Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation

Omnia Hamdy

doi:doi:10.11648/j.aas.20180303.12

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Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation

Omnia Hamdy

Published in Advances in Applied Sciences (Volume 3, Issue 3)

Received: 13 July 2018 Accepted: 31 July 2018 Published: 22 August 2018

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Abstract

Medical diagnosis with optical techniques is favorable due to its safe and painless features. Every tissue type can be distinguished by its optical absorption and scattering properties that are related to many physiological changes and considered to be very important signs for tissue heath. Characterizing light propagation in the human brain tissues is a vital issue in many diagnostic and therapeutic applications. In this work, light propagation in different brain tissues in normal and coagulated state was investigated. A Monte-Carlo simulation model was implemented to obtain spatially resolved steady state diffuse reflectance profiles of the examined tissues. Furthermore, the diffusion equation was solved to create images presenting the optical fluence rate distribution at the tissue surface using the finite element method. The proposed diffuse reflectance curves and fluence rate images show different features regarding tissue type and condition that promises to be effective in medical diagnosis.

Published in	Advances in Applied Sciences (Volume 3, Issue 3)
DOI	10.11648/j.aas.20180303.12
Page(s)	28-33
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Monte-Carlo simulation of Light Propagation, Tissue Optical Parameters, Diffusion Equation, Finite Element Method

References

[1]	L. V. Wang and H. Wu, Biomedical Optics: Principles and Imaging. Canada: Wiley-Interscience, 2007.
[2]	O. Hamdy, M. Fathy, T. A. Al Saeed, J. M. EL-Azab and N. H. Solouma, "Estimation of optical parameters and fluence rate distribution in biological tissues via a single integrating sphere optical setup," Optik, vol. 140, pp. 1004–1009, 2017.
[3]	D. A. Boas, C. Pitris and N. Ramanujam, Hand Book of Biomedical Optics. USA: CRC Press, 2011.
[4]	N. H. Markolf, Laser-Tissue Interactions: Fundamentals and Applications. Germany: Springer, 2007.
[5]	C. Zhu and Q. Liu, "Review of Monte Carlo Modeling of Light Transport in Tissue," Biomedical Optics, vol. 18, no. 5, pp. 050902-1-12, 2013.
[6]	O. Hamdy, J. El-Azab, N. H. Solouma, M. Fathy and T. A. AL-Saeed, "The Use of Optical Fluence Rate Distribution for the Differentiation of Biological Tissues," in 8th Cairo International Biomedical Engineering Conference (CIBEC), Egypt, 2016.
[7]	S. Leyre, J. Cappelle, G. Durinck, A. Abass, J. Hofkens, G. Deconinck and P. Hanselaer, "The use of the adding-doubling method for the optical optimization of planar luminescent down shifting layers for solar cells," Optics Express, vol. 22, no. S3, pp. A765-A778, 2014.
[8]	O. Hamdy, J. El-Azab, T. A. AL-Saeed, M. Fathy and N. H. Solouma, "A Method for Medical Diagnosis Based on Optical Fluence Rate Distribution at Tissue Surface," Materials, no. 10, pp. 1104-13, 2017.
[9]	I. Fredriksson, M. Larsson and T. Strömberg, "Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy," Journal of Biomedical Optics, vol. 17, no. 4, pp. 047004-1-12, 2012.
[10]	J. Yao, "Inverse adding-doubling method for the determination of optical properties of thermotropic material," in Proc. SPIE 7749, 2010 International Conference on Display and Photonics, China, 2010.
[11]	V. Periyasamy, M. Pramanik, " Advances in Monte Carlo Simulation for Light Propagation in Tissue", IEEE Reviews in Biomedical Engineering, vol. 10, pp. 122 - 135, 2017.
[12]	M. Atif, A. Khan and M. Ikram, "Modeling of Light Propagation in Turbid Medium Using Monte Carlo Simulation Technique1," Optics and Spectroscopy, vol. 111, no. 1, p. 107–112, 2011.
[13]	C. Zhu and Q. Liu, "Review of Monte Carlo modeling of light transport in tissues," Journal of Biomedical Optics, vol. 18, no. 5, pp. 050902-1-12, 2013.
[14]	A. J. Welch, M. J. C. van Gemert, Optical-Thermal Responses of Laser-Irradiated Tissue, 2nd Edition, Springer, 2009.
[15]	D. Wangpraseurt, S. L. Jacques, T. Petrie and M. Kühl, "Monte Carlo Modeling of Photon Propagation Reveals Highly Scattering Coral Tissue," Front. Plant Sci., 2016.
[16]	O. K. Dudko and G. H. Weiss, "Photon diffusion in biological tissues," Diffusion Fundamentals, vol. 2, no. 2005, pp. 114.1-114.21, 2005.
[17]	T. Durduran, R. Choe, W. B. Baker and A. G. Yodh, "Diffuse Optics for Tissue Monitoring and Tomography," Rep. Prog. Phys, vol. 73, no. 076701, pp. 1-43, 2010.
[18]	R. Zhang, W. Verkruysse, G. Aguilar and J. Nelson, "Comparison of diffusion approximation and Monte Carlo based finite element models for simulating thermal responses to laser irradiation in discrete vessels.," Phys Med Biol., vol. 50, no. 17, pp. 4075-86., 2005.
[19]	G. Gratton and M. Fabiani, "Fast optical imaging of human brain function," Frontiers in Human Neuroscience, vol. 4, pp. 52-1-9, 2010.
[20]	S. J. Madsen, B. C. Wilson, "Optical Properties of Brain Tissue" In: Madsen S. (eds) Optical Methods and Instrumentation in Brain Imaging and Therapy. Bioanalysis (Advanced Materials, Methods, and Devices), vol 3. Springer, New York, 2013.
[21]	H. Wang, C. Magnain, S. Sakadžić, B. Fischl, D. A. Boas, "Characterizing the optical properties of human brain tissue with high numerical aperture optical coherence tomography", Biomedical Optics Express, vol. 8, no. 2, pp. 5617–5636, 2017.
[22]	L. Wang, S. L. Jacques and L. Zheng, "MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues," Computer Methods and Programs in Biomedicine, vol. 47, no. 2, pp. 131-146, 1995.
[23]	V. V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments in Medical Diagnosis, United States of America: SPIE, 2007.

