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Representation of strong baseline contributions in 1 H MR spectra
Author(s) -
Soher Brian J.,
Young Karl,
Maudsley Andrew A.
Publication year - 2001
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1129
Subject(s) - wavelet , parametric statistics , algorithm , spectral line , filter (signal processing) , computer science , wavelet transform , invariant (physics) , mathematics , pattern recognition (psychology) , nuclear magnetic resonance , artificial intelligence , physics , statistics , computer vision , astronomy , mathematical physics
A comparison is made between two optimization procedures and two data models for automated analysis of in vivo proton MR spectra of brain, typical of that obtained using MR spectroscopic imaging at 1.5 Tesla. First, a shift invariant wavelet filter is presented that provides improved performance over a conventional wavelet filter method for characterizing smoothly varying baseline signals. Next, two spectral fitting methods are described: an iterative spectral analysis method that alternates between optimizing a parametric description of metabolite signals and nonparametric characterization of baseline contributions, and a single‐pass method that optimizes a complete spectral and baseline model. Both methods are evaluated using wavelet and spline models of the baseline function. Results are shown for Monte Carlo simulations of data representative of both long and short TE, in vivo 1 H acquisitions. Magn Reson Med 45:966–972, 2001. © 2001 Wiley‐Liss, Inc.