2D approach to quantitation of inversion-recovery data

Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

“…For clinical 1D MR spectra, modeling in the form of linear combinations of model spectra has become the gold standard and several software packages have been described and are available on a commercial or non-commercial basis [1][2][3] (recently reviewed in [4]). 2D modeling methods have only been used in isolated methodological studies [5][6][7][8][9][10][11][12] and the required tools are not broadly available. Furthermore, previous work was mostly focused on a single type of 2D experiment.…”

On the use of Cramér–Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

“…Given that metabolite signals in a proton MR spectrum usually have considerable overlap that makes the quantification difficult, generally, one of three different approaches is taken: 1) use of a single non-specific one-dimensional spectrum (e.g. a localized short echo time (TE) spectrum) followed by linear combination model fitting based on prior knowledge about the constituent metabolites and spectral parameters (Provencher, 1993;Ratiney et al, 2005;Slotboom et al, 1998;Wilson et al, 2011), or 2) use of a dedicated (socalled editing) one-dimensional experiment optimized for exclusive or selective sensitivity for a single metabolite of interest, usually followed by simple model peak fitting or signal integration (Allen et al, 1997), or 3) use of a standard localized two-dimensional MR spectrum followed by peak integration (Thomas et al, 1996;Thomas et al, 2001) or prior knowledge fitting Gonenc et al, 2010;Kiefer et al, 1998;Kreis et al, 2005;; Thomas et al, 2008;van Ormondt et al, 1990;Vanhamme et al, 1999). In cases 1 and 3, the choice of experimental parameters like TE and repetition time (TR) is most often based on general considerations about maximum signal for given relaxation times, insensitivity to changes in relaxation times or arguments about minimization of macromolecular baseline contributions, while in case 2 the signal yield of wanted and unwanted metabolites and their relative overlap is modeled based on quantum mechanical simulations or solution measurements.…”

Integrated data acquisition and processing to determine metabolite contents, relaxation times, and macromolecule baseline in single examinations of individual subjects

“…The general approach to quantitative 1 H magnetic resonance spectroscopy ( 1 H-MRS) in clinical research (1) is to sequentially acquire all information needed for absolute quantitative information from localized spectra: (1) the spectrum to be fitted and quantitated, (2) a series of measurements for the determination of longitudinal relaxation times (multiple repetition times, saturation recovery, or inversion recovery), (3) a further series of spectra for the evaluation of transverse relaxation times (obtained with differing echo times), (4) further measurements for the experimental determination of the macromolecular baseline (MM-BL) signals (metabolite nulling (2) or saturation recovery (3)), and (5) reference measurements for absolute quantitation. Because the experimental time needed to record all this information is generally too long for a clinical examination in a single subject, true absolute quantitation is hardly ever performed in research, let alone the clinic.…”