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Automated spectral analysis II: Application of wavelet shrinkage for characterization of non‐parameterized signals
Author(s) -
Young Karl,
Soher Brian J.,
Maudsley Andrew A.
Publication year - 1998
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.1910400606
Subject(s) - a priori and a posteriori , parameterized complexity , wavelet , parametric statistics , characterization (materials science) , computer science , pattern recognition (psychology) , noise reduction , algorithm , signal (programming language) , convergence (economics) , artificial intelligence , shrinkage , parametric model , biological system , mathematics , physics , statistics , machine learning , optics , philosophy , epistemology , economics , biology , programming language , economic growth
An iterative method for differentiating between known resonances and uncharacterized baseline contributions in MR spectra is described. The method alternates parametric modeling, using a priori knowledge of spectral parameters, with non‐parametric characterization of remaining signal components, using wavelet shrinkage and denoising. Rapid convergence of the iterative method is demonstrated, and examples are shown for analysis of simulated data and an in vivo 1 H spectrum from the brain. Results show good separation between metabolite signals and strong baseline contributions.