In silico GABA+ MEGA‐PRESS: Effects of signal‐to‐noise ratio and linewidth on modeling the 3 ppm GABA+ resonance

Abstract

To investigate the GABA+ modeling accuracy of MEGA-PRESS GABA+-edited MRS data with various spectral quality scenarios, the influence of varying signal-to-noise ratio (SNR) and linewidth on the model estimates was quantified. MEGA-PRESS data from 46 volunteers were averaged to generate a template MEGA-PRESS spectrum, which was modeled and quantified to generate a GABA+ level ground truth. This spectrum was then manipulated by adding 427 combinations of varying artificial noise levels and line broadening, mimicking variations in GABA+ SNR and B0 homogeneity. GABA+ modeling and quantification was performed with 100 simulated spectra per condition using automated routines in both Gannet 3.0 and Tarquin. The GABA+ estimation error was calculated as the relative deviation to the quantified GABA+ ground truth levels to assess the accuracy of GABA+ modeling. Finally, the accordance between the simulations and different in vivo scenarios was assessed. The GABA+ estimation error was smaller than 5% for all GABA+ SNR values with creatine linewidths lower than 9.7 Hz in Gannet 3.0 or unequal 10.6 Hz in Tarquin. The standard deviation of the GABA+ amplitude over 100 spectra per condition varied between 3.1 and 17% (Gannet 3.0) and between 1 and 11% (Tarquin) over the in vivo relevant GABA+ SNR range between 2.6 and 3.5. GABA+ edited studies might be realized for voxels with low GABA+ SNR at the cost of higher group-level variance. The accuracy of GABA+ modeling had no relation to commonly used quality metrics. The Tarquin algorithm was found to be more robust against linewidth changes than the fitting algorithm in Gannet.