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SU‐D‐218‐06: Acceleration of Optical Photon Monte Carlo Simulations Using the Macro Monte Carlo Method
Author(s) -
Jacqmin D
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4734709
Subject(s) - monte carlo method , monte carlo method for photon transport , photon , physics , reflection (computer programming) , monte carlo molecular modeling , optics , computer science , markov chain monte carlo , mathematics , statistics , programming language
Purpose: The purpose of this work is to demonstrate that optical photon Monte Carlo simulations via a photon transport code called MCML can be accelerated using macro Monte Carlo (MMC) techniques. Methods: MCML was modified to incorporate the macro Monte Carlo radiation transport method. The original MCML uses scattering, absorption, reflection and refraction physics to transport optical photons through multi‐layered geometries. The code determines transmission, reflection and absorption for the layered geometry specified by the user. To make the code run faster, the MMC version of MCML uses large, multi‐interaction steps in regions that are homogeneous. These large steps are pre‐computed and stored in a database with many step sizes, materials and photon energies. The MMC version of MCML determines whether large MMC steps or traditional Monte Carlo should be used depending on the photon's current location inthe geometry. Results: The MMC version of MCML was tested against the original MCML code for a number of simple test geometries. It was also tested in anatomical geometries that are often uses in optical photon modeling. This includes skin and skull geometries. In each case, the reflection and transmission results from each code differed by less than 0.5%. The absorption data produced by each code also differed by less than 0.5% in most cases, and never differed by more than 2%. The MMC version of MCML runs between 1–3 times as many particles per unit time comparedto MCML, depending on the geometry. Conclusions: Applying Macro Monte Carlo methods to MCML produces a faster code without compromising accuracy. The speed‐gains are greatest in geometries thathave regions that are large relative to the mean scattering length for photons in that region. This work has the potential to accelerate light modeling for both photodynamic therapy and near‐infrared spectroscopic imaging.