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APA Style

Omnia Hamdy. (2018). Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation. Advances in Applied Sciences, 3(3), 28-33. https://doi.org/10.11648/j.aas.20180303.12

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ACS Style

Omnia Hamdy. Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation. Adv. Appl. Sci. 2018, 3(3), 28-33. doi: 10.11648/j.aas.20180303.12

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AMA Style

Omnia Hamdy. Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation. Adv Appl Sci. 2018;3(3):28-33. doi: 10.11648/j.aas.20180303.12

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@article{10.11648/j.aas.20180303.12,
  author = {Omnia Hamdy},
  title = {Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation},
  journal = {Advances in Applied Sciences},
  volume = {3},
  number = {3},
  pages = {28-33},
  doi = {10.11648/j.aas.20180303.12},
  url = {https://doi.org/10.11648/j.aas.20180303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20180303.12},
  abstract = {Medical diagnosis with optical techniques is favorable due to its safe and painless features. Every tissue type can be distinguished by its optical absorption and scattering properties that are related to many physiological changes and considered to be very important signs for tissue heath. Characterizing light propagation in the human brain tissues is a vital issue in many diagnostic and therapeutic applications. In this work, light propagation in different brain tissues in normal and coagulated state was investigated. A Monte-Carlo simulation model was implemented to obtain spatially resolved steady state diffuse reflectance profiles of the examined tissues. Furthermore, the diffusion equation was solved to create images presenting the optical fluence rate distribution at the tissue surface using the finite element method. The proposed diffuse reflectance curves and fluence rate images show different features regarding tissue type and condition that promises to be effective in medical diagnosis.},
 year = {2018}
}

