Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations

Abstract

Purpose: To develop a tissue correction for GABA-edited magnetic resonance spectroscopy (MRS) that appropriately addresses differences in voxel gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) fractions.

Materials and methods: Simulations compared the performance of tissue correction approaches. Corrections were then applied to in vivo data from 16 healthy volunteers, acquired at 3T. GM, WM, and CSF fractions were determined from T1 -weighted images. Corrections for CSF content, GM/WM GABA content, and water relaxation of the three compartments are combined into a single, fully corrected measurement.

Results: Simulations show that CSF correction increases the dependence of GABA measurements on GM/WM fraction, by an amount equal to the fraction of CSF. Furthermore, GM correction substantially (and nonlinearly) increases the dependence of GABA measurements on GM/WM fraction, for example, by a factor of over four when the voxel GM tissue fraction is 50%. At this tissue fraction, GABA is overestimated by a factor of 1.5. For the in vivo data, correcting for voxel composition increased measured GABA values (P < 0.001 for all regions), but did not reduce intersubject variance (P > 0.5 for all regions). Corrected GABA values differ significantly based on the segmentation procedure used (P < 0.0001) and tissue parameter assumptions made (P < 0.0001).

Conclusion: We introduce a comprehensive tissue correction factor that adjusts GABA measurements to correct for different voxel compositions of GM, WM, and CSF.

Keywords: GABA-edited MRS; tissue correction; tissue segmentation; voxel coregistration; voxel localization.

© 2015 Wiley Periodicals, Inc.

Figures

Figure 1

Correcting for differing GABA concentration in GM and WM. The simulated voxel has (A) 8% and (B) 30% CSF. The GABA index is simulated across all normalized GM (and WM) tissue fractions, assuming no GABA in CSF, GABA concentration in GM = 1 and in WM = 0.5. The simulated, uncorrected GABA index (shown as a dashed line) is then corrected for differential tissue content using Equation 1, using values of α of 1 (corresponding to CSF correction), 0 (corresponding to GM correction), 0.5 (the true correction value for this simulation), 0.7 and 0.3 (corresponding to over- and under-estimates). The correction aims to normalize the GABA index to an all GM voxel. The efficacy of correction for a given value of α can be evaluated by the ability of the accuracy of correction at a specific GM tissue fraction (i.e., difference between a GABA index and 1) and the dependency of the GABA index on the voxel tissue fractions, denoted by the slope of each correction factor.

Figure 2

Example spectra from one subject, after spectral registration for frequency correction for each of the 5 voxels included in the in-vivo dataset. The voxel location for each spectrum overlaid on the T1-weighted image is shown.

Figure 3

GABA versus gray matter fraction. GABA was quantified using tissue-specific relaxation parameters (Equation 4). All data was pooled for the linear fit, the equation of the fit is GABA = 1.66fGM + 1.24, giving a calculated α = 0.43.

Figure 4

Comparison of corrections. A) Uncorrected GABA concentrations (white) and fully-corrected concentrations are shown for all five brain regions and all data pooled. Correcting for voxel content and correcting for compartment-specific water relaxation result in significantly increased GABA measurements (t-tests p 0.05).

Figure 5

Investigation of the impact of correction factors. A) Correlations of uncorrected and fully corrected concentrations show strong relationships (0.812<0.98). B) Plot of the magnitude of the full correction factor against uncorrected GABA concentrations cuncorr showing no bias in correction factors

Figure 6

Bland-Altman plots assessing the agreement between FSL and SPM voxel segmentation results. While there is agreement between these two segmentation results, FSL biases towards CSF and SPM biases towards GM. WM fractions are relatively consistent between the two methods.

Figure 7

Comparison of resulting GABA estimates when using two different sets of relaxation parameters. In all regions, there is a significant difference in GABA quantification between the two parameter sets. The Wansapura values (applied in Figures 3, 4 and 5) tend to increase the GABA concentrations more than the more recent Stanisz values.