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A Method for Medical Diagnosis Based on Optical Fluence Rate Distribution at Tissue Surface.

Omnia Hamdy1, Jala El-Azab2, Tarek A Al-Saeed3

  • 1Department of Engineering Applications of Laser, National Institute of Laser Enhanced Sciences (NILES), Cairo University, Giza Governorate 12613, Egypt. omnia@niles.edu.eg.

Materials (Basel, Switzerland)
|September 21, 2017
PubMed
Summary

Optical differentiation uses light properties to distinguish between tissue types safely and effectively. This study demonstrates how optical parameters and fluence rate distributions can visually aid in biomedical diagnosis.

Keywords:
Kubelika-Munk modeldiffuse reflectiondiffusion equationfinite element methodoptical fluence ratetissue optical parameters

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Area of Science:

  • Biomedical Optics
  • Biophotonics
  • Medical Imaging

Background:

  • Optical properties of biological tissues vary with histopathology, offering potential for diagnostic differentiation.
  • Optical fluence rate distribution at tissue boundaries is influenced by these optical parameters.
  • Visualizing fluence rate distributions can serve as a non-invasive diagnostic method.

Purpose of the Study:

  • To investigate the use of optical parameters and fluence rate distributions for differentiating between tissue types.
  • To develop and validate an experimental and computational approach for optical tissue analysis.

Main Methods:

  • Experimental measurement of diffuse reflectance and transmittance of chicken liver and skin samples at 635 and 808 nm.
  • In vitro calculation of optical parameters using modified Kubelka-Munk model and Bouguer-Beer-Lambert law.
  • Simulation of surface fluence rate distribution using the finite element method and verification with Monte Carlo simulations.

Main Results:

  • Spatially-resolved diffuse reflectance and transmittance were measured for native and altered tissue samples.
  • Optical parameters were successfully calculated and used to simulate fluence rate distributions.
  • Distinct diffuse reflectance curves and fluence rate distribution images were observed for different tissue types.

Conclusions:

  • Diffuse reflectance curves and simulated fluence rate distribution images can effectively discriminate between different tissue types.
  • Optical differentiation holds promise as a safe and visual tool for biomedical diagnosis.
  • The developed methodology provides a foundation for advanced optical diagnostic techniques.