T 2 relaxation times of macromolecules and metabolites in the human brain at 9.4 T

Simultaneous Concentration and T2 Mapping of Brain Metabolites by Fast Multi-Echo Spectroscopic Imaging

The purpose of this study was to produce metabolite-specific T2 and concentration maps in a clinically compatible time frame. A multi-TE 2D MR spectroscopic imaging (MRSI) experiment (multi-echo single-shot MRSI [MESS-MRSI]) deployed truncated and partially sampled multi-echo trains from single scans and was combined with simultaneous multiparametric model fitting. It was tested in vivo for the brain in five healthy subjects. Cramér-Rao lower bounds (CRLB) were used as the measure of performance. The novel method was compared with (1) traditional multi-echo multi-shot (MEMS) MRSI and, as proof of concept, with (2) a truncated version of MEMS, which mimics the MESS acquisition (MESS-mocked) on the original fully sampled MEMS dataset. MESS-MRSI simultaneously yields concentration and T2 maps with a nominal voxel size of ~2 cm3 with a 16 × 16 FOV matrix in 7 min scan time. The estimated values not only align well with the equivalent mocked experiment but are also in good agreement with the traditional threefold longer MEMS acquisition. The MESS-MRSI scheme extends former findings for single-voxel MESS, with improvements in CRLB ranging from 17% to 45% for concentrations and 10% to 23% for T2s when compared to traditional MEMS. This finding suggests that concentrations and T2 times can be reliably estimated in a multi-echo spectroscopic imaging exam by trading off spectral resolution (for some of the acquired TEs) with a significant reduction in scan time, as long as (1) an appropriate bidimensional frequency-TE model is deployed and (2) one TE is sampled in full. Thus, high spectral resolution information can be injected to the partially sampled TEs during fitting by prior knowledge from the one fully sampled TE. Tissue-type and regional distributions of 16 metabolite concentrations align well with the literature, and T2 distributions for five major metabolites are described by region and tissue. The novel MRSI acquisition strategy, based on partially sampled single-shot multi-echo trains twinned to multiparametric fitting, is optimally suited to provide simultaneous 2D concentration and T2 maps in clinic-compatible scan times. MESS principles allow embedding advanced MRSI techniques to further improve speed, coverage, or resolution. Preliminary findings from a cohort of five subjects reveal correlations between T2 relaxation times and the relative fraction of gray/white matter, suggesting tissue-type-dependent microstructural changes.

Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H-MRS at 3 T and 7 T: The PRaMM Model

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Evidence for biexponential glutamate T2 relaxation in human visual cortex at 3T: A functional MRS study

Functional magnetic resonance spectroscopy (fMRS) measures dynamic changes in metabolite concentration in response to neural stimulation. The biophysical basis of these changes remains unclear. One hypothesis suggests that an increase or decrease in the glutamate signal detected by fMRS could be due to neurotransmitter movements between cellular compartments with different T2 relaxation times. Previous studies reporting glutamate (Glu) T2 values have generally sampled at echo times (TEs) within the range of 30-450 ms, which is not adequate to observe a component with short T2 (<20 ms). Here, we acquire MRS measurements for Glu, (t) total creatine (tCr) and total N-acetylaspartate (tNAA) from the visual cortex in 14 healthy participants at a range of TE values between 9.3-280 ms during short blocks (64 s) of flickering checkerboards and rest to examine both the short- and long-T2 components of the curve. We fit monoexponential and biexponential Glu, tCr and tNAA T2 relaxation curves for rest and stimulation and use Akaike information criterion to assess best model fit. We also include power calculations for detection of a 2% shift of Glu between compartments for each TE. Using pooled data over all participants at rest, we observed a short Glu T2-component with T2 = 10 ms and volume fraction of 0.35, a short tCr T2-component with T2 = 26 ms and volume fraction of 0.25 and a short tNAA T2-component around 15 ms with volume fraction of 0.34. No statistically significant change in Glu, tCr and tNAA signal during stimulation was detected at any TE. The volume fractions of short-T2 component between rest and active conditions were not statistically different. This study provides evidence for a short T2-component for Glu, tCr and tNAA but no evidence to support the hypothesis of task-related changes in glutamate distribution between short and long T2 compartments.

Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for advanced MRS

BACKGROUND

To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study.

METHODS

ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based on the default white matter and gray matter T2 reference values in Osprey and 2) shorter WM and GM T2 values from recent literature.

RESULTS

No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio.

CONCLUSIONS

ISTHMUS facilitated data acquisition and post-processing and reduced operator workload to eliminate potential human error.

Age-related differences in macromolecular resonances observed in ultra-short-TE STEAM MR spectra at 7T

PURPOSE

To understand how macromolecular content varies in the human brain with age in a large cohort of healthy subjects.

METHODS

In-vivo 1H-MR spectra were acquired using ultra-short TE STEAM at 7T in the posterior cingulate cortex. Macromolecular content was studied in 147 datasets from a cohort ranging in age from 19 to 89 y. Three fitting approaches were used to evaluate the macromolecular content: (1) a macromolecular resonances model developed for this study; (2) LCModel-simulated macromolecules; and (3) a combination of measured and LCModel-simulated macromolecules. The effect of age on the macromolecular content was investigated by considering age both as a continuous variable (i.e., linear regressions) and as a categorical variable (i.e., multiple comparisons among sub-groups obtained by stratifying data according to age by decade).

RESULTS

While weak age-related effects were observed for macromolecular peaks at ˜0.9 (MM09), ˜1.2 (MM12), and ˜1.4 (MM14) ppm, moderate to strong effects were observed for peaks at ˜1.7 (MM17), and ˜2.0 (MM20) ppm. Significantly higher MM17 and MM20 content started from 30 to 40 y of age, while for MM09, MM12, and MM14, significantly higher content started from 60 to 70 y of age.

CONCLUSIONS

Our findings provide insights into age-related differences in macromolecular contents and strengthen the necessity of using age-matched measured macromolecules during quantification.

Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS

Background

To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study.

Methods

ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based the default white matter and gray matter T2 reference values in Osprey; 2) shorter WM and GM T2 values from recent literature; and 3) reduced CSF fractions.

Results

No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio.

Conclusions

ISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error.

Multi-echo single-shot spectroscopy combined with simultaneous 2D model fitting for fast and accurate measurement of metabolite-specific concentrations and T2 relaxation times

The purpose of the current study was to develop a novel single-voxel MR spectroscopy acquisition scheme to simultaneously determine metabolite-specific concentrations and transverse relaxation times within realistic clinical scan times. Partly truncated multi-TE data are acquired as an echo train in a single acquisition (multi-echo single-shot [MESS]). A 2D multiparametric model fitting approach combines truncated, low-resolved short TE data with fully sampled, highly resolved, longer TE data to yield concentration and T2 estimates for major brain metabolites simultaneously. Cramer-Rao lower bounds (CRLB) are used as a measure of performance. The novel scheme was compared with traditional multi-echo multi-shot methods. In silico, in vitro, and in vivo experiments support the findings. MESS schemes, requiring only 2 min 12 s for the acquisition of three echo times, provide valid concentration and relaxation estimates for multiple metabolites and outperform traditional methods for simultaneous determinations of metabolite-specific T2 s and concentrations, with improvements ranging from 5% to 30% for T2 s and from 10% to 50% for concentrations. However, substantial unsuppressed residual water signals may hamper the method's reproducibility, as observed in an initial experiment setup that prioritizes short TEs with severely truncated acquisition for the benefit of signal-to-noise ratio (SNR). Nevertheless, CRLB have been confirmed to be well suited as design criteria, and within-session repeatability approaches CRLB when residual water is removed in postprocessing by exploiting longer and less truncated data recordings. MESS MRS combined with 2D model fitting promises comparable accuracy, increased precision, or inversely shorter experimental times compared with traditional approaches. However, the optimal design must be investigated as a trade-off between SNR, the truncation factor, and TE batch selections, all of which influence the robustness of estimations.

Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model

Purpose

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints.

Methods

Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated.

Results

The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out.

