臺灣博碩士論文加值系統

18F-AV-45及18F-AV-133是最新發展的核醫藥物，分別可以評估阿茲海默症患者的類澱粉蛋白酶沉積，與帕金森氏症患者的VMAT2多巴胺神經元退化情形。為了觀察疾病進程及臨床診斷需求，藉由多中心臨床試驗收集的大量數據資料及功能性影像等資料，廣泛探討不同區域及群體範圍的變動性。為了使多中心試驗中不同PET影像系統所提供的影像資料能夠互相比較及互換，系統間的校正及資料轉換是很重要的研究課題。本論文主要是評估多中心臨床試驗研究中的影像品質，建立品質控制的評估參數。接著，利用3D Hoffman假體進行高頻修正與部份體積效應修正，取得各PET系統的高斯平滑曲線參數，以降低影像間的差異；另外也利用紋狀體假體推導出數據轉換方程，以進行PET影像的數據轉換。
結果發現，所有影像皆通過實驗中所設立的影像品質評估參數。利用高斯平滑曲線推導出的平滑化校正在兩個假體實驗的應用中皆可以降低影像的差異，讓資料可以互相比較。在紋狀體假體實驗所得出的一般性轉換方程可以應用在假體測試與退化不嚴重的臨床資料，但對於退化嚴重的區域，必須要重新定義特殊的轉換參數及VOI才能夠使影像量化資料達到有效互換。
從結果中可以歸納出，透過系統解析度校正可以降低影像的差異，使之可以在同一標準下互相比較；利用相關擬人化假體推導出的轉換參數與功能性VOI的考慮，可以有效應用於臨床上影像的量化資料轉換。

18F-AV-45 and 18F-AV-133 are novel PET tracers for imaging the deposition of amyloid in Alzheimer’s disease and the VMAT2 in dopaminergic neuron degeneration of Parkinson’s disease, respectively. For the purpose of observing the disease progression and the need in clinical diagnosis, we can widely investigate the variation between the control and diseased groups by collecting the data from the multicenter clinical trials. To establish the index of quality control assessment to evaluate the image quality in multicenter clinical trials was the first goal in this thesis. Then, in order to achieve comparability and interchangeability of information from different scanning systems, correcting the variation among images and data conversion from different scanners are the second focus in this thesis. Here, to correct the variation and reduce the variability between images, studies were performed using Hoffman phantom by first evaluating the resolution kernel for each system, and then applying smoothing filters to reduce high-frequency variability. In addition, study using the striatal phantom was used to derive the transformation factor for data conversion in PET images, and using another phantom data to validate the performance of the data conversion.
The results demonstrated that all images passed the criteria of quality control assessment. The derived FWHM kernels in high-frequency correction can reduce the variability and increase the comparability between scanners. With the application of the transformation factor, a minor percentage difference between scanners is shown in the retest phantom data. To evaluate its capability, the factor is also applied to the clinical data, and the result shows consistency with those in the phantom study. But for the severely declined area, a specific transformation factor should be used.
In conclusion, by applying the FWHM kernels in high frequency correction can reduce the variation between scanners, and make images comparable. Also, the derived transformation factor can be applied for data conversion between scanners.

目錄
論文指導教授推薦書
論文口試委員審定書
國家圖書館授權書 iii
長庚大學博碩士紙本論文著作授權書 iv
誌謝 v
中文摘要 vi
Abstract viii
目錄 x
表目錄 xiii
圖目錄 xv
第一章 前言 1
1-1功能性影像在神經退化性疾病的應用 1
1-2神經退化性疾病之多中心臨床試驗 4
1-3多中心臨床試驗之影像差異 8
1-4研究目的 10
第二章 多中心臨床試驗之影像品質控制及校正 11
2-1解剖性影像校正 11
2-2功能性核醫影像校正 13
2-2-1 SPECT 之多中心臨床試驗影像校正 13
2-2-2 PET 之多中心臨床試驗影像校正 18
第三章 研究材料與方法 24
3-1Hoffman 假體實驗 .24
3-1-1 Hoffman 假體 24
3-1-2 假體造影流程 25
3-1-3 影像分析 28
3-1-4 影像品質評估 30
3-2紋狀體假體實驗 43
3-2-1 紋狀體假體 43
3-2-2 紋狀體假體造影流程 45
3-2-3 影像分析 48
3-3臨床受試者造影 49
3-3-1 受試者與來源 49
3-3-2 造影流程 50
3-3-3 影像分析 50
第四章 研究結果 53
4-1Hoffman 假體實驗 53
4-1-1 正子造影系統在影像之表現 53
4-1-2 影像高頻修正與部份體積效應修正 54
4-1-3 紋理分析之應用 59
4-2紋狀體假體實驗 64
4-2-1 轉換參數及測試結果 64
4-2-2 轉換參數之臨床應用及限制 66
4-2-3 外核區域的高百分比差異校正 68
第五章 討論與結論 76
5-1系統間影像品質的比較：影像解析度之影響 78
5-1-1 影像高頻修正與影像品質 78
5-1-2 影像高頻增強修正與影像品質 81
5-2系統間分析結果的互換：數據轉換 84
5-3結論 87
5-4未來展望 89
參考文獻 91

