Relaxation-corrected macromolecular model enables determination of 1 H longitudinal T1 -relaxation times and concentrations of human brain metabolites at 9.4T.

PURPOSE: Ultrahigh field MRS has improved characterization of the neurochemical profile. To compare results obtained at 9.4T to those from lower field strengths, it is of interest to quantify the concentrations of metabolites measured. Thus, measuring T1 -relaxation times is necessary to correct for T1 -weighting that occurs in acquisitions for single-voxel spectroscopy and spectroscopic imaging. A macromolecule (MM) simulation model was developed to fit MM contributions to the short TE inversion series used to measure T1 -relaxation times. METHODS: An inversion series with seven time points was acquired with metabolite-cycled STEAM to estimate T1 -relaxation times of metabolites. A short TE was employed in this study to retain signals from metabolites with short T2 -relaxation times and J-couplings. The underlying macromolecule spectrum was corrected by developing a sequence-specific, relaxation-corrected simulated MM model. Quantification of metabolite peaks was performed using internal water referencing and relaxation corrections. RESULTS: T1 -relaxation times for metabolites range from approximately 750 to approximately 2000 ms and approximately 1000 to approximately 2400 ms in gray matter (GM)- and white matter (WM)- rich voxels, respectively. Quantification of metabolites was compared between GM and WM voxels, as well as between results that used a simulated MM spectrum against those that used an experimentally acquired MM spectrum. Metabolite concentrations are reported in mmol/kg quantities. CONCLUSION: T1 -relaxation times are reported for nonsinglet resonances for the first time at 9.4T by use of a MM simulation model to account for contributions from the MM spectrum. In addition to T1 -relaxation times, quantification results of metabolites from GM- and WM-rich voxels are reported.

Phosphorus transversal relaxation times and metabolite concentrations in the human brain at 9.4 T.

A method to estimate phosphorus (31 P) transversal relaxation times (T2 s) of coupled spin systems is demonstrated. Additionally, intracellular and extracellular pH and relaxation-corrected metabolite concentrations are reported. Echo time (TE) series of 31 P metabolite spectra were acquired using stimulated echo acquisition mode (STEAM) localization. Spectra were fitted using LCModel with accurately modeled Versatile Simulation, Pulses and Analysis (VeSPA) basis sets accounting for J-evolution of the coupled spin systems. T2 s were estimated by fitting a single exponential two-parameter model across the TE series. Fitted inorganic phosphate frequencies were used to calculate pH, and estimated relaxation times were used to determine the relaxation-corrected brain metabolite concentrations on an assumption of 3 mM γ-ATP. The method was demonstrated in healthy human brain at a field strength of 9.4 T. T2 times of ATP and nicotinamide adenine dinucleotide (NAD) were shortest between 8 and 20 ms, followed by T2 s of inorganic phosphate between 25 and 50 ms, and phosphocreatine with a T2 of 100 ms. Phosphomonoesters and phosphodiesters had the longest T2 s of about 130 ms. The measured T2 s are comparable with literature values and fit in a decreasing trend with increasing field strengths. Calculated pHs and metabolite concentrations are also comparable with literature values.

In vivo characterization of downfield peaks at 9.4 T: T2 relaxation times, quantification, pH estimation, and assignments.

PURPOSE: Relaxation times are a valuable asset when determining spectral assignments. In this study, apparent T2 relaxation times ( T2app ) of downfield peaks are reported in the human brain at 9.4 T and are used to guide spectral assignments of some downfield metabolite peaks. METHODS: Echo time series of downfield metabolite spectra were acquired at 9.4 T using a metabolite-cycled semi-LASER sequence. Metabolite spectral fitting was performed using LCModel V6.3-1L while fitting a pH sweep to estimate the pH of the homocarnosine (hCs) imidazole ring. T2app were calculated by fitting the resulting relative amplitudes of the peaks to a mono-exponential decay across the TE series. Furthermore, estimated tissue concentrations of molecules were calculated using the relaxation times and internal water as a reference. RESULTS: T2app of downfield metabolites are reported within a range from 16 to 32 ms except for homocarnosine with T2app of 50 ms. Correcting T2app for exchange rates ( T2corr ) resulted in relaxation times between 20 and 33 ms. The estimated pH values based on hCs imidazole range from 7.07 to 7.12 between subjects. Furthermore, analyzing the linewidths of the downfield peaks and their T2app contribution led to possible peak assignments. CONCLUSION: T2app relaxation times were longer for the assigned metabolite peaks compared to the unassigned peaks. Tissue pH estimation in vivo with proton MRS and simultaneous quantification of amide protons at 8.30 ± 0.15 ppm is likely possible. Based on concentration, linewidth, and exchange rates measurements, tentative peak assignments are discussed for adenosine triphosphate (ATP), N-acetylaspartylglutamate (NAAG), and urea.

ProFit-1D-A 1D fitting software and open-source validation data sets.

PURPOSE: Accurate and precise MRS fitting is crucial for metabolite concentration quantification of 1 H-MRS spectra. LCModel, a spectral fitting software, has shown to have certain limitations to perform advanced spectral fitting by previous literature. Herein, we propose an open-source spectral fitting algorithm with adaptive spectral baseline determination and more complex cost functions. THEORY: The MRS spectra are characterized by several parameters, which reflect the environment of the contributing metabolites, properties of the acquisition sequence, or additional disturbances. Fitting parameters should accurately describe these parameters. Baselines are also a major contributor to MRS spectra, in which smoothness of the spline baselines used for fitting can be adjusted based on the properties of the spectra. Three different cost functions used for the minimization problem were also investigated. METHODS: The newly developed ProFit-1D fitting algorithm is systematically evaluated for simulations of several types of possible in vivo parameter variations. Although accuracy and precision are tested with simulated spectra, spectra measured in vivo at 9.4 T are used for testing precision using subsets of averages. ProFit-1D fitting results are also compared with LCModel. RESULTS: Both ProFit-1D and LCModel fitted the spectra well with induced parameter and baseline variations. ProFit-1D proved to be more accurate than LCModel for simulated spectra. However, LCModel showed a somewhat increased precision for some spectral simulations and for in vivo data. CONCLUSION: The open-source ProFit-1D fitting algorithm demonstrated high accuracy while maintaining precise metabolite concentration quantification. Finally, through the newly proposed cost functions, new ways to improve fitting were shown.

