Abstract
INTRODUCTION: Determining the optimal radiofrequency (RF) coil combination method for magnetic resonance spectroscopy (MRS) is crucial for maximizing the signal-to-noise ratio (SNR) and reliably detecting low-concentration metabolites, such as γ-aminobutyric acid (GABA). We compared the performances of several previously proposed algorithms using GABA-edited (1)H MRS data. Given that phased-array coils often exhibit noise correlations that reduce SNR, we hypothesized that noise decorrelation algorithms would be most effective. METHODS: We examined six coil combination methods, with the second half accounting for noise correlations: 1) equal weighting; 2) signal weighting; 3) S/N(2) weighting; 4) noise-decorrelated combination (nd-comb); 5) whitened singular value decomposition (WSVD); and 6) generalized least squares (GLS). Each method was applied to 119 GABA-edited MEGA-PRESS datasets acquired on 3 T GE and Siemens MRI scanners across 11 research sites. We estimated the SNR of GABA+ and N-acetylaspartate (NAA) and tested for statistical differences between the six approaches. We also calculated the intersubject coefficients of variation (CVs) of GABA+/creatine (Cr) ratios. RESULTS: There were significant differences in the SNR of GABA+ and NAA between the methods. Noise decorrelation methods produced higher SNR compared to the other approaches, with nd-comb, WSVD, and GLS yielding, on average, approximately 37 % more GABA+ and 34 % more NAA SNR than equal weighting. GLS yielded the highest SNR for both GABA+ and NAA. The CVs for GABA+/Cr were generally somewhat smaller when using noise decorrelation. CONCLUSION: As predicted, noise decorrelation coil combination, particularly GLS, produced optimal SNR for GABA-edited MRS data.