Conclusion

Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging

Biomedical imaging, especially molecular imaging, has been a driving force in scientific discovery, technological innovation, and precision medicine in the past two decades. While substantial advances and discoveries in chemical biology have been made to develop molecular imaging probes and tracers, translating these exogenous agents to clinical application in precision medicine is a major challenge. Among the clinically accepted imaging modalities, magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) exemplify the most effective and robust biomedical imaging tools. Both MRI and MRS enable a broad range of chemical, biological and clinical applications from determining molecular structures in biochemical analysis to imaging diagnosis and characterization of many diseases and image-guided interventions. Using chemical, biological, and nuclear magnetic resonance properties of specific endogenous metabolites and native MRI contrast-enhancing biomolecules, label-free molecular and cellular imaging with MRI can be achieved in biomedical research and clinical management of patients with various diseases. This review article outlines the chemical and biological bases of several label-free chemically and molecularly selective MRI and MRS methods that have been applied in imaging biomarker discovery, preclinical investigation, and image-guided clinical management. Examples are provided to demonstrate strategies for using endogenous probes to report the molecular, metabolic, physiological, and functional events and processes in living systems, including patients. Future perspectives on label-free molecular MRI and its challenges as well as potential solutions, including the use of rational design and engineered approaches to develop chemical and biological imaging probes to facilitate or combine with label-free molecular MRI, are discussed.

Mapping of glutamate metabolism using 1H FID-MRSI after oral administration of [1-13C]Glc at 9.4 T

Glutamate is the major excitatory transmitter in the brain and malfunction of the related metabolism is associated with various neurological diseases and disorders. The observation of labeling changes in the spectra after the administration of a 13C labelled tracer is a common tool to gain better insights into the function of the metabolic system. But so far, only a very few studies presenting the labeling effects in more than two voxels to show the spatial dependence of metabolism. In the present work, the labeling effects were measured in a transversal plane in the human brain using ultra-short TE and TR 1H FID-MRSI. The measurement set-up was most simple: The [1-13C]Glc was administered orally instead of intravenous and the spectra were measured with a pure 1H technique without the need of a 13C channel (as Boumezbeur et al. demonstrated in 2004). Thus, metabolic maps and enrichment curves could be obtained for more metabolites and in more voxels than ever before in human brain. Labeling changes could be observed in [4-13C]glutamate, [3-13C]glutamate+glutamine, [2-13C]glutamate+glutamine, [4-13C]glutamine, and [3-13C]aspartate with a high temporal (3.6 min) and spatial resolution (32 × 32 grid with nominal voxel size of 0.33 µL) in five volunteers.

The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches

PURPOSE

Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T₂ , T₁ , or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter.

METHODS

We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T₂ s, and a functional MRS experiment designed to estimate fractional change ( δ $$ \delta $$ ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T₂ in multi-echo and δ $$ \delta $$ in functional MRS.

RESULTS

When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible.

CONCLUSION

Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.

Measurement of glucose metabolism in the occipital lobe and frontal cortex after oral administration of [1-13C]glucose at 9.4 T

For the first time, labeling effects after oral intake of [1-13C]glucose are observed in the human brain with pure 1H detection at 9.4 T. Spectral time series were acquired using a short-TE 1H MRS MC-semiLASER (Metabolite Cycling semi Localization by Adiabatic SElective Refocusing) sequence in two voxels of 5.4 mL in the frontal cortex and the occipital lobe. High-quality time-courses of [4-13C]glutamate, [4-13C]glutamine, [3-13C]glutamate + glutamine, [2-13C] glutamate+glutamine and [3-13C]aspartate for individual volunteers and additionally, group-averaged time-courses of labeled and non-labeled brain glucose could be obtained. Using a one-compartment model, mean metabolic rates were calculated for each voxel position: The mean rate of the TCA-cycle (Vtca) value was determined to be 1.36 and 0.93 μmol min-1 g-1, the mean rate of glutamine synthesis (Vgln) was calculated to be 0.23 and 0.45 μmol min-1 g-1, the mean exchange rate between cytosolic amino acids and mitochondrial Krebs cycle intermediates (Vx) rate was found to be 0.57 and 1.21 μmol min-1 g-1 for the occipital lobe and the frontal cortex, respectively. These values were in agreement with previously reported data. Altogether, it can be shown that this most simple technique combining oral administration of [1-13C]Glc with pure 1H MRS acquisition is suitable to measure metabolic rates.

