 1146–1154 (1989).

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” in Dosimetry of Laser Radiation in Medicine and Biology, Proc. SPIE, 102–111 (1989).

Jiang, T.

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Judith, R. M.

M. Canpolat and R. M. Judith, “High-angle scattering events strongly affect light collection in clinically relevant measurement geometries for light transport through tissue,” Phys. Med. Biol. 45(5), 1127 (2000).

Juger, M.

M. Juger, F. Florian, and K. Alwin, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 1–9 (2013).

Kalyagina, N. A.

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

Keefe, K.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Keijzer, M.

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” in Dosimetry of Laser Radiation in Medicine and Biology, Proc. SPIE, 102–111 (1989).

Kennedy, J.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks (IEEE, 1995), pp. 1942–1948.

J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the IEEE Congress on Evolutionary Computation (IEEE, 2001), 2, 1671–1676.

Kholodtsova, M. N.

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

M. N. Kholodtsova, I. S. Samsonova, W. C. P. M. Blondel, and V. B. Loschenov, “Metal nanoparticles of different shapes influence on optical properties of multilayered biological tissues,” in Medical Laser Applications and Laser-Tissue Interactions VII, L.D. Lilge and R. Sroka, eds., Proc. SPIE9542, 954205 (2015).

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. C.P.M. Blondel, “Particle swarm optimisation algorithm for Monte Carlo-based inverse problem solving,” in Proceedings of IEEE conference on Laser Optics, (Institute of Electrical and Electronics Engineers, 2014), p. 1.

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. Blondel, “Pre-processing method to improve optical parameters estimation in Monte Carlo-based inverse problem solving,” in Biophotonics: Photonic Solutions for Better Health Care IV, J. Popp, V. V. Tuchin, D. L. Matthews, F. S. Pavone, and P. Garside, eds., Proc. SPIE9129, 91291Q (2014)

Kochubey, V. I.

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

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Le, D.

Lemos, J.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Lepagnot, J.

I. Boussaid, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristic,” Inform. Sciences 237, 81–117 (2013).

Lie, J.

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Lilge, L.

Liu, C.

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Liu, Q.

Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006).

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), 237–247 (2003).

Lo, W. C. Y.

Loschenov, V. B.

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. C.P.M. Blondel, “Particle swarm optimisation algorithm for Monte Carlo-based inverse problem solving,” in Proceedings of IEEE conference on Laser Optics, (Institute of Electrical and Electronics Engineers, 2014), p. 1.

M. N. Kholodtsova, I. S. Samsonova, W. C. P. M. Blondel, and V. B. Loschenov, “Metal nanoparticles of different shapes influence on optical properties of multilayered biological tissues,” in Medical Laser Applications and Laser-Tissue Interactions VII, L.D. Lilge and R. Sroka, eds., Proc. SPIE9542, 954205 (2015).

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. Blondel, “Pre-processing method to improve optical parameters estimation in Monte Carlo-based inverse problem solving,” in Biophotonics: Photonic Solutions for Better Health Care IV, J. Popp, V. V. Tuchin, D. L. Matthews, F. S. Pavone, and P. Garside, eds., Proc. SPIE9129, 91291Q (2014)

Lu, Q.-P.

Y.-F. Dong, Q.-P. Lu, H.-Q. Ding, and H.-Z. Gao, “Study on the best detector-distance of noninvasive biochemical examination by Monte Carlo simulation,” Spectrosc. Spect. Anal. 34(4), 942–946 (2014).

Lucassen, G. W.

D. J. Evers, B. H. W. Hendriks, G. W. Lucassen, and T. J. M. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).

MacAulay, C.

D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, “Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements,” J. Biomed. Opt. 11(6), 1–16 (2006).

Marcus, L.

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

Markey, M. K.

Mathe, D.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Mayo, M. L.

D. K. Sardar, M. L. Mayo, and R. D. Glickman, “Optical characterization of melanin,” J. Biomed. Opt. 6(4), 404–411 (2001).

Mendes, R.

