Importance of linear combination modeling for quantification of Glutathione and GABA levels using Hadamard-edited MRS

Purpose Two main approaches are used for spectral analysis of edited data: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach will improve quantification of HERMES data compared with simple peak fitting. Methods A test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE 80 ms for HERMES and TE 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variance (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate. Results The reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0% to 9.9%. For MEGA-PRESS data, the GSH reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3% and 8.8% respectively. Conclusion Linear combination modeling with simulated basis functions substantially improves the reproducibility of GSH quantification for HERMES data.