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.

Test-retest reproducibility of human brain multi-slice 1 H FID-MRSI data at 9.4T after optimization of lipid regularization, macromolecular model, and spline baseline stiffness

PURPOSE

This study analyzes the effects of retrospective lipid suppression, a simulated macromolecular prior knowledge and different spline baseline stiffness values on 9.4T multi-slice proton FID-MRSI data spanning the whole cerebrum of human brain and the reproducibility of respective metabolite ratio to total creatine (/tCr) maps for 10 brain metabolites.

METHODS

Measurements were performed twice on 5 volunteers using a short TR and TE FID MRSI 2D sequence at 9.4T. The effects of retrospective lipid L2-regularization, macromolecular spectrum and different LCModel baseline flexibilities on SNR, FWHM, fitting residual, Cramér-Rao lower bound, and metabolite ratio maps were investigated. Intra-subject, inter-session coefficient of variation and the test-retest reproducibility of the mean metabolite ratios (/tCr) of each slice was calculated.

RESULTS

Transversal, sagittal, and coronal slices of many metabolite ratio maps correspond to the anatomically expected concentration relations in gray and white matter for the majority of the cerebrum when using a flexible baseline in LCModel fit. Results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement. L2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected.

CONCLUSION

Reproducible concentration ratio maps (/tCr) for 4 metabolites (total choline, N-acetylaspartate, glutamate, and myoinositol) spanning the majority of the cerebrum and 6 metabolites (N-acetylaspartylglutamate, γ-aminobutyric acid, glutathione, taurine, glutamine, and aspartate) covering 32 mm in the upper part of the brain were acquired at 9.4T using multi-slice FID MRSI with retrospective lipid suppression, a macromolecular spectrum and a flexible LCModel baseline.

Quantitative T1-relaxation corrected metabolite mapping of 12 metabolites in the human brain at 9.4 T

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive imaging modality that enables observation of metabolites. Applications of MRSI for neuroimaging have shown promise for monitoring and detecting various diseases. This study builds off previously developed techniques of short TR, ¹H FID MRSI by correcting for T₁-weighting of the metabolites and utilizing an internal water reference to produce quantitative (mmol kg^-1) metabolite maps. This work reports and shows quantitative metabolite maps for 12 metabolites for a single slice. Voxel-specific T₁-corrections for water are common in MRSI studies; however, most studies use either averaged T₁-relaxation times to correct for T₁-weighting of metabolites or omit this correction step entirely. This work employs the use of voxel-specific T₁-corrections for metabolites in addition to water. Utilizing averaged T₁-relaxation times for metabolites can bias metabolite maps for metabolites that have strong differences between T₁-relaxation for GM and WM (i.e. Glu). This work systematically compares quantitative metabolite maps to single voxel quantitative results and qualitatively compares metabolite maps to previous works.

Multi-nuclear magnetic resonance spectroscopy: state of the art and future directions

With the development of heteronuclear fluorine, sodium, phosphorus, and other probes and imaging technologies as well as the optimization of magnetic resonance imaging (MRI) equipment and sequences, multi-nuclear magnetic resonance (multi-NMR) has enabled localize molecular activities in vivo that are central to a variety of diseases, including cardiovascular disease, neurodegenerative pathologies, metabolic diseases, kidney, and tumor, to shift from the traditional morphological imaging to the molecular imaging, precision diagnosis, and treatment mode. However, due to the low natural abundance and low gyromagnetic ratios, the clinical application of multi-NMR has been hampered. Several techniques have been developed to amplify the NMR sensitivity such as the dynamic nuclear polarization, spin-exchange optical pumping, and brute-force polarization. Meanwhile, a wide range of nuclei can be hyperpolarized, such as ²H, ³He, ¹³C, ¹⁵ N, ³¹P, and ¹²⁹Xe. The signal can be increased and allows real-time observation of biological perfusion, metabolite transport, and metabolic reactions in vivo, overcoming the disadvantages of conventional magnetic resonance of low sensitivity. HP-NMR imaging of different nuclear substrates provides a unique opportunity and invention to map the metabolic changes in various organs without invasive procedures. This review aims to focus on the recent applications of multi-NMR technology not only in a range of preliminary animal experiments but also in various disease spectrum in human. Furthermore, we will discuss the future challenges and opportunities of this multi-NMR from a clinical perspective, in the hope of truly bridging the gap between cutting-edge molecular biology and clinical applications.