J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the IEEE Congress on Evolutionary Computation (IEEE, 2001), 2, 1671–1676.

Metello, L. F.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Mie, G.

G. Mie, “Contributions to the optics of turbid media, particularly of colloidal metal solutions (Beitrage zur Optik truber Medien, speziell kolloidaler Metallosungen),” Annalen der Physik 330(3), 377–445 (1908) (In German).

Munger, K.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Murphy, B. W.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Mycek, M.-A.

R. H. Wilson and M.-A. Mycek, “Models of light propagation in human tissue applied to cancer diagnostics,” Technol. Cancer Res. Treat. 10(2), 121–134 (2011).

Nie, W.

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Novikova, O. V.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Palmer, G. M.

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Parasca, S. V.

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

Pavlova, P.

E. Borisova, P. Troyanova, P. Pavlova, and L. Avramov, “Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy,” Quantum Electron. 38(6), 597–605 (2008).

Pery, E.

E. Pery, W. C. P. M. Blondel, C. Thomas, and F. Guillemin, “Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation,” J. Biomed. Opt. 14(2), 024048 (2009).

E. Pery, “Biomodal spectroscopy in elastical diffusion and spatially resolved autofluorescence: instrumentation, modeling of light-tissue interactions and application to biological tissue characterization ex vivo and in vivo for cancer detection (Spectroscopie bimodale en diffusion elastique et autofluorescence resolue spatialement: instrumentation, modelisation des interactions lumiere-tissus et application a la caracterisation de tissus biologiques ex vivo et in vivo pour la detection de cancer),” Ph.D. thesis, Ecole doctorale IAEM Lorraine, Institut National Polytechnique de Lorraine, France (PhD thesis in French) (2007).

Peshkova, A. Y.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Pfefer, J.

Prahl, S. A.

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” in Dosimetry of Laser Radiation in Medicine and Biology, Proc. SPIE, 102–111 (1989).

Quirk, C. J.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Rajaram, N.

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Ramanujam, N.

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006).

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), 237–247 (2003).

Ramella-Roman, J.

Richards-Kortum, R.

D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, “Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements,” J. Biomed. Opt. 11(6), 1–16 (2006).

Rogalla, S.

S. Rogalla and C. H. Contag, “Early cancer detection at the epithelial surface,” Cancer J. 21(3), 179–187 (2015).

Rose, J.

Ruers, T. J. M.

D. J. Evers, B. H. W. Hendriks, G. W. Lucassen, and T. J. M. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).

Salavastru, C.

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

Sampson, D. D.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Samsonova, I. S.

M. N. Kholodtsova, I. S. Samsonova, W. C. P. M. Blondel, and V. B. Loschenov, “Metal nanoparticles of different shapes influence on optical properties of multilayered biological tissues,” in Medical Laser Applications and Laser-Tissue Interactions VII, L.D. Lilge and R. Sroka, eds., Proc. SPIE9542, 954205 (2015).

Sardar, D. K.

D. K. Sardar, M. L. Mayo, and R. D. Glickman, “Optical characterization of melanin,” J. Biomed. Opt. 6(4), 404–411 (2001).

Savastru, R.

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

Savelieva, T. A.

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

Schmitt, B. I.

B. I. Schmitt, “Convergence Analysis for Particle Swarm Optimisation,” PhD thesis, FAU University, Erlangen (2015).

Shapshay, S.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Sharma, M.

Sheets, E.

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Shi, Y.

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of IEEE International Conference on Evolutionary Computation (IEEE, 1998), pp. 69–73.

Siarry, P.

I. Boussaid, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristic,” Inform. Sciences 237, 81–117 (2013).

A. El Dor, M. Clerc, and P. Siarry, “A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization,” Comput. Optim. Appl.,  53, 271–295 (2012).

Sibata, C. H.

R. R. Allison and C. H. Sibata, “Photodiagnosis for cutaneous malignancy: A brief clinical and technical review,” Photodiag. Photodyn. Ther. 5(4), 247–250 (2008).

