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

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.

[1]  Haiou Shen,et al.  A tetrahedron-based inhomogeneous Monte Carlo optical simulator , 2010, Physics in medicine and biology.

[2]  E D Aydin Three-dimensional photon migration through voidlike regions and channels. , 2007, Applied optics.

[3]  Jari P. Kaipio,et al.  Finite element model for the coupled radiative transfer equation and diffusion approximation , 2006 .

[4]  S R Arridge,et al.  Recent advances in diffuse optical imaging , 2005, Physics in medicine and biology.

[5]  Lihong V. Wang,et al.  Biomedical Optics: Principles and Imaging , 2007 .

[6]  David A Boas,et al.  Comparison of simplified Monte Carlo simulation and diffusion approximation for the fluorescence signal from phantoms with typical mouse tissue optical properties. , 2007, Applied optics.

[7]  Ashleyj . Welch,et al.  Optical-Thermal Response of Laser-Irradiated Tissue , 1995 .

[8]  L Wang,et al.  MCML--Monte Carlo modeling of light transport in multi-layered tissues. , 1995, Computer methods and programs in biomedicine.

[9]  Robert M. Gray,et al.  Toeplitz and Circulant Matrices: A Review , 2005, Found. Trends Commun. Inf. Theory.

[10]  K. Paulsen,et al.  Spatially varying optical property reconstruction using a finite element diffusion equation approximation. , 1995, Medical physics.

[11]  B. Hooper Optical-thermal response of laser-irradiated tissue , 1996 .

[12]  Alwin Kienle,et al.  Exact and efficient solution of the radiative transport equation for the semi-infinite medium , 2013, Scientific Reports.

[13]  Aldo Badano,et al.  Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. , 2009, Medical physics.

[14]  Leo Grady,et al.  Discrete Calculus - Applied Analysis on Graphs for Computational Science , 2010 .