表目錄
表 3 -1 分別將各組造影系統以(1) ~ (6)分別編號，詳列了各正子造影系統的造影條件與參數 27
表 3 -2 Hoffman 影像之 VOI 定義，並為各 VOI 標號 29
表 3 -3 配置相關濃度所需要的放射性藥物活度 46
表 3 -4 左右邊紋狀體注入的藥物濃度及墨水顏色 46
表 3 -5 GE 和 SE PET/CT 掃描參數 47
表 3 -6 受測者之臨床表徵 49
表 4 -1 各正子造影系統的影像評估標準結果 54
表 4 -2 高頻修正之下，為使各造影系統達到和標準影像相同影像解析度所需要的高斯平滑曲線程度 (FWHM：i mm) 55
表 4 -3 校正部份體積效應，使影像能夠達到 Centrum/aGM=25% (Hoffman 假體最初所設計的 GM 與 WM 比值) 所需要的de-convolution kernel 大小(mm) 56
表 4 -4 紋理分析結果─像素對比度：校正前後之紋理特性百分比差異 63
表 4 -5 紋理分析結果─像素同質性：校正前後之紋理特性百分比差異 63
表 4 -6 測試轉換參數之參考影像，其影像之原始 SBR 值、 利用轉換參數將 GE 轉換至 SE 的 SBR、GE 轉換後和 SE 原始值之百分比差異 66
表 4 -7 轉換參數之臨床應用結果。SUVRm為臨床初始量化值；%diff為 GE 系統上，經過轉換後的 SUVRm和 SE 系統的 SUVRm之百分比差異 67
表 4 -8 外核轉換參數所應用的 VOI：Original 表示目前臨床分析所應用的 VOI 大小；Modified 表依據受測者影像所修改的 VOI. 68
表 4 -9 分別利用一般的轉換參數及特殊的轉換參數與不同 VOI在臨床影像上所求得的轉換前後之 SUVR 百分比差異 (負值表示 GE系統轉換後的值較 SE 系統高) 71
表 4 -10 在不同程度高斯平滑曲線的應用下，經過畫素強度校正，GE和 SE 系統間在 VOI 計算中的 SBR 73
表 4-11 在不同程度高斯平滑曲線的應用下，GE 和 SE 系統間在 VOI計算中的百分比差異 74
表 4 -12 臨床資料經過高頻校正的百分比差異結果。%diff(smoothed)為經過smooth後的影像之間的SUVR百分比差異；%diff(SUVRm)為初始測量到的系統間之 SUVR 百分比差異 75
圖目錄
圖 2 -1 減少邊緣溢出效應：紅色區域以測量到的紋狀體值為主，綠色區域以量化到的背景值為輔。 16
圖 3 -1 Hoffman 假體示意圖 25
圖 3 -2 Hoffman 假體實驗流程 (材料準備、假體灌注、造影流程) 26
圖 3 -3 Hoffman 影像之 VOI 區域定義 29
圖 3 -4 計算衰減分率於尚未經過空間校正的影像 (A)中心丘腦之VOI 示意 (B)邊緣 VOI 示意 32
圖 3 -5 產生 GLCM 的灰階轉換方法 39
圖 3 -6 GLCM 計算中，不同的鄰近方向 40
圖 3 -7 藍色區域為外核(putamen)，紅色區域為尾核(caudate)；其他的空間則為剩餘的大腦區域 44
圖 3 -8 紋狀體假體隻造影流程 (材料準備、假體灌注、正子造影) 47
圖 3 -9 本研究所使用的 4 個 VOI 與參考區域；(A)CT 影像 (B)SE 正子影像 (C)GE 正子影像 48
圖 3 -10 本研究所使用的 6 個 VOI 與參考組織(視覺皮質區)，於其中一位受測者的 MRI 影像及 18F-AV-133 影像上示意圖 51
圖 4 -1 以global max當作畫素強度校正標準的初始與校正影像 (A)為各系統原始的影像 (B)為經過部份體積效應修正的影像 (C)為經過高頻修正後的影像 56
圖 4 -2 以最小平方差觀察經過高頻修正前後之影像和標準參考影像( R ) 的差異 (粉紅色圓型標示為經過高頻修正後的影像而藍色菱形標示為原始影像) 57
圖 4 -3 以均方根誤差 (%RMSE) 觀察各系統間校正前後的影像差異：(A) 為高頻修正前後各系統間的差異；(B)為部份體積效應修正前後各系統間的差異(橘色三角形標示為經過部份體積效應修正後的影像、粉紅色圓型標示為經過高頻修正後的影像而藍色菱形標示為原始影像) 58
圖 4 -4 紋理分析結果─像素對比度：VOI 在校正前後之紋理特性差異 61
圖 4 -5 紋理分析結果─像素同質性：VOI 校正前後之紋理特性差異 62
圖 4 -6 測試轉換參數準確性的參考影像 65
圖 4 -7 GE 和 SE 之間的轉換方程。x 軸為在 GE 影像上所肏量到的SBR、y 軸為在 SE 影像上所測量到的 SBR 值。 65
圖 4 -8 經由轉換參數應用的 18F-AV-133 臨床影像 (A)GE 原始影像(B)SE 原始影像 (C)經過轉換後的 GE 影像 67
圖 4 -9 針對低濃度 SBR 所定義出的特殊轉換參數 (specific transformation factor) 71
圖 4 -10 紋狀假體經過不同 FWHM kernel 後的高頻修正影像 (A)GE原始影像 (B)SE 原始影像 (C)以 FWHM 8mm 經過平滑化的 SE影像 (D) 以 FWHM 10.3mm 經過平滑化的 SE 影像 74
圖 4 -11 臨床受測者經過高頻修正後的影像示意圖 (A)GE 原始影像(B)SE 原始影像 (C)經過 smooth 後的 SE 影像 75

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