  Many tools exist for the quantification of GABA-edited magnetic resonance
spectroscopy (MRS) data. Despite a recent consensus effort by the MRS
community, literature comparing them is sparse but indicates a methodological
bias. While invivo data sets can ascertain the level of agreement between
tools, ground-truth is required to establish accuracy, and investigate the
sources of discrepancy. We present a novel approach to benchmarking GABA
quantification tools, using several series of phantom experiments with iterated
GABA concentration. Each series presents a different set of background
metabolites and environmental conditions allowing comparison of not only
individual estimates, but the ability of tools to characterise changes in GABA
across a range of potential confounds. The methodology of the phantom
experiments is presented, as well as characterisation of the data. We also
perform an initial comparative analysis of several common MRS quantification
tools and in-house code, to illustrate utility of the dataset and the potential
bias introduced by different quantification methods. The GABA-to-NAA ratios
reported by each tool are compared to the ground-truth, and estimation accuracy
is assessed by linear regression of this relationship. While the linearity of
GABA-to-NAA gradients is generally captured by all of the tools, a large
variation in the slope, offset and environmental stability of the gradient is
observed. The primary driver of differences in linear combination modelling is
the choice of basis function. However, tools employing a common basis and
pre-processing still produce differences on the order of 4%. Less-strictly
parametrised fitting approaches appear to improve the robustness of
quantification, but accurate modelling of edit efficiency calculations is still
necessary to avoid systematic offsets. In general, the level of variation
suggests that comparisons across...
