title: Time-domain quantitation of 1H short echo-time signals: background accommodation.
abstract: Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. While the model function of the metabolite signal is known, that of the macromolecules is only partially known. We present time-domain semi-parametric estimation approaches based on the QUEST quantitation algorithm (QUantitation based on QUantum ESTimation) and encompassing Cramér-Rao bounds that handle the influence of 'nuisance' parameters related to the background. Three novel methods for background accommodation are presented. They are based on the fast decay of the background signal in the time domain. After automatic estimation, the background signal can be automatically (1) subtracted from the raw data, (2) included in the basis set as multiple components, or (3) included in the basis set as a single entity. The performances of these methods combined with QUEST are evaluated through extensive Monte Carlo studies. They are compared in terms of bias-variance trade-off. Because error bars on the amplitudes are of paramount importance for diagnostic reliability, Cramér-Rao bounds accounting for the uncertainty caused by the background are proposed. Quantitation with QUEST of in vivo short echo-time (1)H human brain with estimation of the background is demonstrated.
authors.affiliation: Laboratoire de RMN, CNRS, UMR 5012, Université LYON I-CPE, 3 Rue Victor Grignard, 69 616, Villeurbanne, France.
authors.forename: H
authors.initials: H
authors.lastname: Ratiney
authors.forename: Y
authors.initials: Y
authors.lastname: Coenradie
authors.forename: S
authors.initials: S
authors.lastname: Cavassila
authors.forename: D
authors.initials: D
authors.lastname: van Ormondt
authors.forename: D
authors.initials: D
authors.lastname: Graveron-Demilly
mesh_terms: D001921:Brain / Q000473:pathology; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D050356:Lipid Metabolism; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D016011:Normal Distribution; D012984:Software; D013997:Time Factors