Star, W. M.

M. J. C. V. Gemert, S. L. Jacques, H. J. C. M. Sterenborg, and W. M. Star, “Skin optics,” IEEE Trans. Biomed. Eng. 36(12), 1146–1154 (1989).

Sterenborg, H. J. C. M.

M. J. C. V. Gemert, S. L. Jacques, H. J. C. M. Sterenborg, and W. M. Star, “Skin optics,” IEEE Trans. Biomed. Eng. 36(12), 1146–1154 (1989).

Stolnitz, M. M.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Stratigos, A.

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

Stromberg, T.

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

Summavielle, T.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

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).

Szigeti, K.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Thomas, C.

E. Pery, W. C. P. M. Blondel, C. Thomas, and F. Guillemin, “Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation,” J. Biomed. Opt. 14(2), 024048 (2009).

Tian, D.

D. Tian, “A Review of Convergence Analysis of Particle Swarm Optimization,” Int. J. Grid. Distr. Comput. 6(6), 117–128 (2013).

Troyanova, P.

E. Borisova, P. Troyanova, P. Pavlova, and L. Avramov, “Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy,” Quantum Electron. 38(6), 597–605 (2008).

Tuchin, V. V.

A. N. Bashkatov, A N. E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues–a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).

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

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

Tunnell, J. W.

Turlach, B. A.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Veres, D. S.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Vergou, T.

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

Vieira, D.

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Vishwanath, K.

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

Wang, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).

Wang, Q.

Webster, R. J.

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

Welch, A. J.

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” in Dosimetry of Laser Radiation in Medicine and Biology, Proc. SPIE, 102–111 (1989).

Wilson, R. H.

R. H. Wilson and M.-A. Mycek, “Models of light propagation in human tissue applied to cancer diagnostics,” Technol. Cancer Res. Treat. 10(2), 121–134 (2011).

Xu, Z.

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Zeng, F.-A.

Zhang, Y.

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Zheng, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).

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), 237–247 (2003).

Zhu, J.

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Annalen der Physik (1)

G. Mie, “Contributions to the optics of turbid media, particularly of colloidal metal solutions (Beitrage zur Optik truber Medien, speziell kolloidaler Metallosungen),” Annalen der Physik 330(3), 377–445 (1908) (In German).

Appl. Opt. (2)

Biomed. Opt. Express (4)

Cancer Biol. Ther. (1)

I. J. Bigio and S. G. Bown, “Spectroscopic sensing of cancer and cancer therapy–current status of translational research,” Cancer Biol. Ther. 3(3), 259–267 (2004).

Cancer J. (1)

S. Rogalla and C. H. Contag, “Early cancer detection at the epithelial surface,” Cancer J. 21(3), 179–187 (2015).

Comput. Methods Programs Biomed. (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).

Comput. Optim. Appl. (1)

A. El Dor, M. Clerc, and P. Siarry, “A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization,” Comput. Optim. Appl.,  53, 271–295 (2012).

Faraday Discuss. (1)

K. Badizadegan, V. Backman, C. Boone, C. Crum, R. Dasari, I. Georgakoudi, K. Keefe, K. Munger, S. Shapshay, E. Sheets, and M. Feld, “Spectroscopic diagnosis and imaging of invisible pre-cancer,” Faraday Discuss. 126, 265–279 (2004).

Future Oncol. (1)

D. J. Evers, B. H. W. Hendriks, G. W. Lucassen, and T. J. M. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).

IEEE Trans. Biomed. Eng. (1)

M. J. C. V. Gemert, S. L. Jacques, H. J. C. M. Sterenborg, and W. M. Star, “Skin optics,” IEEE Trans. Biomed. Eng. 36(12), 1146–1154 (1989).

Inform. Sciences (1)

I. Boussaid, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristic,” Inform. Sciences 237, 81–117 (2013).

Int. J. Grid. Distr. Comput. (1)

D. Tian, “A Review of Convergence Analysis of Particle Swarm Optimization,” Int. J. Grid. Distr. Comput. 6(6), 117–128 (2013).

