Factors affecting the quantification of short echoin-vivo1H MR spectra: prior knowledge, peak elimination, and filtering

Abstract

Short echo 1H in‐vivo brain MR spectra are difficult to quantify for several reasons: low signal to noise ratio, the severe overlap of spectral lines, the presence of macromolecule resonances beneath the resonances of interest, and the effect of resonances adjacent to the spectral region of interest (SRI). This paper outlines several different quantification strategies and the effect of each on the precision of in‐vivo metabolite measurements. In‐vivo spectra were quantified with no operator interaction using a template of prior knowledge determined by mathematically modeling separate in‐vitro metabolite spectra. Metabolite level estimates and associated precision were compared before and after the inclusion of macromolecule resonances as part of the prior knowledge, and following two different methods of handling resonances adjacent to the SRI. The effects of rectangular and exponential filters were also investigated. All methods were tested using repeated in‐vivo spectra from one individual acquired at 1.5 T using stimulated echo acquisition mode (STEAM, TE = 20 ms) localization. The results showed that the inclusion of macromolecules in the prior knowledge was necessary to obtain metabolite levels consistent with the literature, while the fitting of resonances adjacent to the SRI concurrent with modeled metabolites optimized the precision of metabolite estimates. Metabolite levels and precision were also affected by rectangular and exponential filtering, suggesting caution must be taken when such filters are used. Copyright © 1999 John Wiley & Sons, Ltd.

MR spectroscopy quantitation: a review of time‐domain methods

“…In Martin 15 a decreased parameter accuracy due to linebroadening was reported based on results on simulated data. In Bartha et al 5 the same conclusion was reached based on the analysis of in vivo short-echo proton spectra.…”

“…Most methods use some kind of model for the baseline. In Hofmann et al 66 the baseline is modeled by a sum of Voigt lines and in Bartha et al 5 by a set of Gaussian peaks. In the frequency domain, the baseline has also been approximated by a spline function 6 and by wavelet coefficients.…”

“…The total amplitudes of P a , P b and P g relate as 1:1:1. In summary: a 1 = a 2 = a 3 = a 4 = 2a 5 = a 6 = 2a 7 . * The phases of all peaks are equal: f 1 = f 2 = … = f 7 .…”

“…The prior knowledge can also be derived from phantom spectra of pure metabolite solutions measured under the same experimental conditions as in vivo spectra. 4,5 Alternatively, the complete phantom metabolite spectra can be used as a model, circumventing the need for a mathematical model function. 6 In order to avoid the need to acquire phantom spectra for each field strength and pulse sequence used, spectral simulation has been used to form a priori information.…”

Common processing of in vivo MR spectra

“…47 Parameter data derived from in vitro metabolite measurements have been applied at an increasing level of sophistication together with knowledge of the contribution of broad macromolecular components to the spectra. 48 In parallel VARPRO-type time-domain fitting procedures including parametric prior knowledge has been applied to 1 H MR spectra of the brain, first including some rudimentary information e.g. 49,50 and later at a more robust and advanced level.…”