| Popis: |
The present study aimed to accelerate and improve accuracy of ɣ-aminobutyric acid (GABA) and glutathione (GSH) quantification. These metabolites, present at low concentrations in the brain, are challenging to detect using MR spectroscopy due to the fact that their resonance frequencies overlap with those of other more abundant metabolites. The advanced spectral editing techniques involving J-difference editing that are required to resolve the overlapping signals of these low concentration metabolites are not routinely available on clinical MRI scanners. In this work we implemented on a 3T Siemens Skyra MRI a novel MRS technique called Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES) to simultaneously detect GABA and GSH, developed a novel postprocessing technique that simultaneously models the sum and various difference spectra, and evaluated the performance of the sequence and processing method both in phantoms and in vivo. HERMES was implemented by modifying the Siemens GABA-edited MEGA-PRESS WIP sequence to include two additional sub-experiments – one editing GSH with a single lobe pulse and one editing both GABA and GSH using a dual lobe pulse, and replacing the Siemens pulses with ‘universal' pulses similar to those used in a previous implementation of HERMES on a Philips platform. Performance was assessed in a phantom and 22 healthy adults, 15 of whom provided usable data (7 male, mean age 25.6 ± 2.7 yr). Three of the subjects were scanned 3 times to assess reproducibility. Data were processed and compared using a set of custom scripts in MATLAB. Following frequency and phase correction of individual averages with GANNET, we applied our custom simultaneous linear combination model that iteratively fit the concatenated sum and difference spectra using a least squares routine. SPM was used for tissue segmentation of structural images and FID-A to simulate high-resolution basis sets. The simultaneous modelling technique provided absolute quantification results for 15 metabolite moieties using internal unsuppressed water as a reference. The performance of the simultaneous fitting approach was compared to multiple independent fittings for HERCULES (Hadamard Editing Resolves Chemicals Using Linear-combination Estimation of Spectra) data that had been previously acquired on a 3T Philips Achieva MRI. Although the HERMES sequence implemented on the Siemens platform as part of this project was able to successfully edit both GABA and GSH, and generate line shapes consistent with the work by Saleh et al. (2016), quantification accuracy was disappointing. In the phantom data, GSH and GABA concentrations were both roughly 50% of known levels. Since the actual concentrations in vivo were not known, we were not able to establish accuracy, but quantification agreement between the MEGA-PRESS and HERMES sequences was poor for most metabolites. Specifically, GABA levels were two to three times higher than expected values using both HERMES and GABA-edited MEGA-PRESS. Despite poor absolute agreement, concentrations from HERMES and MEGA-PRESS data were moderately correlated, and HERMES data showed lower coefficients of variation across subjects, suggesting that it may be more reliable. HERMES was also more reproducible across scanning sessions and participants for more metabolites than GABA- or GSH-edited MEGA-PRESS. Our findings also showed that simultaneous fitting using the sum and difference spectra produces lower coefficients of variation for most metabolites than fittings to sum and difference spectra separately. |
