Optimized in vivo brain glutamate measurement using long-echo-time semi-LASER at 7 T
Document Type
Article
Publication Date
11-1-2018
Journal
NMR in Biomedicine
Volume
31
Issue
11
URL with Digital Object Identifier
10.1002/nbm.4002
Abstract
A short echo time (TE) is commonly used for brain glutamate measurement by 1H MRS to minimize drawbacks of long TE such as signal modulation due to J evolution and T2 relaxation. However, J coupling causes the spectral patterns of glutamate to change with TE, and the shortest achievable TE may not produce the optimal glutamate measurement. The purpose of this study was to determine the optimal TE for glutamate measurement at 7 T using semi-LASER (localization by adiabatic selective refocusing). Time-domain simulations were performed to model the TE dependence of glutamate signal energy, a measure of glutamate signal strength, and were verified against measurements made in the human sensorimotor cortex (five subjects, 2 × 2 × 2 cm3 voxel, 16 averages) on a 7 T MRI scanner. Simulations showed a local maximum of glutamate signal energy at TE = 107 ms. In vivo, TE = 105 ms produced a low Cramér-Rao lower bound of 6.5 ± 2.0% across subjects, indicating high-quality fits of the prior knowledge model to in vivo data. TE = 105 ms also produced the greatest glutamate signal energy with the smallest inter-subject glutamate-to-creatine ratio (Glu/Cr) coefficient of variation (CV), 4.6%. Using these CVs, we performed sample size calculations to estimate the number of participants per group required to detect a 10% change in Glu/Cr between two groups with 95% confidence. 13 were required at TE = 45 ms, the shortest achievable echo time on our 7 T MRI scanner, while only 5 were required at TE = 105 ms, indicating greater statistical power. These results indicate that TE = 105 ms is optimum for in vivo glutamate measurement at 7 T with semi-LASER. Using long TE decreases power deposition by allowing lower maximum RF pulse amplitudes in conjunction with longer RF pulses. Importantly, long TE minimizes macromolecule contributions, eliminating the requirement for acquisition of separate macromolecule spectra or macromolecule fitting techniques, which add additional scan time or bias the estimated glutamate fit.