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.

In vivo Human MR Spectroscopy Using a Clinical Scanner: Development, Applications, and Future Prospects

MR spectroscopy (MRS) is a unique and useful method for noninvasively evaluating biochemical metabolism in human organs and tissues, but its clinical dissemination has been slow and often limited to specialized institutions or hospitals with experts in MRS technology. The number of 3-T clinical MR scanners is now increasing, representing a major opportunity to promote the use of clinical MRS. In this review, we summarize the theoretical background and basic knowledge required to understand the results obtained with MRS and introduce the general consensus on the clinical utility of proton MRS in routine clinical practice. In addition, we present updates to the consensus guidelines on proton MRS published by the members of a working committee of the Japan Society of Magnetic Resonance in Medicine in 2013. Recent research into multinuclear MRS equipped in clinical MR scanners is explained with an eye toward future development. This article seeks to provide an overview of the current status of clinical MRS and to promote the understanding of when it can be useful. In the coming years, MRS-mediated biochemical evaluation is expected to become available for even routine clinical practice.

Spatially resolved free-induction decay spectroscopy using a 3D ultra-short echo time multi-echo imaging sequence with systematic echo shifting and compensation of B0 field drifts

PURPOSE

Biologically interesting signals can exhibit fast transverse relaxation and frequency shifts compared to free water. For spectral assignment, a ultra-short echo time (UTE) imaging sequence was modified to provide pixel-wise free-induction decay (FID) acquisition.

METHODS

The UTE-FID approach presented relies on a multi-echo 3D spiral UTE sequence with six echoes per radiofrequency (RF) excitation (TE_min 0.05 ms, echo spacing 3 ms). A complex pixel-wise raw data set for FID spectroscopy is obtained by several multi-echo UTE measurements with systematic shifting of the readout by 0.25 or 0.5 ms, until the time domain is filled for 18 or 45 ms. B₀ drifts are compensated by mapping and according phase correction. Autoregressive extrapolation of the signal is performed before Gaussian filtering. This method was applied to a phantom containing collagen-water solutions of different concentrations. To calculate the collagen content, a 19-peak collagen model was extracted from a non-selective FID spectrum (50% collagen solution). Proton-density-collagen-fraction (PDCF) was calculated for 10 collagen solutions (2%-50%). Furthermore, an in vivo UTE-FID spectrum of adipose tissue was recorded.

RESULTS

UTE-FID signal patterns agreed well with the non-spatially selective pulse-acquire FID spectrum from a sphere filled with 50% collagen. Differentiation of collagen solution from distilled water in the PDCF map was possible from 4% collagen concentration for a UTE-FID sequence with 128 × 128 × 64 matrix (voxel size 1 × 1 × 2.85 mm³ ). The mean values of the PDCF correlate linearly with collagen concentration.

CONCLUSION

The presented UTE-FID approach allows pixel-wise raw data acquisition similar to non-spatially selective pulse-acquire spectroscopy. Spatially resolved applications for assessment of spectra of rapidly decaying signals seem feasible.

The macromolecular MR spectrum does not change with healthy aging

PURPOSE

To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex.

METHODS

102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm³ , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes.

RESULTS

MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions.

CONCLUSION

Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.

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.

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.

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.

Are Cramér-Rao lower bounds an accurate estimate for standard deviations in in vivo magnetic resonance spectroscopy?

