MCMLpar and MCSLinv: A Parallel Version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters

Published

October 20, 2017

Author(s)

Richelle H. Streater, Anne-Michelle R. Lieberson, Adam L. Pintar, Zachary H. Levine

Abstract

The MCML program for Monte Carlo modeling of light transport in multi-layered tissues has been widely used in the past 20 years or so. Here, we have re-implemented MCML for solving the inverse problem. Our formulation features optimizing the profile log likelihood which takes into account uncertainties due to both experimental and Monte Carlo sampling. We limit the search space for the optimum parameters with relatively few Monte Carlo trials and then iteratively double the number of Monte Carlo trials until the search space stabilizes. At this point, the log likelihood can be fit with a quadratic function to find the optimum. The time-to-solution is only a few minutes in typical cases because we use importance sampling to determine the log likelihood on a grid of parameters at each iteration. Also, our implementation uses OpenMP and SPRNG to generate Monte Carlo trials in parallel.

Citation

Journal of Research (NIST JRES) -

Volume

122

NIST Pub Series

Journal of Research (NIST JRES)

Pub Type

NIST Pubs

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DOI Link

Keywords

biomedical optics, importance sampling, inverse, Monte Carlo, scattering parameters

Citation

Streater, R. , Lieberson, A. , Pintar, A. and Levine, Z. (2017), MCMLpar and MCSLinv: A Parallel Version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.122.038 (Accessed October 26, 2025)