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Parametric methods for frequency-selective MR spectroscopy
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2004 (English)Licentiate thesis, monograph (Other scientific)
Abstract [en]

Accurate and efficient quantitation of the data components in magnetic resonance spectroscopy (MRS) signals can be an important step in the analysis of biochemical substances. In several practical applications of MR spectroscopy the user is interested only in the data components lying in a small frequency band of the spectrum. A frequency-selective, or sub-band, analysis deals precisely with this kind of spectroscopy: estimation of only those spectroscopic components that lie in a selected frequency band of the (MR) data spectrum, with as little interference as possible from the out-of-band components and in a computationally efficient way. One obvious application for such a sub-band technique is to lower the computational burden in situations when the measured data sequence contains too many samples to be processed using a standard full-spectrum method. This thesis deals with several parametric methods to perform a frequency-selective data analysis. In addition, the possibility to incorporate prior knowledge about some of the components in the data is considered, a procedure that generally increases the parameter estimation performance significantly. A data model of exponentially damped sinusoids is assumed for the presented methods, which are applied to both simulated and experimental (in-vitro) MR data.

Place, publisher, year, edition, pages
Uppsala University, 2004.
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2004-001
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-86305OAI: oai:DiVA.org:uu-86305DiVA: diva2:117114
Supervisors
Available from: 2004-03-15 Created: 2005-05-24 Last updated: 2017-08-31Bibliographically approved

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Sandgren, Niclas

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