In Vivo Brain Metabolite Quantification

The ¹H-MRS data was processed offline using in-house software. The data was voxel-shifted to align the NAA grid with the VOI, then Fourier transformed in the time, AP and LR direction and Hadamard-transformed along the IS dimension. Each spectrum was frequency-aligned and zero-order phased in reference to the NAA peak. Relative levels of the i-th (i=NAA, Cr, Cho, mI) metabolite in the j-th (j-1…480) voxel in the k-th (k-1…18) subject, S_ijk, were estimated from their peak area using the freely available SITools-FITT spectral modeling software of Soher et al. (24 (link)). It used the full lineshapes of aspartate, glutamate, glutamine, Cho, Cr, mI, NAA and taurine as model functions obtained with the GAVA simulation program for our pulse sequence (25 ). This process, which takes about 30 minutes, uses a priori spectral information and includes non-parametric baseline signal components characterization and Lorenz-Gauss lineshape assumption. Analysis of this baseline modeling showed that for spectra with 5 Hz linewidth, the mean errors of the fit are 3.4%, 2.3% and 2.8% for NAA, Cr and Cho (26 (link)). The S_ijk-s were scaled into absolute amounts, Q_ijk, against a 2 L sphere of C_i^vitro=12.5, 10.0, 3.0 and 7.5 mM NAA, Cr, Cho and mI in water at physiological ionic strength to load the coil and VOI size and position similar to the in vivo studies to approximate a similar B₁ profile up to the intrinsic differences between the phantom and the head due to tissue – RF field interactions at 3 T:
$${\mathcal{Q}}_{ijk}=\frac{C_i^{vitro}}{V}\cdot \frac{S_{ijk}}{S_{ijR}}\cdot {\left(\frac{P_k^{180°}}{P_R^{180°}}\right)}^{1∕2}\cdot f_i\text{millimoles},$$
where V is the voxel volume (0.75 cm³), S_ijR the sphere voxels' metabolites' signal, P_k¹⁸⁰° and P_R¹⁸⁰° are the RF power for a non-selective 1 ms 180° inversion pulse on the k-th subject and reference. To account for different relaxation times in vivo (T₁^vivo, T₂^vivo) and in the phantom (T₁^vitro, T₂^vitro), the Q_ijk in were corrected for each metabolite i with (27 (link)):
$$f_i=\frac{\exp \left(-TE∕T_2^{vitro}\right)}{\exp \left(-TE∕T_2^{vivo}\right)}\cdot \frac{1-\exp \left(-TR∕T_1^{vitro}\right)}{1-\exp \left(-TR∕T_1^{vivo}\right)}.$$