||使用光源與光偵測距離小於 3mm 之頻域光子遷移系統量測皮膚光學參數
||Determination of the skin optical properties from frequency domain photon migration measurements carried out at source to detector separations shorter than 3 mm
||Department of Photonics
diffuse reflectance spectroscopy
frequency-domain photon migration system
artificial neural networks
本研究中，我們提出以FDPM系統量測SDS=1mm之人體皮膚，為了改善傳統光子傳播模型的缺點 ( Monte Carlo model:光子傳播演算時間過久，diffusion theory:高反照率→縮減散射係數≫吸收係數，且光子在組織中的平均自由路徑長度≫1/(縮減散射係數+吸收係數))，將透過GPU-MCML建立大量的database，並經由類神經建立吸收係數、縮減散射係數隨頻率對應的振幅與相位延遲量的光子傳播模型，取代傳統Monte Carlo model與diffusion theory，本論文題出之方法比起Monte Carlo model其優勢為可快速模擬吸收係數、縮減散射係數隨頻率對應的振幅與相位延遲量，比起diffusion theory其優勢將不受到行走路徑的影響，且可應用於大範圍吸收係數、縮減散射係數組合。
以模擬實驗而言，Monte Carlo model為光子傳播模型的黃金標準，GPU-MCML在振幅方面與MCML的誤差為4%，相位延遲的誤差為6%，而diffusion theory在振幅方面與MCML的誤差為30%，相位延遲的誤差為17%。我們也以已知光學性質的均質假體驗證類神經模擬的可行性，因此就SDS=1mm的距離，類神經模型有其絕對的優勢。
In this study, we propose a method to measure human skin with SDS = 1mm by FDPM system. In order to improve the shortcomings of traditional photon propagation model, we established a new photon propagation model which combined the GPU-MCML and ANN method to derive the sample absorption coefficient、reduced scattering coefficient corresponding to the amplitude and phase delay with the frequency. The new model can substitute traditional Monte Carlo model and standard diffusion equation which have some using limitation.
In simulation, Monte Carlo is considered as a gold standard model of photon propagation. However it consumes lots of time to do the simulation. Compared with Monte Carlo, the percent deviation of amplitude and phase which simulated by artificial neural networks are about 4% and 6%, respectively. In the other hand, comparing with Monte Carlo, the percent deviation of amplitude and phase which simulated by standard diffusion equation are about 30% and 17%, respectively. Therefore compared with diffusion equation the new model with a 1mm source to detector separation indeed has its absolute advantage. We also use the homogeneous phantom which optical properties is known to confirm the feasibility of the artificial neural networks model.
In experience,we measured three position of three healthy adults, including the finger, the inner forearm and outer forearm with the artificial neural networks model extrapolated absorption coefficient、reduced scattering coefficient with three wavelengths. And then we put the absorption coefficient into the chromophore fitting to quantify the chromophore concentrations of tissue and StO2.
In conclusion, our new artificial neural networks model has better efficiency and wider applied range than other traditional model, which can accurately calculate the optical properties of the superficial skin tissue.
Chapter 1 研究動機與目的 1
Chapter 2 理論背景 5
2.1輻射轉換方程式(radiative transport equation, RTE) 5
2.1.2 輻射轉換方程式 6
2.1.3擴散理論(standard diffusion equation) 9
2.2蒙地卡羅法(Monte Carlo Model) 17
2.3 GPU蒙地卡羅法 21
2.3.1 Compute Unified Device Architecture (CUDA) 21
2.3.2 GPU-MCML 22
2.4.1 人工類神經網路架構 24
2.4.2 人工類神經網路收斂 26
Chapter 3材料與實驗方法 28
3.1 FDPM的實驗架構 28
3.2 GPU-MCML所產生大量數據透過人工類神經ANN建立模型 31
3.2.1 FDPM系統之類神經(FDPM-ANN) 31
3.3 假體配方 35
3.3.1 固態假體 35
3.4 量測數據計算物質光學特性 37
3.4.1 FDPM系統估算物質光學特性 37
3.4.2 色團濃度擬合 40
Chapter 4研究結果與討論 41
4.1 模擬比較與結果 41
4.1.1 GPU-MCML與 diffusion theory 正確性比較 42
4.1.2 人工類神經模擬結果 45
4.1.3 模擬淺層組織之光子分布探討 49
Chapter 5結論與未來工作 80
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