Errors in 1H‐MRS estimates of brain metabolite concentrations caused by failing to take into account tissue‐specific signal relaxation

Accurate measurement of brain metabolite concentrations with proton magnetic resonance spectroscopy (¹H‐MRS) can be problematic because of large voxels with mixed tissue composition, requiring adjustment for differing relaxation rates in each tissue if absolute concentration estimates are desired. Adjusting for tissue‐specific metabolite signal relaxation, however, also requires a knowledge of the relative concentrations of the metabolite in gray (GM) and white (WM) matter, which are not known a priori. Expressions for the estimation of the molality and molarity of brain metabolites with ¹H‐MRS are extended to account for tissue‐specific relaxation of the metabolite signals and examined under different assumptions with simulated and real data. Although the modified equations have two unknowns, and hence are unsolvable explicitly, they are nonetheless useful for the estimation of the effect of tissue‐specific metabolite relaxation rates on concentration estimates under a range of assumptions and experimental parameters using simulated and real data. In simulated data using reported GM and WM T₁ and T₂ times for N‐acetylaspartate (NAA) at 3 T and a hypothetical GM/WM NAA ratio, errors of 6.5–7.8% in concentrations resulted when TR = 1.5 s and TE = 0.144 s, but were reduced to less than 0.5% when TR = 6 s and TE = 0.006 s. In real data obtained at TR/TE = 1.5 s/0.04 s, the difference in the results (4%) was similar to that obtained with simulated data when assuming tissue‐specific relaxation times rather than GM–WM‐averaged times. Using the expressions introduced in this article, these results can be extrapolated to any metabolite or set of assumptions regarding tissue‐specific relaxation. Furthermore, although serving to bound the problem, this work underscores the challenge of correcting for relaxation effects, given that relaxation times are generally not known and impractical to measure in most studies. To minimize such effects, the data should be acquired with pulse sequence parameters that minimize the effect of signal relaxation.