由於磁振造影 (magnetic resonance imaging, MRI) 與磁振頻譜 (magnetic resonance spectroscopy, MRS) 具有非侵入性探測的特色，所以被廣泛地發展以應用在臨床分析的研究上。近年來隨著MRS應用於臨床的例子增加，所以也發展出許多針對後處理的工具軟體，其中最廣為人知的後處理軟體為LCModel。

LCModel根據basis set對病人的頻譜做定量分析以估計代謝物的絕對濃度。所以basis set對所估計出來的絕對濃度的準確性佔了極大的因素。而LCModel預設的basis sets是由phantom實驗所產生的，但是會有某些特定的代謝物不易取得的問題。為了避免這種情況，LCModel另外提供一個方法稱為:頻譜給予。

在本篇論文中，我們使用GAMMA Visual Analysis (GAVA) 軟體採用數學模擬的方式製作basis sets。而LCModel於phantom實驗製造出來的basis sets中所缺乏的代謝物我們也會一併產生，並比較LCModel預設basis sets與GAVA模擬的basis sets的差異。除此之外，我們也會分別使用這二組basis sets去對正常受試者做絕對濃度的定量比較。另外，根據文獻顯示，acetate (Act)、succinate (Suc) 和amino acids (AA) 為腦膿瘍 (abscess) 診斷上的重要指標，因此我們也比較使用LCModel分別採用二種basis sets對病患定量後的結果。

使用LCModel「頻譜給予」的方法來產生LCModel於phantom實驗時所缺乏的代謝物，只適用於單峰共振的頻譜 (singlet resonance) 且不需考慮耦合常數 (coupling constant) 的代謝物。我們的結果証明使用GAVA模擬出來的basis sets定量後的結果會不同於使用LCModel預設basis sets定量後的結果。我們相信使用GAVA模擬出來的basis sets，對於每個代謝物彼此之間有較好的一致性，因此對於在MRS的定量上會比較準確。

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) has been developed and applied to clinical analysis studies due to its non-invasive properties. Because of the increasing clinical interests of applying MRS, a lot of post-processing tools has been developed, among which LCModel is one of the most popular.

LCModel estimates the absolute metabolite concentrations in vivo according to the basis file, so basis files play an important role for the accuracy of absolute metabolite concentrations. The default basis sets of LCModel were made by phantom experiments. However, some special metabolites are difficult to get them, so the basis sets lack for these metabolites. In order to avoid this trouble, LCModel provides a special method called “spectra offering”.

In this study, we use GAMMA Visual Analysis (GAVA) software to create basis sets and compare the shape of LCModel default basis sets with the shape of GAVA basis sets. Some metabolites which are not included in the LCModel phantom experiments are also generated. Finally, we estimate the absolute concentrations in normal subjects and patients by using these two kinds of basis sets respectively.

Using LCModel “spectra offering” method to append extra metabolites for LCModel basis sets is applicable to those metabolites of singlet resonance but not of J-coupling resonance in the meanwhile. Our results demonstrate that using GAVA simulation as the basis set leads to different quantitative results from using basis sets of in vitro. We believe that using GAVA simulation as the basis set would provide better consistency among all metabolites and thus achieve more accurate quantification of MRS.

致謝 i
摘要 ii
Abstract iv
目錄 vi
圖片目錄 vii
表格目錄 ix
第一章 序論 1
1.1 背景 1
1.2 動機 3
1.3 大綱 4
第二章 理論 5
2.1 MRS的基本原理 5
2.2 人類大腦中的代謝物 6
2.3 MRS的定位技術 9
2.4 後處理技術 10
2.4.1 Zero filling 10
2.4.2 Apodization 10
2.4.3 相位角校正 11
第三章 方法 16
3.1 LCModel 16
3.2 GAVA 17
3.3 製作basis sets的方法 17
3.3.1 Phantom實驗與頻譜給予 17
3.3.2 GAVA模擬 19
3.4 產生basis sets 19
3.5 實驗設計 20
第四章 結果 30
4.1 LCModel basis sets與GAVA basis sets的比較 30
4.2 正常受試者與病患之絕對代謝物濃度估算 31
第五章 討論與結論 52
5.1 討論 52
5.2 結論 55
參考文獻 61

1. Pfeifer, H., A short history of nuclear magnetic resonance spectroscopy and of its early years in Germany. Magnetic Resonance in Chemistry, 1999. 37: p. S154-S159.
2. Kramer, D.M., Basic principles of magnetic resonance imaging. Radiol Clin North Am, 1984. 22(4): p. 765-78.
3. Salibi, N., Clinical MR Spectroscopy: First Principles. 1998: John Wiley & Sons.
4. Skoch, A., F. Jiru, and J. Bunke, Spectroscopic imaging: basic principles. Eur J Radiol, 2008. 67(2): p. 230-9.
5. Provencher, S.W., Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed, 2001. 14(4): p. 260-4.
6. Provencher, S.W., ed. LCModel & LCMgui User's Manual. 2005.
7. Soher, B.J., ed. GAVA-Users Manual and Reference. 2008.
8. Soher, B.J., et al., GAVA: spectral simulation for in vivo MRS applications. J Magn Reson, 2007. 185(2): p. 291-9.
9. Maudsley, A.A., et al., Numerical simulation of PRESS localized MR spectroscopy. J Magn Reson, 2005. 173(1): p. 54-63.
10. Jensen, J.E., et al., Quantification of J-resolved proton spectra in two-dimensions with LCModel using GAMMA-simulated basis sets at 4 Tesla. NMR Biomed, 2009. 22(7): p. 762-9.
11. Lai, P.H., Pyogenic Brain Abscess Findings from in vivo 1.5T and 11.7T in vitro Proton MR spectroscopy. AJNR Am J Neuroradiol, 2005. 26: p. 279-288.
12. de Graaf, R.A., In Vivo NMR Spectroscopy: Principles and Techniques 2ed. 2007: John Wiley & Sons.
13. Govindaraju, V., K. Young, and A.A. Maudsley, Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed, 2000. 13(3): p. 129-53.
14. Klose, U., Measurement sequences for single voxel proton MR spectroscopy. Eur J Radiol, 2008. 67(2): p. 194-201.
15. in 't Zandt, H., M. van Der Graaf, and A. Heerschap, Common processing of in vivo MR spectra. NMR Biomed, 2001. 14(4): p. 224-32.
16. Freeman, R., Handbook of Nuclear Magnetic Resonance. 1998: Longman:Harlow.
17. Kreis, R., Issues of spectral quality in clinical 1H-magnetic resonance spectroscopy and a gallery of artifacts. NMR Biomed, 2004. 17(6): p. 361-81.
18. Tikhonov, V.P. and N.A. Kostromina, PMR study of conformational changes in leucine, isoleucine, and valine. Teoreticheskaya i Eksperimental'naya Khimiya,, 1977. 13(4): p. 496-503.