臺灣博碩士論文加值系統

磁共振頻譜(MRS)是一種收取腦中代謝物資訊的技術，它是以非侵入式的方式透過收取氫原子核的訊號，利用不同的化學鍵結會影響氫原子核的旋進頻率，進一步利用這樣的資訊觀測腦中代謝物情形。通常是利用單點式頻譜(SVS)技術，藉由使用者在感興趣區域定義一長方體(稱其為VOI)，再掃描收取此長方體區域內的MRS資料。近年來，也發現有越來越多的研究是結合了MRS及fMRI兩項技術，來測量在大腦活化區域或者是大腦靜息態區域的代謝物資訊。因此，能在標準腦空間中定義VOI是相當重要的功能，可以藉此將VOI定義在fMRI的活化區域上或者是特定的大腦結構上，再將VOI轉換到受試者空間上供MRS掃描用。如此代謝物訊息可以與fMRI的結果連結，也可以減少不同操作者間造成的定位差異。
在這個研究中，我們開發了一套自動化單點式磁共振頻譜定位技術的圖形介面工具：ALLVOI。在MRS掃描前先在標準腦上定義好VOI參數值，然後再經由自動對位演算法計算出在受試者空間上VOI的參數值，包括大小、位置和旋轉。這套自動對位工具提供了一個客觀的定位方法，工具中程式及圖形使用者介面的設計，提供使用者能有一個簡單且方便的方式來定義VOI。

Magnetic resonance spectroscopy (MRS) data were usually acquired with single voxel spectroscopy techniques (SVS), which is used to access metabolites concentrations from a predefined volume of interest (VOI). Currently, MRS has been linked to fMRI studies to access metabolic information in activated brain regions or in resting brain areas. Therefore, it’s important to define the VOI on standard space (template) and transform the predefined VOI from standard space to subject space for each MRS scan. In this way, VOI can be determined from the template based on a cluster in fMRI results or a known brain structure instead of determining VOI subject-by-subject by operator’s experience. Metabolic information can be directly linked to fMRI results and operator bias on the localization of VOI can be minimized.
In this study, an automatic tool is developed for this purpose. We developed a toolbox: automatic localization of VOI (ALLVOI). It is to guide the definition of VOI on template before MRS scan then automatically calculate parameters of VOI such as voxel position, voxel size, orientation, and rotation in subject space. Moreover, the toolbox based on user-defined VOI on the MNI template is a more objective processing, and it is simple and convenient on operation so that users can easily perform the task of VOI definition.

Abstract i
摘 要 ii
Contents iii
Figures iv
Tables v
誌 謝 vi
Chapter 1. Introduction 1
1.1 Magnetic Resonance Spectroscopy 1
1.2 Single-Voxel Spectroscopy 2
1.3 Functional Magnetic Resonance Imaging 4
1.4 Statistical Parametric Mapping 4
1.5 Montreal Neurological Institute (MNI) space 5
1.6 Background and Motivation 6
Chapter 2. Methods and materials 9
2.1 Subjects and materials 10
2.2 Environment setting 11
2.3 User-defined VOI 11
2.3.1 GUI for user-defined VOI 12
2.3.2 Load images 14
2.3.3 Set parameters to fit in the brain structure 15
2.3.4 fMRI activation map 18
2.4 Automatic localization of user-defined VOI 19
2.4.1 Input data for automatic localization 19
2.4.2 T1WI normalization by SPM8 20
2.4.3 Transformation the User-defined VOI to system space 21
2.4.4 Output Data of Automatic Localization 26
Chapter 3. Results 28
Chapter 4. Discussion 36
Chapter 5. Conclusion and Future Work 38
Chapter 6. References 40

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