Int. J. Photoenergy (1)

M. N. Kholodtsova, P. V. Grachev, T. A. Savelieva, N. A. Kalyagina, W. Blondel, and V. B. Loschenov, “Scattered and fluorescent photon track reconstruction in a biological tissue,” Int. J. Photoenergy 20141–7 (2014).

J. Biomed. Opt. (10)

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), 237–247 (2003).

M. Juger, F. Florian, and K. Alwin, “Application of multiple artificial neural networks for the determination of the optical properties of turbid media,” J. Biomed. Opt. 18(5), 1–9 (2013).

E. Pery, W. C. P. M. Blondel, C. Thomas, and F. Guillemin, “Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation,” J. Biomed. Opt. 14(2), 024048 (2009).

C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, G. M. Palmer, and N. Ramanujam, “Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model,” J. Biomed. Opt. 17(7), 1–15 (2012).

E. Drakaki, T. Vergou, C. Dessinioti, A. Stratigos, C. Salavastru, and C. Antoniou, “Spectroscopic methods for the photodiagnosis of nonmelanoma skin cancer,” J. Biomed. Opt. 18(6), 1–10 (2013).

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).

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

B. W. Murphy, R. J. Webster, B. A. Turlach, C. J. Quirk, C. D. Clay, P. J. Heenan, and D. D. Sampson, “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt. 10(6), 064020 (2005).

D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, “Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements,” J. Biomed. Opt. 11(6), 1–16 (2006).

D. K. Sardar, M. L. Mayo, and R. D. Glickman, “Optical characterization of melanin,” J. Biomed. Opt. 6(4), 404–411 (2001).

J. Cancer Res. Clin. Oncol. (1)

M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, and S. Dontu, “Optical techniques for the noninvasive diagnosis of skin cancer,” J. Cancer Res. Clin. Oncol. 139(7), 1083–1104 (2013).

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

A. N. Bashkatov, A N. E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues–a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).

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

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

Mol. Diagn. Ther. (1)

L. Cunha, I. Horvath, S. Ferreira, J. Lemos, P. Costa, D. Vieira, D. S. Veres, K. Szigeti, T. Summavielle, D. Mathe, and L. F. Metello, “Preclinical imaging: an essential ally in modern biosciences,” Mol. Diagn. Ther. 18(2), 153–173 (2014).

Opt. Commun. (1)

Y. Zhang, J. Zhu, W. Cui, W. Nie, J. Lie, and Z. Xu, “Threshold thickness for applying diffusion equation in thin tissue optical imaging,” Opt. Commun. 325, 95–99 (2014).

Photodiag. Photodyn. Ther. (1)

R. R. Allison and C. H. Sibata, “Photodiagnosis for cutaneous malignancy: A brief clinical and technical review,” Photodiag. Photodyn. Ther. 5(4), 247–250 (2008).

Phys. Med. Biol. (2)

M. Canpolat and R. M. Judith, “High-angle scattering events strongly affect light collection in clinically relevant measurement geometries for light transport through tissue,” Phys. Med. Biol. 45(5), 1127 (2000).

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

Quantum Electron. (1)

E. Borisova, P. Troyanova, P. Pavlova, and L. Avramov, “Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy,” Quantum Electron. 38(6), 597–605 (2008).

Spectrosc. Spect. Anal. (1)

Y.-F. Dong, Q.-P. Lu, H.-Q. Ding, and H.-Z. Gao, “Study on the best detector-distance of noninvasive biochemical examination by Monte Carlo simulation,” Spectrosc. Spect. Anal. 34(4), 942–946 (2014).

Technol. Cancer Res. Treat. (1)

R. H. Wilson and M.-A. Mycek, “Models of light propagation in human tissue applied to cancer diagnostics,” Technol. Cancer Res. Treat. 10(2), 121–134 (2011).

