Methods for variance reduction in Monte Carlo simulations

Joel N. Bixler; Brett H. Hokr; Aidan Winblad; Gabriel Elpers; Byron Zollars; Robert J. Thomas

doi:10.1117/12.2213470

7 March 2016 Methods for variance reduction in Monte Carlo simulations

Joel N. Bixler, Brett H. Hokr, Aidan Winblad, Gabriel Elpers, Byron Zollars, Robert J. Thomas

Proceedings Volume 9706, Optical Interactions with Tissue and Cells XXVII; 970612 (2016) https://doi.org/10.1117/12.2213470
Event: SPIE BiOS, 2016, San Francisco, California, United States

Abstract

Monte Carlo simulations are widely considered to be the gold standard for studying the propagation of light in turbid media. However, due to the probabilistic nature of these simulations, large numbers of photons are often required in order to generate relevant results. Here, we present methods for reduction in the variance of dose distribution in a computational volume. Dose distribution is computed via tracing of a large number of rays, and tracking the absorption and scattering of the rays within discrete voxels that comprise the volume. Variance reduction is shown here using quasi-random sampling, interaction forcing for weakly scattering media, and dose smoothing via bi-lateral filtering. These methods, along with the corresponding performance enhancements are detailed here.

Conference Presentation

Citation Download Citation

Joel N. Bixler, Brett H. Hokr, Aidan Winblad, Gabriel Elpers, Byron Zollars, and Robert J. Thomas "Methods for variance reduction in Monte Carlo simulations", Proc. SPIE 9706, Optical Interactions with Tissue and Cells XXVII, 970612 (7 March 2016); https://doi.org/10.1117/12.2213470

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