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Nevin Manimala Statistics

A Computationally Efficient Monte-Carlo Model for Biomedical Raman Spectroscopy

J Biophotonics. 2021 Mar 18:e202000377. doi: 10.1002/jbio.202000377. Online ahead of print.

ABSTRACT

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through “isoweight”, a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800-2000cm-1 with an approximately 100x increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation. This article is protected by copyright. All rights reserved.

PMID:33733621 | DOI:10.1002/jbio.202000377

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