Uncertainty analysis in perturbation Monte Carlo simulations of radiative transport

Mahsa Parsanasab; Carole Hayakawa; Jerome Spanier; Yanning Shen; Vasan Venugopalan

doi:10.1117/12.2650969

7 March 2023 Uncertainty analysis in perturbation Monte Carlo simulations of radiative transport

Mahsa Parsanasab, Carole Hayakawa, Jerome Spanier, Yanning Shen, Vasan Venugopalan

Proceedings Volume 12376, Optical Tomography and Spectroscopy of Tissue XV; 1237603 (2023) https://doi.org/10.1117/12.2650969
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

In this paper, the accuracy of perturbation Monte Carlo(pMC) estimates as a function of scattering perturbation size for spatially-resolved diffuse reflectance over a broad range of optical properties is analyzed. We also propose a methodology to predict the variation of pMC variance with perturbation size based on the data from the reference simulation alone. The results show better pMC performance when based on results from reference Monte Carlo simulations that utilize a Russian Roulette as a variance reduction method. Specifically, we demonstrate that for a proximal detector we can estimate the pMC relative error within 5% of the true value for scattering perturbations in the range of [-15%, +20%]. For a distal, our method provides relative error estimates within 20% for scattering perturbations in the range of [-8%, +15%]. Moreover, improved performance is observed in case of both proximal and distal detectors when reference simulations performed at lower ( 𝜇𝑠 ′ 𝜇𝑎 ) values. This methodology is useful for the optimal design of pMC analysis of multi-spectral data sets.

Conference Presentation

Citation Download Citation

Mahsa Parsanasab, Carole Hayakawa, Jerome Spanier, Yanning Shen, and Vasan Venugopalan "Uncertainty analysis in perturbation Monte Carlo simulations of radiative transport", Proc. SPIE 12376, Optical Tomography and Spectroscopy of Tissue XV, 1237603 (7 March 2023); https://doi.org/10.1117/12.2650969

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