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Many tools exist for the quantification of GABA-edited magnetic resonancespectroscopy (MRS) data. Despite a recent consensus effort by the MRScommunity, literature comparing them is sparse but indicates a methodologicalbias. While invivo data sets can ascertain the level of agreement betweentools, ground-truth is required to establish accuracy, and investigate thesources of discrepancy. We present a novel approach to benchmarking GABAquantification tools, using several series of phantom experiments with iteratedGABA concentration. Each series presents a different set of backgroundmetabolites and environmental conditions allowing comparison of not onlyindividual estimates, but the ability of tools to characterise changes in GABAacross a range of potential confounds. The methodology of the phantomexperiments is presented, as well as characterisation of the data. We alsoperform an initial comparative analysis of several common MRS quantificationtools and in-house code, to illustrate utility of the dataset and the potentialbias introduced by different quantification methods. The GABA-to-NAA ratiosreported by each tool are compared to the ground-truth, and estimation accuracyis assessed by linear regression of this relationship. While the linearity ofGABA-to-NAA gradients is generally captured by all of the tools, a largevariation in the slope, offset and environmental stability of the gradient isobserved. The primary driver of differences in linear combination modelling isthe choice of basis function. However, tools employing a common basis andpre-processing still produce differences on the order of 4%. Less-strictlyparametrised fitting approaches appear to improve the robustness ofquantification, but accurate modelling of edit efficiency calculations is stillnecessary to avoid systematic offsets. In general, the level of variationsuggests that comparisons across...

Benchmarking GABA Quantification: A Ground Truth Data Set and Comparative Analysis of TARQUIN, LCModel, jMRUI and Gannet