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Titolo:
Water modeled signal removal and data quantification in localized MR spectroscopy using a time-scale postacquistion method
Autore:
Serrai, H; Senhadji, L; Clayton, DB; Zuo, C; Lenkinski, RE;
Indirizzi:
Harvard Univ, Sch Med, Beth Israel Deaconess Med Ctr, Boston, MA 02215 USAHarvard Univ Boston MA USA 02215 Deaconess Med Ctr, Boston, MA 02215 USA Univ Rennes 1, LTSI, INSERM, EMI 9934, F-35042 Rennes, France Univ Rennes 1 Rennes France F-35042 RM, EMI 9934, F-35042 Rennes, France Univ Penn, Sch Med, Dept Radiol, Philadelphia, PA 19104 USA Univ Penn Philadelphia PA USA 19104 pt Radiol, Philadelphia, PA 19104 USA
Titolo Testata:
JOURNAL OF MAGNETIC RESONANCE
fascicolo: 1, volume: 149, anno: 2001,
pagine: 45 - 51
SICI:
1090-7807(200103)149:1<45:WMSRAD>2.0.ZU;2-O
Fonte:
ISI
Lingua:
ENG
Soggetto:
CONTINUOUS WAVELET TRANSFORM; MAGNETIC-RESONANCE SPECTROSCOPY; DOMAIN DATA; SOLVENT SUPPRESSION; BRAIN-METABOLITES; NMR-SPECTROSCOPY; DYNAMIC-RANGE; SPECTRA; DECOMPOSITION; RELAXATION;
Keywords:
localized magnetic resonance spectroscopy; continuous wavelet transform; water suppression; quantification;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Physical, Chemical & Earth Sciences
Citazioni:
30
Recensione:
Indirizzi per estratti:
Indirizzo: Serrai, H Harvard Univ, Sch Med, Beth Israel Deaconess Med Ctr, Boston, MA02215 USA Harvard Univ Boston MA USA 02215 Med Ctr, Boston, MA 02215 USA
Citazione:
H. Serrai et al., "Water modeled signal removal and data quantification in localized MR spectroscopy using a time-scale postacquistion method", J MAGN RES, 149(1), 2001, pp. 45-51

Abstract

We have previously shown the continuous wavelet transform (CWT), a signal-processing tool, which is based upon an iterative algorithm using a lorentzian signal model, to be useful as a postacquisition water suppression technique. To further exploit this tool we show its usefulness in accurately quantifying the signal metabolites after water removal. However, due to the static held inhomogeneities, eddy currents, and "radiation damping," the water signal and the metabolites may no longer have a lorentzian lineshape. Therefore, another signal model must be used. As the CWT is a flexible method,we have developed a new algorithm using a gaussian model and found that itfits the signal components, especially the water resonance, better than the lorentzian model in most cases. A new framework, which uses the two models, is proposed. The framework iteratively extracts each resonance, startingby the water peak; from the raw signal and adjusts its envelope to both the lorentzian and the gaussian models. The model giving the best fit is selected. As a consequence, the small signals originating from metabolites whenselecting, removing, and quantifying the dominant water resonance from theraw time domain signal are preserved and an accurate estimation of their concentrations is obtained. This is demonstrated by analyzing (H-1) magneticresonance spectroscopy unsuppressed water data collected from a phantom with known concentrations at two different field strengths and data collectedfrom normal volunteers using two different localization methods. (C) 2001 Academic Press.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 22/01/20 alle ore 21:40:48