A novel method to measure T1 -relaxation times of macromolecules and quantification of the macromolecular resonances.

PURPOSE: Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T1 relaxation times among individual MM peaks must be considered. Characterization of T1 -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T1 -relaxation times of individual MM peaks is reported in this work. METHODS: Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T1 -relaxation times. RESULTS: T1 -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported. CONCLUSION: A novel DIR method is reported in this work to calculate T1 -relaxation times of MMs in the human brain. T1 -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T.

In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times.

PURPOSE: Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) 1 H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components. METHODS: A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1 H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel. RESULTS: A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks. CONCLUSION: Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations.

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

T2 relaxation times of macromolecules and metabolites in the human brain at 9.4 T.

PURPOSE: Relaxation times can contribute to spectral assignment. In this study, effective T2 relaxation times ( T2eff ) of macromolecules are reported for gray and white matter-rich voxels in the human brain at 9.4 T. The T2eff of macromolecules are helpful to understand their behavior and the effect they have on metabolite quantification. Additionally, for absolute quantification of metabolites with magnetic resonance spectroscopy, appropriate T2 values of metabolites must be considered. The T2 relaxation times of metabolites are calculated after accounting for TE/sequence-specific macromolecular baselines. METHODS: Macromolecular and metabolite spectra for a series of TEs were acquired at 9.4 T using double inversion-recovery metabolite-cycled semi-LASER and metabolite-cycled semi-LASER, respectively. The T2 relaxation times were calculated by fitting the LCModel relative amplitudes of macromolecular peaks and metabolites to a mono-exponential decay across the TE series. Furthermore, absolute concentrations of metabolites were calculated using the estimated relaxation times and internal water as reference. RESULTS: The T2eff of macromolecules are reported, which range from 13 ms to 40 ms, whereas, for metabolites, they range from 40 ms to 110 ms. Both macromolecular and metabolite T2 relaxation times are observed to follow the decreasing trend, with increasing B0 . The linewidths of metabolite singlets can be fully attributed to T2 and B0 components. However, in addition to these components, macromolecule linewidths have contributions from J-coupling and overlapping resonances. CONCLUSION: The T2 relaxation times of all macromolecular and metabolite peaks at 9.4 T in vivo are reported for the first time. Metabolite relaxation times were used to calculate the absolute metabolite concentrations.

Investigation of the influence of macromolecules and spline baseline in the fitting model of human brain spectra at 9.4T.

PURPOSE: In this study, the influence of experimentally measured macromolecules and spline baseline on the quantification results of proton MRS data was investigated. METHODS: Proton MRS spectra from the left parietal lobe and the occipital lobe were acquired at 9.4T in the human brain using metabolite-cycled semi-LASER. Then, the left parietal lobe data, along with the occipital lobe, spectra were quantified and the influence of the inclusion of experimentally measured macromolecular basis sets in the fitting model was evaluated. Furthermore, the effect of the stiffness of the fitted spline baselines on the resulting metabolite concentrations was evaluated. RESULTS: In general, concentrations were higher for metabolites in occipital lobe than the left parietal lobe. The inclusion of an experimentally acquired measured macromolecular basis set from another brain region neither affected the quantification results nor the resulting spline baselines significantly. A highly flexible spline baseline led to overestimation or underestimation of metabolite concentrations. Differences of above 15% in the quantification of metabolite levels for both lobes were observed for several metabolites using LCModel default settings for spline baselines and macromolecules in comparison to stiffer spline baselines. CONCLUSION: Fitting with the default LCModel macromolecular basis set and spline baseline model had significant influence in the resulting spline baselines, leading to large deviations both in the concentrations and fitted macromolecular components. The number of knots in the spline may create overflexible baselines, which can potentially lead to quantification errors. Interestingly, the interchange of macromolecular basis set between occipital lobe and left parietal lobe spectra had less influence on the quantification results compared to the default LCModel settings.

Simultaneous detection of metabolite concentration changes, water BOLD signal and pH changes during visual stimulation in the human brain at 9.4T.

This study presents a method to directly link metabolite concentration changes and BOLD response in the human brain during visual stimulation by measuring the water and metabolite signals simultaneously. Therefore, the metabolite-cycling (MC) non-water suppressed semiLASER localization technique was optimized for functional 1H MRS in the human brain at 9.4 T. Data of 13 volunteers were acquired during a 26:40 min visual stimulation block-design paradigm. Activation-induced BOLD signal was observed in the MC water signal as well as in the NAA-CH3 and tCr-CH3 singlets. During stimulation, glutamate concentration increased 2.3 ± 2.0% to a new steady-state, while a continuous increase over the whole stimulation period could be observed in lactate with a mean increase of 35.6 ± 23.1%. These increases of Lac and Glu during brain activation confirm previous findings reported in literature. A positive correlation of the MC water BOLD signal with glutamate and lactate concentration changes was found. In addition, a pH decrease calculated from a change in the ratio of PCr to Cr was observed during brain activation, particularly at the onset of the stimulation.