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Spectral analysis and magnetic resonance 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.
2001 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This dissertation is concerned with nonparametric approaches for spectral analysis (SA) and algorithms for magnetic resonance spectroscopy (MRS) data analysis.

A method to obtain the optimal smoothing window for the class of SA methods based on local smoothing of the periodogram is proposed. Under a local smoothness assumption the Cramér-Rao lower bound on the estimation accuracy for nonparametric SA methods is derived. Furthermore, the maximum likelihood (ML) approach is considered and the relation between the ML approach and other SA methods is given.

A novel nonparametric method for MRS data analysis is proposed. The method uses data dependent filterbanks to separate the MRS signal as a function of both frequency and damping. The two-dimensional interpretation leads to high resolution and estimation accuracy comparable with the best parametric approaches.

A computationally convenient implementation of the ML estimator for MRS data incorporating maximum available a priori knowledge about model parameters to reduce the dimensionality of the problem is derived.

Techniques that handle imperfections in the MRS data axe studied. A method based on maximum-phase finite impulse response filters for water peak suppression and frequency selective quantification of MRS data is presented. Finally a robust estimation algorithm using an unsuppressed water reference to model experimental imperfections is proposed. The algorithm is evaluated on MRS data acquired on a clinical magnetic resonance imaging scanner.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2001. , vii, 180 p.
Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-2516 ; 29
Keyword [en]
Nonparametric spectral analysis, magnetic resonance spectroscopy, parameter estimation, maximum likelihood estimation, nonlinear least squares
National Category
Signal Processing
Research subject
Signal Processing
URN: urn:nbn:se:uu:diva-621ISBN: 91-554-4971-9OAI: oai:DiVA.org:uu-621DiVA: diva2:167461
Public defence
2001-04-20, Building 2, room 247, Center of Mathematics and Information Technology (MIC), Uppsala, 10:15 (English)
Available from: 2001-03-30 Created: 2001-03-30 Last updated: 2011-02-16Bibliographically approved

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