Premium
Spectral quantitation by principal component analysis using complex singular value decomposition
Author(s) -
Elliott Mark A.,
Walter Glenn A.,
Swift Alex,
Vandenborne Krista,
Schotland John C.,
Leigh John S.
Publication year - 1999
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/(sici)1522-2594(199903)41:3<450::aid-mrm4>3.0.co;2-9
Subject(s) - principal component analysis , singular value decomposition , spectral line , phaser , convergence (economics) , data set , singular spectrum analysis , phase (matter) , mathematics , algorithm , computer science , pattern recognition (psychology) , physics , artificial intelligence , statistics , optics , astronomy , economics , economic growth , quantum mechanics
Principal component analysis (PCA) is a powerful method for quantitative analysis of nuclear magnetic resonance spectral data sets. It has the advantage of being model independent, making it well suited for the analysis of spectra with complicated or unknown line shapes. Previous applications of PCA have required that all spectra in a data set be in phase or have implemented iterative methods to analyze spectra that are not perfectly phased. However, improper phasing or imperfect convergence of the iterative methods has resulted in systematic errors in the estimation of peak areas with PCA. Presented here is a modified method of PCA, which utilizes complex singular value decomposition (SVD) to analyze spectral data sets with any amount of variation in spectral phase. The new method is shown to be completely insensitive to spectral phase. In the presence of noise, PCA with complex SVD yields a lower variation in the estimation of peak area than conventional PCA by a factor of approximately ✓2. The performance of the method is demonstrated with simulated data and in vivo 31 P spectra from human skeletal muscle.Magn Reson Med 41:450–455, 1999. © 1999 Wiley‐Liss, Inc.