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Area-Selective Signal Parameter Estimation for Two-Dimensional MR Spectroscopy Data
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2006 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 183, no 1, 50-59 p.Article in journal (Refereed) Published
Abstract [en]

We consider the problem of parametric spectral analysis of two-dimensional (2D) magnetic resonance spectroscopy (MRS) data. Estimating the signal components from 2D MRS data is becoming common practice in many clinical MR applications. The most frequently used signal processing tool for this estimation problem is the non-parametric 2D-FFT. There are several alternative parametric methods available to perform this analysis, yet their computational complexity is generally rather high and it becomes prohibitive when the number of points in the measured data matrix is large. In this paper, we propose a novel signal parameter estimation technique which operates on a pre-specified sub-area of the 2D spectrum. This area-selective approach can be used either to estimate only the signal components of main interest in the data, or to compute signal parameter estimates of all present signal components as the computational burden for each sub-area is low. In the numerical example section we consider both simulated data and in vitro H-1 data acquired from a 1.5 T MR scanner.

Place, publisher, year, edition, pages
2006. Vol. 183, no 1, 50-59 p.
Keyword [en]
two-dimensional (2D) magnetic resonance spectroscopy, damped sinusoidal model, parametric area-selective analysis
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-19021DOI: 10.1016/j.jmr.2006.07.018ISI: 000242131100006OAI: oai:DiVA.org:uu-19021DiVA: diva2:46793
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2017-12-08Bibliographically approved

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Sandgren, NiclasStoica, Peter

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