Using GPU to accelerate Monte Carlo simulations and analyze diffuse reflectance and fluorescence spectra
Date Issued
2014
Date
2014
Author(s)
Hsu, Fang-Wei
Abstract
The development and progression of neoplasia will change the morphological structures of tissue, thus in turn affect the optical properties of tissue, diffuse reflectance spectra and fluorescence spectra. This research employs Monte Carlo algorithm to simulate how photons propagate in tissue and uses a curve-fitting tool to inversely extract the optical properties of diffuse reflectance spectra and fluorescence spectra. The inverse curve-fitting tool comprises GPU-version Monte Carlo program and MATALB interface for fitting.
In order to analyze diffuse reflectance spectra, we fabricate several two-layered tissue phantoms and use different fiber configurations to measure their spectra. In the process of curve-fitting, the Monte Carlo program would take different fiber configurations and phase functions into account. Compared with perpendicular fiber configuration, oblique fiber configuration is more sensitive to change of phase function and more capable of extracting optical properties of epithelium. For example, the error for thickness of upper layer is about 9.83% and the RMS percentage error for scattering coefficient of upper layer is about 12.17%.
As for fluorescence spectra, we first refer some literature to set the optical properties of normal tissue and pathological tissue. And then we simulate spectra under different fiber conditions to see whether the fraction of detected fluorescence from upper layer would increase or not. According to the simulation results, we find out the fluorescence intensity measured from dysplasia tissue is lower than the one from normal tissue and most of the detected fluorescence from dysplasia tissue comes from upper layer. In addition, oblique fiber configuration can more effectively detect fluorescence from upper layer than perpendicular fiber configuration. However, increasing the tilted angle of fibers doesn’t necessarily mean the increase in fraction of detected fluorescence from upper layer. In order to do the curve-fitting for fluorescence spectra, we add 3% of noise to the theoretical spectra and use the noise-added spectra as the input. Then we inversely extract the fluorescence parameters, such as the quantum yield and the absorption coefficient at excitation wavelength of certain fluorophore. Fluorescence efficiency, the product of quantum yield and absorption coefficient, is what we pay most attention in this research. The reason is because a fluorophore’s fluorescence efficiency represents the fluorophore’s capacity for emitting fluorescence and it also serves as a standard for comparing different fluorophores. Though the extracted fluorescence parameters vary significantly from theoretical values, the analysis of fluorescence efficiency shows a much more acceptable result. The average error of normal tissue’s fluorescence efficiency is about 20.75% for upper layer and 15.5% for bottom layer. The average error of dysplasia tissue’s fluorescence efficiency is about 17.75% for upper layer and 12.5% for bottom layer. This result demonstrates our proposed fitting procedure for fluorescence spectra can really help us extract the real value of fluorescence efficiency, which helps a lot for measuring fluorescence spectra by experiment in the future.
Subjects
漫反射光譜
螢光光譜
蒙地卡羅
仿體
GPU
Type
thesis
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