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.
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