Automated spectral analysis II: application of wavelet shrinkage for characterization of non-parameterized signals.
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 1H spectrum from the brain. Results show good separation between metabolite signals and strong baseline contributions.
Young
K
K
Department of Radiology, University of California San Francisco, DVA Medical Center, 94121, USA.
Soher
B J
BJ
Maudsley
A A
AA
D000465:Algorithms; D001224:Aspartic Acid / Q000031:analogs & derivatives / Q000378:metabolism; D001921:Brain / Q000378:metabolism; D002794:Choline / Q000378:metabolism; D003198:Computer Simulation; D003401:Creatine / Q000378:metabolism; D005583:Fourier Analysis; D006801:Humans; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D015233:Models, Statistical; D018709:Statistics, Nonparametric
D001224:Aspartic Acid; C000179:N-acetylaspartate; D003401:Creatine; D002794:Choline