Simultaneous mapping of metabolites and individual macromolecular components via ultra‐short acquisition delay ¹H MRSI in the brain at 7T

Abstract

PurposeShort‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID‐MRSI) data.MethodsThe measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel‐Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over‐parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID‐MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets.ResultsThe MM model using nine individual AMARES‐parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice.ConclusionThe proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231–1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Macromolecule-suppressed GABA measurements correlate more strongly with behavior than macromolecule-contaminated GABA+ measurements

“…Additionally, MM tend to have quite short T 2 relaxation times (Behar et al, 1994), and since GABA-edited MRS experiments are normally performed at medium TE (68–80 ms), it has been suggested that the MM signal arises from a mobile form of lysine rather than a bound MM pool (Choi et al, 2007). Some studies have reported regional and tissue-type differences in the MM baseline (Považan et al, 2018, 2015; Schaller et al, 2014), but this is contradicted by other reports (Giapitzakis et al, 2018; Snoussi et al, 2015). Whatever the source of the contaminating MM signal or its heterogeneity across the brain, our results demonstrate that its removal from the edited GABA signal improves the discriminative power of correlational analyses of GABA measurements and behavioral metrics.…”

Effects of different macromolecular models on reproducibility of FID‐MRSI at 7T

“…The processing included a multichannel spectroscopic data combined by matching image calibration data (MUSICAL) coil combination of the raw data, parallel‐imaging reconstruction, Hamming filtering, removal of lipid signal via L 2 ‐regularization, and fitting of the individual spectra with LCModel (version 6.3; LCModel Inc., Oakville, ON, Canada). For this purpose, 3 different basis sets were used, each consisting of 15 metabolite resonances simulated in NMR Scope (jMRUI 5.0), as well as the following: full MM: A single in vivo MM spectrum obtained in previous study by nulling of brain metabolite signals via double inversion recovery (2 inversion 40 ms Wurst pulses, acquisition delay/TR of 1.3 ms/879 ms, TI 1 of 570 ms, TI 2 of 21 ms, flip angle of 55°, matrix size of 32 × 32, nominal voxel volume of 5.6 × 5.6 × 12 mm 3 , 2048 complex spectral data points, acquisition bandwidth of 6000 Hz, water suppression enhanced through T 1 effects [WET]), removing the residual metabolite signals using advanced method for accurate, robust, and efficient spectral fitting (AMARES), as described by Craveiro et al, and averaging over different brain regions and healthy volunteers. param MM: Nine individual MM components extracted from measured in vivo metabolite‐nulled spectra, with applied soft constraints (via LCModel parameter CHRATO) to avoid overparameterization of the fitting model (detailed description of the parameterization can be found in Považan et al). no MM: With no macromolecular information included. …”

“…from measured in vivo metabolite-nulled spectra, with applied soft constraints (via LCModel parameter CHRATO) to avoid overparameterization of the fitting model (detailed description of the parameterization can be found in Považan et al 24 ). 3. no MM: With no macromolecular information included.…”

“…This can be avoided by the application of soft constraints. Both approaches, using a whole MM spectrum or several parameterized MM components, were evaluated for fairly high SNR (thus, long scan times). Although the precision of metabolite quantification was investigated, the effects on reproducibility were not assessed.…”

Overdiscrete echo‐planar spectroscopic imaging with correlated higher‐order phase correction

“…Phase-encoded MRSI (PE MRSI) methods using short TE and TR [12][13][14][15][16] are capable of measuring large matrix sizes in clinically feasible times. Although PE MRSI is SNR-efficient, the sequence only samples 1 full k-space throughout the scan duration.…”