Automated spectral analysis II: Application of wavelet shrinkage for characterization of non-parameterized signals

Karl Young, Brian J. Soher, Andrew A Maudsley

Research output: Contribution to journalArticle

84 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)816-821
Number of pages6
JournalMagnetic Resonance in Medicine
Volume40
Issue number6
StatePublished - Dec 1 1998
Externally publishedYes

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Keywords

  • NMR
  • Parametric spectral analysis
  • Wavelet transform

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Automated spectral analysis II : Application of wavelet shrinkage for characterization of non-parameterized signals. / Young, Karl; Soher, Brian J.; Maudsley, Andrew A.

In: Magnetic Resonance in Medicine, Vol. 40, No. 6, 01.12.1998, p. 816-821.

Research output: Contribution to journalArticle

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