17 May 2018 A heuristic algorithm to calculate optical properties of turbid media
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Abstract
The interaction between biological tissues and light of a certain wavelength is influenced by the optical properties of each tissue. These are typically estimated from of the measurements of the main characteristics of the radiations pathway.

We herein present our approach that uses a customized version of the Monte Carlo Multi-Layer Algorithm (MCML)1 to simulate the radiation propagation through biological tissues. We assumed a set of optical properties for each tissue and simulated the above mentioned measurements in silico. A comparison was then done between the results of the simulation and the results of real measurements.2 Further, an optimization algorithm searched the set of optical properties that best fit the real optical properties of each tissue. This algorithm was based on adaptions of the Monte-Carlo Simulated Annealing algorithm3 and the Downhill Simplex algorithm4 We implemented the MCML using NVidias CUDA application programming interface to speed up the optimization procedure. We validated the software by using van de Hulsts table for Henyey-Greenstein scattering.2 A linear regression resulted in coefficients of determination between 0.929 and 0.973 for the optical properties. Our results prove that our algorithm can be effectively used for the determination of the optical properties of turbid media.

One first application for this software is the support in the development of a new generation of hearing devices based on optical energy.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marius Hinsberger, Marius Hinsberger, Stefan Naumann, Stefan Naumann, Klaus-Uwe Gollmer, Klaus-Uwe Gollmer, Achim Langenbucher, Achim Langenbucher, Bernhard Schick, Bernhard Schick, Gentiana Wenzel, Gentiana Wenzel, } "A heuristic algorithm to calculate optical properties of turbid media", Proc. SPIE 10685, Biophotonics: Photonic Solutions for Better Health Care VI, 106853O (17 May 2018); doi: 10.1117/12.2306778; https://doi.org/10.1117/12.2306778
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