To differentiate pyogenic brain abscess from other brain diseases such as necrotic glioblastomas is very important for clinic treatment. Cytosolic animo acids, lactate, alanine, succinate and acetate have been recognized as potential abscess markers. LCModel is a well-known tool to analyze the MRS data, as it provides opportunity of quantitative of metabolite concentration. Using MRS with LCModel to identify and quantitate these metabolites would benefit more precisely noninvasive diagnosis and treatment of pyogenic brain abscess.
However, to differentiate the MR spectra of strongly overlapping metabolites are not easy. In this study, we validate the accuracy of LCModel on detecting these overlapping metabolites. We use some GAVA-simulated resonance spectra as our input signals and figure out the performance of LCModel analysis in different conditions. Our goal is to find an optimal analysis method to help the clinic diagnosis of abscess patients. Our result shows that the determination of basis sets is very important since the analyzed result might be different due to the improper selection of basis sets.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0723110-175516 |
| Date | 23 July 2010 |
| Creators | Chang, Lung-Sheng |
| Contributors | Ping-Hong Lai, Cheng-Wen Ko, Tzu-Chao Chuang, Hsiao-Wen Chung |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723110-175516 |
| Rights | campus_withheld, Copyright information available at source archive |
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