Quantifying the fluorescence optical properties of two-layered tissue from spectroscopic data using fluorescence information table constructed by fluorescence Monte Carlo model
Date Issued
2015
Date
2015
Author(s)
Tien, Gen-Hao
Abstract
This research is to develop an inverse fluorescence spectra fitting tool, using the Monte Carlo model, to quantify the fluorescence optical parameters of tissue, hoping that we can acquire the tissue fluorescence information for analyzing and distinguishing the cancer tissues form normal ones. We use two different ways, MPG (Method of parameter grid) and MMC (Method of matrix convolution), to build the fluorescence information table, and apply them into our inverse fluorescence spectra fitting tool to reduce the time cost during the parameters extracted process, to make the inverse tool faster. We use different grid sizes of information table in our inverse fluorescence spectra fitting tool and analyze the correction of extracting parameters to get the ideas how big of information table we should build. When the size of information table is small, we find that MPG needs less memory and time than MMC does to build the information table. However, the result may reverse if the table size keeps increasing. We also analyze the fluorescence spectra noise depends on the portable spectroscopy we designed and analyze the diffusion optical parameters error which may have an effect on information table searching during parameters extracted process. We find that the former one does not have an obvious influence on the correction of extracting fluorescence optical parameters, while the later one does. In the future, we will use the inverse fluorescence spectra fitting tool to analyze the in vivo fluorescence spectra measured from our portable spectroscopy, and hoping that the optical parameters we get can help the tumor diagnosis.
Subjects
Fluorescence spectra
Fluorescence optical parameters
Monte Carlo model
Table
MPG (Method of parameter grid)
MMC (Method of matrix convolution)
SDGs
Type
thesis
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