Other (11)

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. Blondel, “Pre-processing method to improve optical parameters estimation in Monte Carlo-based inverse problem solving,” in Biophotonics: Photonic Solutions for Better Health Care IV, J. Popp, V. V. Tuchin, D. L. Matthews, F. S. Pavone, and P. Garside, eds., Proc. SPIE9129, 91291Q (2014)

M. N. Kholodtsova, I. S. Samsonova, W. C. P. M. Blondel, and V. B. Loschenov, “Metal nanoparticles of different shapes influence on optical properties of multilayered biological tissues,” in Medical Laser Applications and Laser-Tissue Interactions VII, L.D. Lilge and R. Sroka, eds., Proc. SPIE9542, 954205 (2015).

M. N. Kholodtsova, V. B. Loschenov, C. Daul, and W. C.P.M. Blondel, “Particle swarm optimisation algorithm for Monte Carlo-based inverse problem solving,” in Proceedings of IEEE conference on Laser Optics, (Institute of Electrical and Electronics Engineers, 2014), p. 1.

E. Pery, “Biomodal spectroscopy in elastical diffusion and spatially resolved autofluorescence: instrumentation, modeling of light-tissue interactions and application to biological tissue characterization ex vivo and in vivo for cancer detection (Spectroscopie bimodale en diffusion elastique et autofluorescence resolue spatialement: instrumentation, modelisation des interactions lumiere-tissus et application a la caracterisation de tissus biologiques ex vivo et in vivo pour la detection de cancer),” Ph.D. thesis, Ecole doctorale IAEM Lorraine, Institut National Polytechnique de Lorraine, France (PhD thesis in French) (2007).

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo Model of Light Propagation in Tissue,” in Dosimetry of Laser Radiation in Medicine and Biology, Proc. SPIE, 102–111 (1989).

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks (IEEE, 1995), pp. 1942–1948.

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of IEEE International Conference on Evolutionary Computation (IEEE, 1998), pp. 69–73.

J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the IEEE Congress on Evolutionary Computation (IEEE, 2001), 2, 1671–1676.

B. I. Schmitt, “Convergence Analysis for Particle Swarm Optimisation,” PhD thesis, FAU University, Erlangen (2015).

A. Ishimaru, “Wave propagation and scattering in random media and rough surfaces,” in Proceedings of IEEE Photovoltaic Specialists Conference, (IEEE, 1991), pp. 1359–1366.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, M. M. Stolnitz, T. A. Bashkatova, O. V. Novikova, A. Y. Peshkova, and V. V. Tuchin, “Optical properties of melanin in the skin and skin-like phantoms,” in Controlling Tissue Optical Properties: Applications in Clinical Study, V.V. Tuchin, ed., Proc. SPIE, 4162 (2000).

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Figures (7)

Fig. 1
Fig. 1 General context of the work. (a) Simplified scheme of the SDRS system including main parts: light source, probe, tissue and multichannel spectrometer; (b) Schematic representation of the correspondence between experimental (tissue), simulation (numerical model) and physical model configurations; (c) Inverse problem general scheme corresponding to optical parameter estimation based on SDRS measurements.
Fig. 2
Fig. 2 Geometrical configuration of the experimental set-up used for the numerical simulations. The excitation light source illuminates the tissue through fiber F0 of the multi-fiber probe [26] with tissue incident light intensity I0(λ). SDR intensity spectra Iexp(λ,r) are collected at 6 different SDS corresponding to distances r1…6 = [261,344,500,778,1041,1290] µm, respectively.
Fig. 3
Fig. 3 MC simulation noise level (standard deviation in %) as a function of SDS (r) and photon number Nf launched : for Nf = 107, ▲ for Nf = 108 and for Nf = 109. The continuous, dashed and dotted curves drawn are respective exponential fitting (with corresponding equations listed in the legend) for each Nf.
Fig. 4
Fig. 4 Scattering μ s r e f ( λ ) and absorption μ a r e f ( λ ) coefficients defined as the ground-truth spectral data in the estimation algorithms using WR-PSO (section 3.3) and FWR-PSO (section 3.4) approaches.
Fig. 5
Fig. 5 Impact of the average < I > and maximum (Imax) number of iterations on the χ2 error for the WR-PSO and FWR-PSO-based approaches. Solid curves correspond to fitting functions whose meaning is indicated by their different associated symbols.
Fig. 6
Fig. 6 Impact of the χ2 value on the WR-PSO and FWR-PSO convergence success rates (RSS in %).
Fig. 7
Fig. 7 Dependence on χ2 value of estimated optical parameters error in scattering ( ε μ s, %) and absorption ( ε μ a, %) values during WR-PSO (section 3.3) and FWR-PSO (section 3.4) algorithms convergence

