Probing depth in diffuse optical spectroscopy and structured illumination imaging: a Monte Carlo study

Daria A. Loginova (Login required)
Institute of Applied Physics RAS, Nizhny Novgorod, Russia
Lobachevsky State University of Nizhny Novgorod, Russia

Ekaterina A. Sergeeva
Institute of Applied Physics RAS, Nizhny Novgorod, Russia

Ilya I. Fiks
Institute of Applied Physics RAS, Nizhny Novgorod, Russia

Mikhail Yu. Kirillin
Institute of Applied Physics RAS, Nizhny Novgorod, Russia

Paper #3172 received 16 Mar 2017; revised manuscript received 2 Apr 2017; accepted for publication 4 Apr 2017; published online 6 Apr 2017.

DOI: 10.18287/JBPE17.03.010303


Diffuse optical spectroscopy (DOS) and its modification employing structured illumination are widely used in monitoring biotissue oxygenation. In such measurements it is important to know the probing volume for definite source-detector configuration; however, it cannot be measured directly. Monte Carlo simulations allow to trace the probing depth of each individual photon contributing to the signal, which provides a numerical solution for this problem. In this study we investigate distributions of photons over maximal depth reached in turbid media (probing depth) with optical parameters typical for cutaneous tissues at the wavelength of 600 nm. Different configurations of probing illumination are considered, such as collimated point source, one-dimension sinusoidal and rectangular patterns.  For collimated point source and zero source-detector separation the number of collected photons monotonously decreases with the probing depth while a pronounced maximum in the distribution is manifested with the increase of source-detector separation. The position of this maximum shifts to higher depths with the decrease of µa. For one-direction sinusoidal and rectangular illumination patterns it is shown that when the photons are collected near the center of a bright stripe, the peak of the distribution remains close to the surface. When the photons are collected near the center of a dark stripe the peak shifts towards higher depths with the decrease in spatial duty cycle and spatial frequency of the illumination pattern. Employment of rectangular illumination pattern seems more efficient for DOS applications due to wider abilities for controlling probing depth.


Monte Carlo simulations; optical diffuse spectroscopy; structured illumination; spatially modulated light; probing depth

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

2. T. D. O’Sullivan, A. E. Cerussi, D. J. Cuccia, and B. J. Tromberg, “Diffuse optical imaging using spatially and temporally modulated light,” J. Biomed. Opt.17(7), 071311 (2012).

3. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse Optics for Tissue Monitoring and Tomography,” Rep. Prog. Phys. 73(7), 76701 (2010).

4. N. Shah, A. E. Cerussi, D. Jakubowski, D. Hsiang, J. Butler, and B. J. Tromberg, “The role of diffuse optical spectroscopy in the clinical management of breast cancer,” Dis. Markers 19(2-3), 95-105 (2004). Crossref

5. D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).

6. A. Poellinger, “Near-infrared imaging of breast cancer using optical contrast agents,” J. Biophotonics 5(11-12), 815-826 (2012). Crossref

7. A. Torricelli, D. Contini, A. Pifferi, M. Caffini, R. Re, L. Zucchelli, and L. Spinelli, “Time domain functional NIRS imaging for human brain mapping,” Neuroimage 85(1), 28-50 (2014). Crossref

8. A. G. Orlova, M. Yu. Kirillin, A. B. Volovetsky, N. Yu. Shilyagina, E. A. Sergeeva, G. Yu. Golubiatnikov, and I. V. Turchin, “Diffuse optical spectroscopy monitoring of oxygen state and hemoglobin concentration during SKBR-3 tumor model growth,” Laser Physics Letters, 14(1), 015601 (2017). Crossref

9. 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(4), 879-888 (1992).

10. D. Y. Churmakov, I. V. Meglinski, and D.A. Greenhalgh, “Influence of refractive index matching on the photon diffuse reflectance,” Phys. Med. Biol. 47(23), 4271-4285 (2002).

11. C. C. Chuang, Y. T. Lee, C. M. Chen, Y. S. Hsieh, T. C. Liu, and C. W. Sun, “Patient-Oriented Simulation Based on Monte Carlo Algorithm by Using MRI Data,” Biomed. Eng. Online, 11(1), 21 (2012).

12. N. Dognitz, and G. Wagnieres, “Determination of tissue optical properties by steady-state spatial frequency-domain reflectometry,” Laser. Med. Sci. 13(1), 55-65 (1998).

13. D. J. Cuccia, F. Bevilacqua, A. J. Durkin, F. R. Ayers, and B. J. Tromberg, “Quantitation and mapping of tissue optical properties using modulated imaging,” J. Biomed. Opt. 14(2), 024012 (2009).

14. V. O. Korhonen, T. S. Myllylä, M.Yu. Kirillin, A. Popov, A. Bykov, A. V. Gorshkov, E. A. Sergeeva, M. Kinnunen, and V. Kiviniemi, “Light propagation in near-infrared spectroscopy of the human brain,” IEEE Journal of Selected Topics in Quantum Electronics, 20(2), 7100310 (2014). Crossref

15. T. Lister, P. A. Wright, and P. H. Chappell, “Optical properties of human skin,” J. Biomed. Opt. 17(9), 090901 (2012).

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