Time-domain quantitation of 1 H 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.

Quantification ofin vivoshort echo-time proton magnetic resonance spectra at 14.1 T using two different approaches of modelling the macromolecule spectrum

“…The metabolite basis set can be obtained either by measuring aqueous solutions of pure metabolites or by quantum-mechanical simulations using known spectral parameters [4]. Well-known timeand frequency-domain algorithms [5][6][7][8], based on metabolite basis sets, are currently used for an accurate quantification. The most frequently used algorithms for proton spectra quantification are QUEST [7] from jMRUI software working in the time domain and LCModel [5,6] working in the frequency domain.…”

Quantitative magnetic resonance spectroscopy: Semi‐parametric modeling and determination of uncertainties

“…[25] into Eq. [24] results in p͑ ͉y͒ ϰ e Ϫ͑1/2͒͑yϪF ͒ T U͑yϪF ͒ [26] and ˆa ccording to Eq. [8] then corresponds to ˆϭ arg max p͑ ͉y͒.…”

“…[3], Eq. [24] is considered after linearization of F around ˆ( determined for some chosen , ϭ *), [32] where 0 ϭ ˆϩ ͑J T UJ͒ Ϫ1 J T U͑y Ϫ Fˆ͒ [33] and ␣ ϭ ͑y Ϫ Fˆϩ Jˆ͒ T U͑y Ϫ Fˆϩ Jˆ͒ Ϫ 0 T J T UJ 0 . [34] Recall that in Eq.…”

“…Since this does not account for errors in the baseline approximation, such characterization of the accuracy of the MRS parameter estimates may be too optimistic. This has recently been accounted for in a novel approach (23,24) that extends the Cramér-Rao lower bounds by adding a correction term.…”

Temperature dependence of 1H NMR chemical shifts and its influence on estimated metabolite concentrations

“…However, since the analysis of short TE 1 H MR spectra of the brain is often hampered by severe signal overlap, the use of prior knowledge on the chemical shifts and the J-coupling constants for all relevant metabolites is of central importance. Thus, well established quantification programs such as LCModel [8], QUEST [9,10], or AQSES [11] use a model function for each metabolite to minimise the number of variables during the fitting procedure. These model functions are either measured on phantom solutions or simulated using published values of chemical shifts and J-coupling constants as prior knowledge [12,13].…”