Tables (5)

Tables Icon

Algorithm 1 PSO-based algorithm applied to Spatially Resolved (SR) data

Tables Icon

Algorithm 2 Wavelength-Resolved (WR)-PSO based algorithm

Tables Icon

Table 1 Results obtained with the WR-PSO algorithm 2, page 13.

Tables Icon

Algorithm 3 Fitting modified (FWR)-PSO algorithm

Tables Icon

Table 2 Results obtained with the FWR-PSO algorithm 3 page 15.

Equations (9)

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f ( I exp , I m o d ) = 1 M K k = 1 K m = 1 M ( 1 I m o d ( λ k , r m ; p ( λ k ) I exp ( λ k , r m ) ) 2
f ( I exp , I m o d ) = k = 1 K ( I exp ( λ k ) I m o d ( λ k ; p ( λ k ) ) ) 2
f ( I exp , I m o d ) = m = 1 m = M [ k = 1 k = K ( I exp ( λ k , r m ) I m o d ( λ l , r m ; p ( λ k ) ) σ m ( λ k ) ) 2 + ( ϕ ( I exp ) ϕ ( I m o d ) A m ) 2 ]
I ( λ , r ) = 1 μ t ( λ ) ( μ e f f ( λ ) + 1 r 1 ( λ , r ) ) e μ e f f ( λ ) r 1 ( λ , r ) r 1 2 ( λ , r ) + ( 1 μ t ( λ ) + 2 z b ( λ ) ) ( μ e f f ( λ ) + 1 r 2 ( λ , r ) ) e μ e f f ( λ ) r 2 ( λ , r ) r 2 2 ( λ , r ) , with { D ( λ ) = 1 3 μ t ( λ ) ( diffusion coefficient ) μ e f f ( λ ) = μ a ( λ ) D ( λ ) ( effective attenuation coefficient ) μ t ( λ ) = μ a ( λ ) + μ s ( λ ) ( 1 g ( λ ) ) ( transport coefficient ) r 1 ( λ , r ) = 1 μ t 2 ( λ ) + r 2 r 2 ( λ , r ) = 2 ( 1 μ t 2 ( λ ) + 2 z b ( λ ) ) 2 + r 2   z b ( λ ) = 2 D ( λ ) ( extended boundary [ 34 ] ) .
χ j 2 ( λ k ) = 1 P M i = 1 P m = 1 M ( 1 I m o d ( λ k , r m ; p i ( λ k ) ) I exp ( λ k , r m ; p r e f ( λ k ) ) ) 2
{ V i t e r + 1 = w V i t e r + a 1 r a n d 1 ( g r B e s t j X i t e r ) + a 2 r a n d 2 ( g B e s t X i t e r ) , X i t e r + 1 = X i t e r + V i t e r + 1 , w = w M a x ( w M a x w M i n ) i t e r t h r e s I t e r
{ a 1 , a 2 [ 0.1 , 0.2 , , 2.9 , 3 ] w M i n [ 0.1 , 0.2 , , 2.8 , 2.9 ] w M a x [ 0.2 , 0.3 , , 2.9 , 3 ] ( w M a x > w M i n )
χ 2 = 1 K k = 1 K χ m i n 2 ( λ k )
ε μ i = 1 K k = 1 K ( μ i r e f ( λ k ) μ ˜ i ( λ k ) μ i r e f ( λ k ) ) × 100.

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