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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79976完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 宋孔彬(Kung-Bin Sung) | |
| dc.contributor.author | Tzu-Chia Kao | en |
| dc.contributor.author | 高子佳 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:19:18Z | - |
| dc.date.available | 2022-08-01 | |
| dc.date.available | 2022-11-23T09:19:18Z | - |
| dc.date.copyright | 2021-08-18 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-22 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79976 | - |
| dc.description.abstract | 本研究目的為定量活體受試者的頭部光學參數，以非侵入式的連續波近紅外漫反射光譜系統量測受試者的活體光譜，並且以數值模型進行光譜擬合，定量得到受試者右前額的頭皮、頭骨、灰質層的吸收係數與散射係數，可用於分析功能性近紅外光譜量測資料與模擬經顱紅外雷射刺激的能量分布等。 為得到穩定的活體寬頻近紅外光譜，本研究以鹵素燈作為光源並以光譜儀作為偵測儀器，以光纖導光作為光源與多個距離在0.8 cm至4.5 cm之間的偵測器，並且自製弧形探頭以穩定貼附受試者右前額，針對不同偵測器使用不同曝光時間使信號足夠穩定，並且進行多次測量確保量得穩定、具代表性的光譜。量測光譜經過校正仿體進行校正，可得到可用波常在700 nm至900 nm的活體光譜。 在數值模型上，本研究將受試者的核磁共振影像分割為頭皮、頭骨、腦脊髓液、灰質、白質與額竇共六種組織，並轉換為可供蒙地卡羅光學模擬的三維模型。為加快模擬速度，本研究使用大數值孔徑模擬漫反射光譜，經由迴歸模型轉換為與光纖相符的數值孔徑；針對每位受試者以尋找表配合白蒙地卡羅法產生大量訓練資料以訓練類神經網路，所得到的類神經網路可取代蒙地卡羅法，將輸入的光學參數快速計算成漫反射模擬光譜。 將活體光譜作為擬合目標，使用遞迴式曲線擬合調整組織模型的光學參數，使模擬光譜最接近活體光譜，從而得到組織光學參數。本研究共定量五位健康受試者的光學參數，五位受試者的光學參數差異大於因活體量測誤差造成的光學參數誤差，且大多在文獻參數的合理範圍之內。使用擬合得到的光學參數模擬灰質路徑長與雷射刺激能量分布，可發現結果與使用文獻值有顯著差異，可見本方法有其重要性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:19:18Z (GMT). No. of bitstreams: 1 U0001-2207202115403700.pdf: 17989019 bytes, checksum: 5a88b6e412faaf09b1f115d33f412f7e (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 致謝 ii 中文摘要 iii Abstract v 圖目錄 ix 表目錄 xiv 第1章. 緒論 1 1.1. 前言 1 1.2. 研究動機 2 1.3. 文獻回顧 4 1.3.1. 光學參數 4 1.3.2. 以NIRS測量光學參數 9 第2章. 原理 11 2.1. 近紅外漫反射光譜 11 2.2. 曲線擬合 15 2.2.1. 曲線擬合基本原理 15 2.2.2. 疊代式曲線擬合於本研究中的應用 18 2.3. 蒙地卡羅法 18 2.3.1. 蒙地卡羅法原理 18 2.3.2. 蒙地卡羅法於本研究中的應用 32 2.4. 白蒙地卡羅法 33 2.5. 尋找表 34 2.5.1. 尋找表介紹 34 2.5.2. 尋找表於本研究中的應用 36 2.6. 人工神經網路 37 2.6.1. 人工神經網路介紹 37 2.6.2. 人工神經網路於本研究中的應用 40 第3章. 研究方法 41 3.1. 模擬模型建立 42 3.1.1. 活體大腦模型 42 3.1.2. 仿體模型 46 3.2. 靈敏度分析 49 3.2.1. 不同SDS對各層組織的靈敏度 49 3.2.2. 選取適合本研究的SDS 54 3.3. 硬體與實驗 56 3.3.1. 硬體架構 56 3.3.2. 漫反射光譜量測 62 3.3.3. 校正結果 72 3.4. 順向模型建立 75 3.4.1. 使用大數值孔徑模擬 76 3.4.2. 建立尋找表 81 3.4.3. 建立人工神經網路 85 3.5. 曲線擬合 91 3.5.1. 擬合參數 91 3.5.2. 擬合工具 92 3.5.3. 進行擬合 93 第4章. 研究結果與討論 95 4.1. 誤差分析 95 4.1.1. 擬合理論誤差 95 4.1.2. 系統雜訊 101 4.1.3. 活體量測信號的變動 106 4.1.4. 影響最佳化因素 116 4.2. 初步擬合結果 118 4.2.1. 選擇適合的SDS組合 118 4.2.2. 有無多重解 121 4.3. 最終擬合結果 128 4.3.1. 擬合光譜結果 128 4.3.2. 擬合光學參數結果 132 4.4. 於應用之效果 134 4.4.1. 路徑長模擬結果 137 4.4.2. TILS能量模擬結果 138 4.5. 實際應用可行性 141 第5章. 結論與未來展望 142 5.1. 結論 142 5.1.1. 順向模型建立 143 5.1.2. 定量參數誤差 143 5.1.3. 活體實驗 143 5.2. 未來展望 144 5.2.1. 縮小參數誤差 144 5.2.2. 減少模型與活體差異 144 5.2.3. 加速建立模型 145 參考文獻 146 | |
| dc.language.iso | zh-TW | |
| dc.subject | 頭部光學參數 | zh_TW |
| dc.subject | 漫反射光譜 | zh_TW |
| dc.subject | 近紅外光譜 | zh_TW |
| dc.subject | 蒙地卡羅模擬 | zh_TW |
| dc.subject | 尋找表 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 光譜擬合 | zh_TW |
| dc.subject | Spectrum fitting | en |
| dc.subject | Head optical parameters | en |
| dc.subject | Diffusion reflection spectrum (DRS) | en |
| dc.subject | Near-infrared spectroscopy (NIRS) | en |
| dc.subject | Monte Carlo simulation | en |
| dc.subject | Lookup table | en |
| dc.subject | Artificial neural networks | en |
| dc.title | 以連續波近紅外光譜與三維模型定量人體腦部光學參數 | zh_TW |
| dc.title | Quantify Optical Properties in Human Brain Using Continuous Wave Near-Infrared Spectrum and Three-Dimensional Model | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0001-6204-3979 | |
| dc.contributor.advisor-orcid | 宋孔彬(0000-0002-7253-1332) | |
| dc.contributor.oralexamcommittee | 盧家鋒(Hsin-Tsai Liu),曾雪峰(Chih-Yang Tseng) | |
| dc.subject.keyword | 漫反射光譜,近紅外光譜,蒙地卡羅模擬,尋找表,類神經網路,光譜擬合,頭部光學參數, | zh_TW |
| dc.subject.keyword | Diffusion reflection spectrum (DRS),Near-infrared spectroscopy (NIRS),Monte Carlo simulation,Lookup table,Artificial neural networks,Spectrum fitting,Head optical parameters, | en |
| dc.relation.page | 151 | |
| dc.identifier.doi | 10.6342/NTU202101665 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-22 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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| U0001-2207202115403700.pdf | 17.57 MB | Adobe PDF | 檢視/開啟 |
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