臺灣博碩士論文加值系統

阿茲海默症(Alzheimer’s disease)是最常見的失智症之一，目前還沒有有效的方法與藥物治療或減緩疾病的發生。藥物的研究發展上，主要是針對大腦不正常代謝的類澱粉β胜肽(amyloid-, Aβ)與tau蛋白作治療。目前所有治療阿茲海默症藥物均未成功通過第三期臨床試驗(Phase III clinical trials)。主要的原因可能是受試驗的對象，在臨床試驗中沒有被有效的診斷出疾病的致病因子，並針對病因作治療。目前正迫切的需要發展一套早期診斷工具，若能在發病前，藉由準確的診斷工具，將對病人大腦惡化不同的程度檢測出來，對未來診斷及藥物的開發上將有所幫助。目前使用18F-AV1451作為追蹤劑的正子斷層掃描(PET)影像，可檢測於大腦中的tau蛋白，有助於阿茲海默症的預測與研究疾病的病理發展。量化使用18F-AV1451作為追蹤劑的tau影像，可作為評估大腦中tau蛋白的產生與聚集程度，並作為臨床上輔助診斷與疾病分期的應用。在治療上，特別是針對不可逆的神經退化進程，提供更早期的檢測，並且能在疾病早期的發展階段有效的給與特定的治療。
方法:此研究主要為量化使用18F-AV1451作為追蹤劑的tau蛋白影像。我們研究用影像資料共141組，其中包含127位病人的臨床失智症評估與診斷紀錄，皆來自阿茲海默症腦神經影像計畫(Alzheimer’s Disease Neuroimaging Initiative, ADNI)與美國國防部阿茲海默症腦神經影像計畫(ADNIDOD)的資料庫。我們目前已開發全自動化量化tau影像的技術於我們實驗室的軟體Chang Gung Image Texture Analysis toolbox (CGITA)進行分析。另外，我們也加入小波的分析方法計算出影像中紋理參數於CGITA的軟體中。我們的方法在自動化分割出欲分析的大腦區域後，可以計算出影像中大量的特徵參數，並透過接收者操作特徵曲線計算其曲線下面積，來評估病人的認知功能與影像中紋理特徵參數之間的關聯性。結果:在接收者操作特徵曲線下，鑑別MMSE的分數在23作為認知功能正常與受損的分類，有很好的表現。我們利用小波紋理特徵參數(wavelet-based entropy)，得到AUC 為0.851 (敏感度為71.43%，特異度為89.08%)。鑑別藉由醫師診斷為認知功能正常(NL)與阿茲海默症患者(AD)的組別。我們利用標準攝取值(SUV)的參數，得到AUC為0.82 (敏感度為81.8%，特異度為76.56%)。結論:我們發現利用小波紋理特徵參數可以鑑別認知功能受損的嚴重程度。透過我們全自動化量化影像的方法，將來也可以延伸應用於腫瘤與心血管影像的疾病，作為全自動化影像鑑別與分類之用途。

Alzheimer’s disease (AD) is the most common cause of dementia with no approved treatment or cure up to date. Development of drug strategies focuses on the abnormal production and clearance of the amyloid-β(Aβ) peptides and tau proteins. So far, all AD drugs underwent Phase III trial have failed to show significant efficacy. There are many potential reasons for such failure of clinical trials. One of the reasons, and perhaps the most important one, is the lack of identification for the pathological status of a test subject. There is an urgent need to find accurate methods of early detection and effective therapies for AD before the symptoms start. Recently, noninvasive detection of tau proteins using positron emission tomography (PET) with the advent of a tau tracer 18F-AV1451 is aimed to assist the diagnosis of AD as well as to track and predict the disease progression. Quantitative analysis of tau pathology in human brain with tau images can be a powerful method as a diagnostic aid for staging the disease. Therapies, especially those targeting irreversible neurodegenerative processes, may have a better chance of succeeding if applied early and specifically.
Methods: This study is focused on 18F-AV1451 PET images quantitative analysis. We acquired 141 tau image data from 127 patients with clinical diagnosis information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Department of Defense ADNI (ADNIDOD). We have developed a fully automatic quantitative method based on SPM8, and used Chang Gung Image Texture Analysis (CGITA) toolbox to evaluate the tau images with texture analysis. Furthermore, we have added additional wavelet-based methods into CGITA for generating the quantitative textural features. With our methods, a large amount of quantitative textural features can be extracted after the brain segmentation. We evaluated the relation between the cognitive functions and textural features by area under curve (AUC) of receiver operation characteristics (ROC) curves. Results: In the ROC analysis, we found that, when using the mini-mental state examination (MMSE) cutoff of 23 to discriminate cognitive normal (CN) from cognitive impairment (CI), the AUC is 0.851 for wavelet-based entropy, with 71.4% sensitivity and 89.1% specificity. The classification of normal (NL) and AD groups yields an AUC of 0.82 for standard uptake value (SUV) Entropy-Asphericity product, with 81.8% sensitivity and 76.56% specificity. Conclusion: The wavelet-based features may have a great potential to be used for early detection of CI patients. With our fully automatic quantification methods, we may further extend this application into oncology and cardiology for image-based discrimination and classification.

Abstract I
致謝 V
Contents VI
List of Figures VIII
List of Tables X
Chapter 1 Overview 1
Chapter 2 Literature Review 3
2.1 Causes and symptoms of Alzheimer’s disease 3
2.2 The pathophysiology and progression of Alzheimer’s disease 5
2.3 Unmet clinical need 7
2.4 Clinical features and diagnosis of Alzheimer’s disease 8
2.5 Current literature of Alzheimer’s disease with tau images 10
2.6 Quantitative analysis 12
2.6.1 Conventional quantification of PET images 12
2.6.2 Quantification of PET images with texture analysis 12
2.6.3 Textural features extraction with wavelet-based methods 14
2.7 Motivation and Specific aims 16
2.7.1 The role of tau imaging in Alzheimer’s disease 16
2.7.2 Specific aim 1: Development of automatic quantification methods for tau images analysis 16
2.7.3 Specific aim 2: Evaluation of developed methods for staging of cognitive impairment 17
Chapter 3 Materials and Methods 18
3.1 Quantification and feature extraction for tau images 18
3.1.1 Image acquisition 19
3.1.2 Input image models 21
3.1.3 Study population 22
3.1.4 Automatic segmentation method 25
3.1.5 Wavelet-based feature extraction 31
3.2 Quantitative analysis 36
3.2.1 Texture analysis with CGITA 36
3.2.2 Statistical analysis 38
Chapter 4 Results 39
4.1 Quantification of 18F-AV1451 PET images 39
4.1.1 Results of automatic brain segmentation 39
4.1.2 Results of wavelet-based texture analysis 42
4.2 Development of the software package 42
4.3 ROC analysis for Alzheimer’s disease 45
Chapter 5 Discussion 51
Chapter 6 Conclusion 53
References 54

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