Abstract
Estimation of brain metabolite concentrations by MR spectroscopic imaging (MRSI) is complicated by partial volume contributions from different tissues. This study evaluates a method for increasing tissue specificity that incorporates prior knowledge of tissue distributions.
A spectral decomposition (sDec) technique was evaluated for separation of spectra from white matter (WM) and gray matter (GM), and for measurements in small brain regions using whole-brain MRSI. Simulation and in vivo studies compare results of metabolite quantifications obtained with the sDec technique to those obtained by spectral fitting of individual voxels using mean values and linear regression against tissue fractions and spectral fitting of regionally integrated spectra.
Simulation studies showed that, for GM and the putamen, the sDec method offers < 2% and 3.5% error, respectively, in metabolite estimates. These errors are considerably reduced in comparison to methods that do not account for partial volume effects or use regressions against tissue fractions. In an analysis of data from 197 studies, significant differences in mean metabolite values and changes with age were found. Spectral decomposition resulted in significantly better linewidth, signal-to-noise ratio, and spectral fitting quality as compared to individual spectral analysis. Moreover, significant partial volume effects were seen on correlations of neurometabolite estimates with age.
The sDec analysis approach is of considerable value in studies of pathologies that may preferentially affect WM or GM, as well as smaller brain regions significantly affected by partial volume effects. Magn Reson Med 79:2886-2895, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