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TY  - JOUR
T1  - Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation
AU  - Omnia Hamdy
Y1  - 2018/08/22
PY  - 2018
N1  - https://doi.org/10.11648/j.aas.20180303.12
DO  - 10.11648/j.aas.20180303.12
T2  - Advances in Applied Sciences
JF  - Advances in Applied Sciences
JO  - Advances in Applied Sciences
SP  - 28
EP  - 33
PB  - Science Publishing Group
SN  - 2575-1514
UR  - https://doi.org/10.11648/j.aas.20180303.12
AB  - Medical diagnosis with optical techniques is favorable due to its safe and painless features. Every tissue type can be distinguished by its optical absorption and scattering properties that are related to many physiological changes and considered to be very important signs for tissue heath. Characterizing light propagation in the human brain tissues is a vital issue in many diagnostic and therapeutic applications. In this work, light propagation in different brain tissues in normal and coagulated state was investigated. A Monte-Carlo simulation model was implemented to obtain spatially resolved steady state diffuse reflectance profiles of the examined tissues. Furthermore, the diffusion equation was solved to create images presenting the optical fluence rate distribution at the tissue surface using the finite element method. The proposed diffuse reflectance curves and fluence rate images show different features regarding tissue type and condition that promises to be effective in medical diagnosis.
VL  - 3
IS  - 3
ER  -

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Author Information

Omnia Hamdy

Department of Engineering Applications of Laser, National Institute of Laser Enhanced Sciences, Giza, Egypt

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APA Style

Omnia Hamdy. (2018). Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation. Advances in Applied Sciences, 3(3), 28-33. https://doi.org/10.11648/j.aas.20180303.12

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ACS Style

Omnia Hamdy. Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation. Adv. Appl. Sci. 2018, 3(3), 28-33. doi: 10.11648/j.aas.20180303.12

Copy | Download

AMA Style

Omnia Hamdy. Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation. Adv Appl Sci. 2018;3(3):28-33. doi: 10.11648/j.aas.20180303.12

Copy | Download

@article{10.11648/j.aas.20180303.12,
  author = {Omnia Hamdy},
  title = {Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation},
  journal = {Advances in Applied Sciences},
  volume = {3},
  number = {3},
  pages = {28-33},
  doi = {10.11648/j.aas.20180303.12},
  url = {https://doi.org/10.11648/j.aas.20180303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20180303.12},
  abstract = {Medical diagnosis with optical techniques is favorable due to its safe and painless features. Every tissue type can be distinguished by its optical absorption and scattering properties that are related to many physiological changes and considered to be very important signs for tissue heath. Characterizing light propagation in the human brain tissues is a vital issue in many diagnostic and therapeutic applications. In this work, light propagation in different brain tissues in normal and coagulated state was investigated. A Monte-Carlo simulation model was implemented to obtain spatially resolved steady state diffuse reflectance profiles of the examined tissues. Furthermore, the diffusion equation was solved to create images presenting the optical fluence rate distribution at the tissue surface using the finite element method. The proposed diffuse reflectance curves and fluence rate images show different features regarding tissue type and condition that promises to be effective in medical diagnosis.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation
AU  - Omnia Hamdy
Y1  - 2018/08/22
PY  - 2018
N1  - https://doi.org/10.11648/j.aas.20180303.12
DO  - 10.11648/j.aas.20180303.12
T2  - Advances in Applied Sciences
JF  - Advances in Applied Sciences
JO  - Advances in Applied Sciences
SP  - 28
EP  - 33
PB  - Science Publishing Group
SN  - 2575-1514
UR  - https://doi.org/10.11648/j.aas.20180303.12
AB  - Medical diagnosis with optical techniques is favorable due to its safe and painless features. Every tissue type can be distinguished by its optical absorption and scattering properties that are related to many physiological changes and considered to be very important signs for tissue heath. Characterizing light propagation in the human brain tissues is a vital issue in many diagnostic and therapeutic applications. In this work, light propagation in different brain tissues in normal and coagulated state was investigated. A Monte-Carlo simulation model was implemented to obtain spatially resolved steady state diffuse reflectance profiles of the examined tissues. Furthermore, the diffusion equation was solved to create images presenting the optical fluence rate distribution at the tissue surface using the finite element method. The proposed diffuse reflectance curves and fluence rate images show different features regarding tissue type and condition that promises to be effective in medical diagnosis.
VL  - 3
IS  - 3
ER  -

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