In the last two decades, magnetic resonance spectroscopy (MRS) has had an increasing success in biomedical research. This technique has the faculty of discerning several metabolites in human tissue non-invasively and thus offers a multitude of medical applications. In clinical routine, quantification plays a key role in the evaluation of the different chemical elements. The quantification of metabolites characterizing specific pathologies helps physicians establish the patient's diagnosis. Estimating quantities of metabolites remains a major challenge in MRS. This thesis presents the implementation of a promising quantification algorithm called selective-frequency singular value decomposition (SELF-SVD). Numerous tests on simulated MRS data have been carried out to bring an insight on the complex dependencies between the various components of the data. Based on the test results, suggestions have been made on how best to set the SELF-SVD parameters depending on the nature of the data. The algorithm has also been tested for the first time with in-vivo 1H MRS data, in which SELF-SVD quantification results allow the localization of a brain tumor.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ntnu-9825 |
| Date | January 2009 |
| Creators | Massé, Kunal |
| Publisher | Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, Institutt for elektronikk og telekommunikasjon |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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