Conventional magnetic resonance imaging (MRI) is a noninvasive and nondestructive technique and ideally suited for applications in clinical studies. In addition to the information of human anatomy provided by MRI, magnetic resonance spectroscopy (MRS) also provided a noninvasive method to investigate the metabolites in the body and is therefore regarded as a valuable method to examine tumors and disorders especially for the brain applications. To diagnose pyogenic brain abscess from other diseases is very important for clinic treatment. Cytosolic amino acids, lactate, alanine and acetate have been recognized as potential abscess markers, especially amino acids. LCModel is a well-known and reliable post-processing tool for MRS which can provide objectively quantitative of metabolite concentration. In this thesis, we would use LCModel to analyze the spectra of amino acids and further to identify and quantitate these metabolites. And we hope that the method would benefit more precisely noninvasive diagnosis and treatment of pyogenic brain abscess. However, due to the possibly poor SNR of in vivo proton MR spectroscopy, it might be difficult to identify these metabolites. In this study, we would validate the accuracy of LCModel in the analysis of amino acids. We used GAVA-simulated resonance spectra with different level noise as our input signals and analyzed by LCModel to understand the influence of concentrations and SNR caused by different level noise. Our goal is to find an optimally reliable method to help the clinic diagnosis of abscess patients.