Application of time‐domain fitting in the quantification of in vivo¹H spectroscopic imaging data sets

Abstract

Time-domain model function fitting techniques were applied to improve the reconstruction of metabolite maps from the data sets obtained from in vivo 1H spectroscopic imaging (SI) experiments. First, residual water-related signals were removed from the SI data sets by using SVD-based linear time-domain fitting based upon the HSVD (State Space) approach. Second, peak integrals of the metabolites of interest were obtained by quantifying the proton spin-echoes of the voxels by means of non-linear time-domain fitting based upon the maximum likelihood principle. Third, in order to save computational time, interpolation of the metabolite images (from size 32 x 32 to 128 x 128) was performed in the image-domain by applying one-dimensional cubic splines. It was found that the residual water signals can be almost completely removed from the SI data sets by applying the linear HSVD fitting method. Furthermore, it was found that voxel dependency of certain NMR parameters (e.g., variations of the spin-echo offset frequencies and/or phase factors) can be accounted for automatically by applying the nonlinear time-domain fitting technique. For that purpose it appeared to be essential to employ prior knowledge of the NMR spectral parameters.

Effects of Identical Weight Loss on Body Composition and Features of Insulin Resistance in Obese Women With High and Low Liver Fat Content

“…The methylene signal, which represents intracellular trigyceride, was measured at 1.4 ppm. The spectra were fitted in the time domain using the variable projection method (32,33). Spectroscopic intracellular triglyceride content (in percent) was expressed as a ratio of the area under the methylene peak to that under the sum of methylene and water peaks ϫ 100 (LFAT).…”

Toward an in Vivo Neurochemical Profile: Quantification of 18 Metabolites in Short-Echo-Time 1H NMR Spectra of the Rat Brain

“…Detection of resolved glucose resonances may be critical in achieving the needed accuracy for glucose measurements by 1 H NMR. Extensions to other deconvolution methods are possible, although it remains to be demonstrated that other quantification methods, e.g., time domain fitting with prior knowledge (21,62), principal component (22) or wavelet analysis (23), are able to handle such large sets of model spectra, containing multiple resonances, and that they are equally robust. It also remains to be shown whether the accuracy and efficiency of quantification of metabolites with low concentrations can be improved using alternate methods, which focus on the detection of selected metabolites, e.g., spectral editing of GABA (46) or using 2D fitting of multiple echo times (21).…”

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