Document Detail


Area-selective signal parameter estimation for two-dimensional MR spectroscopy data.
MedLine Citation:
PMID:  16904355     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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 1H data acquired from a 1.5 T MR scanner.
Authors:
Niclas Sandgren; Petre Stoica; Frederick J Frigo
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2006-08-10
Journal Detail:
Title:  Journal of magnetic resonance (San Diego, Calif. : 1997)     Volume:  183     ISSN:  1090-7807     ISO Abbreviation:  J. Magn. Reson.     Publication Date:  2006 Nov 
Date Detail:
Created Date:  2006-11-10     Completed Date:  2007-02-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9707935     Medline TA:  J Magn Reson     Country:  United States    
Other Details:
Languages:  eng     Pagination:  50-9     Citation Subset:  IM    
Affiliation:
Systems and Control Division, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05 Uppsala, Sweden. niclas.sandgren@it.uu.se
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Complex Mixtures / chemistry*
Computer Simulation
Magnetic Resonance Spectroscopy / methods*
Models, Chemical*
Models, Molecular*
Reproducibility of Results
Sensitivity and Specificity
Chemical
Reg. No./Substance:
0/Complex Mixtures

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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