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Proceedings Paper

A non-stochastic iterative computational method to model light propagation in turbid media
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Paper Abstract

Monte Carlo models are widely used to model light transport in turbid media, however their results implicitly contain stochastic variations. These fluctuations are not ideal, especially for inverse problems where Jacobian matrix errors can lead to large uncertainties upon matrix inversion. Yet Monte Carlo approaches are more computationally favorable than solving the full Radiative Transport Equation. Here, a non-stochastic computational method of estimating fluence distributions in turbid media is proposed, which is called the Non-Stochastic Propagation by Iterative Radiance Evaluation method (NSPIRE). Rather than using stochastic means to determine a random walk for each photon packet, the propagation of light from any element to all other elements in a grid is modelled simultaneously. For locally homogeneous anisotropic turbid media, the matrices used to represent scattering and projection are shown to be block Toeplitz, which leads to computational simplifications via convolution operators. To evaluate the accuracy of the algorithm, 2D simulations were done and compared against Monte Carlo models for the cases of an isotropic point source and a pencil beam incident on a semi-infinite turbid medium. The model was shown to have a mean percent error less than 2%. The algorithm represents a new paradigm in radiative transport modelling and may offer a non-stochastic alternative to modeling light transport in anisotropic scattering media for applications where the diffusion approximation is insufficient.

Paper Details

Date Published: 12 March 2015
PDF: 11 pages
Proc. SPIE 9319, Optical Tomography and Spectroscopy of Tissue XI, 931918 (12 March 2015); doi: 10.1117/12.2079656
Show Author Affiliations
Thomas J. McIntyre, Univ. of Alberta (Canada)
Roger J. Zemp, Univ. of Alberta (Canada)

Published in SPIE Proceedings Vol. 9319:
Optical Tomography and Spectroscopy of Tissue XI
Bruce J. Tromberg; Arjun G. Yodh; Eva Marie Sevick-Muraca; Robert R. Alfano, Editor(s)

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