雖然由老化引發的腦白質病變是老年人認知障礙的一個重要誘因，其機理缺並不為人所知。最新的小樣本研究表明擴散核磁造影在很大程度上是對腦白質病變最為敏感的的成像檢測手段。加深對擴散核磁造影所給出的各種指數的理解和認知對於檢測腦白質病變的病理發展以及研發試驗療法的替代標記有重要的意義。 / 為了獲得更具有臨床價值的擴散核磁造影指數，我們首先需要重構腦白質纖維束並沿著重構出的腦白質纖維束採集數值。然而，傳統的腦白質纖維束重構技術對於腦白質病變十分敏感。此外，不同病人所重構出的腦白質纖維束間缺乏映射關係也使我們無法有效進行大樣本統計分析。 / 在這個課題裡，我們提出了一個可以解決以上問題的一個全新框架。我們將專家標註過功能區的全腦白質纖維束模板配準到各個個體的腦部。此方案可自動生成個體化的全腦白質纖維束以及纖維束的功能區標註。自由形變模型被用於在全局層面對配準進行約束。所重構纖維束的曲率被用於在局部對配準進行約束。為了減輕腦白質病變對配準的影響，我們運用了一種 魯棒的主成分分析手段來檢測被病灶所干擾的纖維束。為了指導這些被干擾纖維束的配準，我們提出了一種全新的沿纖維束的區域特徵作為替代。此外，我們也探究了通過在纖維束上建立坐標系來除去離群纖維已經提供更高相關性的辦法。 / 我們所提出的框架被運用於一個腦小血管病變的臨床研究。在64個研究對象中約半數是腦白質病變患者。試驗結果證實此算法成功地將全腦白質纖維束模板配準到了所有研究對象上。沿著特定纖維束改採集的指數與認知測試分數的相關性顯著地超越了傳統指數所給出的結果。我們同時也發現沿著不同功能區腦白質纖維改採集的指數與相應的認知測試分數統計相關。 / Although age-related white matter lesion(WML)is an important substrate for cognitive impairment in the elderly, the mechanisms whereby WML induces cognitive impairment are uncertain. Recent findings based on small studies suggested that diffusion tensor imaging (DTI) measures might be the most sensitive imaging predictors in patients with WML. Understanding the imaging predictors for such disease will be useful in monitoring disease progression and in devising surrogate marker for treatment trials. / In order to obtain DTI measurements with diagnostic significance, it is first necessary to reconstruct the white-matter fiber pathways inside the brain along which certain DTI-derived values are calculated. Nevertheless, the traditional approach of white-matter tract reconstruction, or tractography, is severely hindered by the abundant existence of lesions inside the brains of WML patients. The lack of correspondence between fiber bundles across patients also makes obtaining group statistics of individual fiber bundles dicult. / In this study, we propose a novel framework that can mitigate the aforementioned issues of traditional tractography approaches. An expert-labeled whole brain tractography template is registered onto individual patients. Fiber trajectories and anatomically meaningful fiber bundles are automatically obtained by this registration. The free-form deformations are used to regularize the transformations at the whole brain level and across fiber bundles. Fiber curvatures are penalized as the intra-fiber regularization to encourage the smoothness of transformed fibers. White matter (WM) lesion is one of the major factors affecting tractography and registration of subjects with neuro Logical disorders. The Robust Principal Component Analysis(RPCA) is used to automatically detect fiber tract segments that are affected by WM lesion and a novel along-fiber regional prior is learned from healthy subjects to facilitate the registration of these fiber tract segments. We also propose to establish bundle-wise coordinate system by utilizing low-rank constraints upon the DTI measurements. The eort elevates the summary for an anatomical bundle from a scalar statistic to a vector containing changes along the representative fiber pathway. It provides means to exclude the outlier fibers while retaining partially-complete fibers. / The proposed scheme is applied to a clinical study of cerebral small vessel diseases(SVD).Experimental results show successful registration of the whole brain tractography template onto all 64 subjects, including both healthy con¬trol subjects and SVD patients. The DTI measures measured along specific registered anatomical fiber bundles exhibit significant boost in correlation with cognitive functions as compared with traditional measures. It also shows that different anatomical WM structures correlate with multiple types of cognitive functions in different ways. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / He, Xiaotian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 46-53). / Abstracts also in Chinese. / List of Figures --- p.ix / List of Tables --- p.xii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Our Work and Contributions --- p.2 / Chapter 1.3 --- Related Work --- p.4 / Chapter 1.4 --- Thesis Organization --- p.5 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Background of Neuroanatomy --- p.6 / Chapter 2.2 --- Background on Diffusion Tensor Magnetic Resonance Imaging (DTMRI) --- p.11 / Chapter 3 --- Free Form Fibers --- p.18 / Chapter 3.1 --- DTI Acquisition --- p.20 / Chapter 3.2 --- Fiber Model --- p.20 / Chapter 3.3 --- Fiber-to-DTI Registration --- p.21 / Chapter 3.3.1 --- Free-Form Fibers (FFFs) --- p.21 / Chapter 3.3.2 --- Tensor-Driven Fiber-to-DTI Registration --- p.23 / Chapter 3.3.3 --- Reliability Assessment by Robust Principal Component Analysis --- p.24 / Chapter 3.3.4 --- Contextual Feature --- p.26 / Chapter 3.3.5 --- Learning the Fiber Context Prior --- p.29 / Chapter 3.3.6 --- Registration Refinement Using the Fiber Context Prior --- p.29 / Chapter 4 --- Results --- p.31 / Chapter 5 --- Future Work --- p.39 / Chapter 5.1 --- Refinement on Large Bundles --- p.39 / Chapter 5.2 --- Outlier Fiber Removal in Fiber Template --- p.40 / Chapter 6 --- Conclusion --- p.44 / Bibliography --- p.46