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系統識別號 U0026-1208201515032400
論文名稱(中文) 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
論文名稱(英文) Determination of the skin optical properties from frequency domain photon migration measurements carried out at source to detector separations shorter than 3 mm
校院名稱 成功大學
系所名稱(中) 光電科學與工程學系
系所名稱(英) Department of Photonics
學年度 103
學期 2
出版年 104
研究生(中文) 黃柏蓉
研究生(英文) Po-Jung Huang
學號 L76024161
學位類別 碩士
語文別 中文
論文頁數 86頁
口試委員 指導教授-曾盛豪
口試委員-洪明原
口試委員-陳智揚
口試委員-孫家偉
口試委員-詹明哲
中文關鍵字 漫反射光譜  頻域光子遷移系統  淺層組織  血氧飽和濃度  人工類神經 
英文關鍵字 diffuse reflectance spectroscopy  frequency-domain photon migration system  superficial tissue  artificial neural networks 
學科別分類
中文摘要 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。
以人體皮膚量測而言,我們以3個受測者為例,並個別量測三個部位,分別是手指、前手臂內側、前手臂外側,經由類神經傳播模型推算出吸收係數、縮減散射係數,由三個波長下的吸收係數進行chromophore fitting,分析組織的血氧飽和濃度,達到量化組織色團濃度的目的。
簡而言之,類神經模型是快速且應用範圍廣泛的。而以本研究中訓練的人體皮膚範為的吸收係數、縮減散射係數其最大偵測深度約為1.6mm。透過SDS=1mm之類神經光子傳播模型的建立與驗證可行性下,本研究將可以精確的計算出淺層皮膚的光學性質。
英文摘要 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.
論文目次 摘要 I
致謝 V
符號列表 VI
目錄 VII
圖目錄 IX
表目錄 XIII
Chapter 1 研究動機與目的 1
1.1研究背景 1
1.2研究動機 3
1.3研究目的 4
Chapter 2 理論背景 5
2.1輻射轉換方程式(radiative transport equation, RTE) 5
2.1.1參數定義 5
2.1.2 輻射轉換方程式 6
2.1.3擴散理論(standard diffusion equation) 9
2.1.4邊界條件 12
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人工類神經網路 24
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
4.2量測數據分析與探討 50
4.2.1系統穩定度 50
4.2.2重複組數與重複組數間最大誤差百分比 54
4.2.3正向類神經模型結合最小平方法之反向模型 57
4.2.4類神經模型與漫反射模型演算法應用於實驗量測上之比較 60
4.2.5類神經模型應用於人體皮膚的量測 65
Chapter 5結論與未來工作 80
5.1結論 80
5.2未來工作 82
參考文獻 84
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