Journal Highlight: Quantification of magnetic resonance spectroscopy signals with lineshape estimation

Skip to Navigation

Ezine

  • Published: May 24, 2011
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Quantification of magnetic resonance spectroscopy signals with lineshape estimation

Quantification of magnetic resonance spectroscopy signals with lineshape estimation
Journal of Chemometrics
Volume 25, Issue 4, pages 183-192, April 2011
M. I. Osorio-Garcia, D. M. Sima, F. U. Nielsen, U. Himmelreich and S. Van Huffel

Quantification of magnetic resonance spectroscopy (MRS) signals is required for providing metabolite concentrations of the tissue under investigation. However, inhomogeneities of the static magnetic field and tissue heterogeneities affect the lineshape of MRS signals and thus quantification. We propose an extension of the self-deconvolution method, by estimating and smoothing a common lineshape using a robust method with local regression. This common lineshape is imposed in the metabolite quantification method and the spectral parameters are obtained via nonlinear least squares.

This paper is free to view to spectroscopyNOW registered users until the end of July 2011. After this time it will be available via Wiley's Pay-Per-View service for US$35.

  • Click here to access the abstract of this paper. From here you can progress to read the full paper.
  • Click here for more details about Journal of Chemometrics 

 

Quantification of magnetic resonance spectroscopy (MRS) signals is required for providing metabolite concentrations of the tissue under investigation. However, inhomogeneities of the static magnetic field and tissue heterogeneities affect the lineshape of MRS signals and thus quantification. We propose an extension of the self-deconvolution method, by estimating and smoothing a common lineshape using a robust method with local regression. This common lineshape is imposed in the metabolite quantification method and the spectral parameters are obtained via nonlinear least squares.

Social Links

Share This Links

Bookmark and Share

Microsites

Suppliers Selection
Societies Selection

Banner Ad

Click here to see
all job opportunities

Copyright Information

Interested in separation science? Visit our sister site separationsNOW.com

Copyright © 2013 John Wiley & Sons, Inc. All Rights Reserved