Noise decorrelation optimizes SNR of GABA-edited MRS data: A comparison of RF coil combination methods
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Introduction
Determining the best radiofrequency (RF) coil combination method is crucial for maximizing the signal-to-noise ratio (SNR) to detect low concentration metabolites (e.g., γ-aminobutyric acid (GABA)) in magnetic resonance spectroscopy (MRS). We hypothesized that algorithms accounting for noise correlations between coil elements would optimize SNR, given that phased-array coils provide better SNR than surface coils and allow accelerated acquisitions, and methods accounting for noise correlations outperform those assuming no correlations.
Methods
We examined six coil combination methods, the latter 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); 6) generalized least squares (GLS). We utilized MEGA-PRESS data from 119 participants (mean age: 26.4 ± 1 SD 4.2 years; males/females: 54/65) acquired on 3T GE and Siemens MRI scanners at 11 research sites, obtained from the Big GABA study. We measured the SNR of GABA and N -acetylaspartate (NAA). We also calculated the intersubject coefficients of variation of GABA.
Results
There were significant differences in SNR between coil combination methods for both GABA+ and NAA. More specifically, the noise decorrelation methods produced higher GABA+ and NAA SNR than the other approaches, where nd-comb, WSVD, and GLS produced, on average, ∼37% and ∼34% more SNR than equal weighting, respectively. GLS produced the highest SNR for GABA+ and NAA. The coefficients of variation for GABA+ were generally slightly smaller for the noise decorrelation methods.
Conclusion
Noise-decorrelation methods produced higher SNR than other methods, especially GLS, which should be investigated in advanced editing protocols.