Due to inherent time constraints for in vivo experiments, it is infeasible to repeat multiple MRS scans to estimate standard deviations on the desired measured parameters. As such, the Cramér-Rao lower bounds (CRLBs) have become the routine method to approximate standard deviations for in vivo experiments. Cramér-Rao lower bounds, however, as the name suggests, are theoretically a lower bound on the standard deviation and it is not clear if and under what circumstances this approximation is valid. Realistic synthetic 3 T spectra were used to investigate the relationship between estimated CRLBs, true CRLBs and standard deviations. Here we demonstrate that, although the CRLBs are theoretically truly a lower bound on the standard deviation (not an equality) for the problem typically encountered in quantification, they are still an adequate approximation to standard deviation as long as the model perfectly characterizes the data. In the case when the macromolecule basis deviates from the measured macromolecules it was shown that the CRLBs can deviate from standard deviations by approximately 50% for N-acetylaspartic acid, creatine and glutamate and of the order of 100% or more for myo-inositol and γ-aminobutyric acid. In the case when the model perfectly reflects the data the CRLBs are within approximately 10% of standard deviations for all metabolites. The result of the CRLB being within 10% of standard deviations means that, for an accurate model, novel quantification methods such as machine learning or deep learning will not be able to obtain substantially more precise estimates for the desired parameters than traditional maximum-likelihood estimation.

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.

Spectral editing in 1 H magnetic resonance spectroscopy: Experts' consensus recommendations

Spectral editing in in vivo ¹ H-MRS provides an effective means to measure low-concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, for example, γ-aminobutyric acid, glutathione and D-2-hydroxyglutarate. Spectral editing strategies utilize known J-coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homonuclear editing techniques and considerations for data acquisition, processing and quantification. Also, we have listed the experts' recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted coedited resonances, spectral fitting of edited spectra, setting up multicenter clinical trials and recommending sequence parameters to be reported in publications.

An integrated RF-receive/B0-shim array coil boosts performance of whole-brain MR spectroscopic imaging at 7 T

Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B0 field. Here, we show that an integrated RF-receive/B0-shim (AC/DC) array coil can be used to mitigate 7 T B0 inhomogeneity, which improves spectral quality and metabolite quantification over a whole-brain slab. Our results from simulations, phantoms, healthy and brain tumor human subjects indicate improvements of global B0 homogeneity by 55%, narrower spectral linewidth by 29%, higher signal-to-noise ratio by 31%, more precise metabolite quantification by 22%, and an increase by 21% of the brain volume that can be reliably analyzed. AC/DC shimming provide the highest correlation (R2 = 0.98, P = 0.001) with ground-truth values for metabolite concentration. Clinical translation of AC/DC and MRSI is demonstrated in a patient with mutant-IDH1 glioma where it enables imaging of D-2-hydroxyglutarate oncometabolite with a 2.8-fold increase in contrast-to-noise ratio at higher resolution and more brain coverage compared to previous 7 T studies. Hence, AC/DC technology may help ultra-high field MRSI become more feasible to take advantage of higher signal/contrast-to-noise in clinical applications.

A semi-LASER, single-voxel spectroscopic sequence with a minimal echo time of 20.1 ms in the human brain at 3 T

An optimized semi-LASER sequence that is capable of acquiring artefact-free data with an echo time (TE) of 20.1 ms on a standard clinical 3 T MR system was developed. Simulations were performed to determine the optimal TEs that minimize the expected Cramér-Rao lower bound (CRLB) as proxy for quantification accuracy of metabolites. Optimized RF pulses, crusher gradients and phase cycling were used to achieve the shortest TE in a semi-LASER sequence to date on a clinical system. Synthetic spectra were simulated using the density matrix formalism for TEs spanning from 20.1 to 220.1 ms. These simulations were used to calculate the expected CRLB for each of the 18 metabolites typically considered in ¹ H MRS. High quality spectra were obtained in six healthy volunteers in the prefrontal cortex, which is known for spurious echoes due to its proximity to the paranasal sinuses, and in the parietal-occipital cortex. Spectral transients were sufficient in quality to enable phase and frequency alignment prior to summation over all repetitions. Automated high-quality water suppression was obtained for all voxels without manual adjustment. The shortest TE minimized the CRLB for all brain metabolites except glycine due to its overlap with myo-inositol at this TE. It is also demonstrated that the CRLBs increase rapidly with TE for certain coupled metabolites.

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 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 ( T 2 app ) 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. T 2 app 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

T 2 app of downfield metabolites are reported within a range from 16 to 32 ms except for homocarnosine with T 2 app of 50 ms. Correcting T 2 app for exchange rates ( T 2 c o r r ) 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 T 2 app contribution led to possible peak assignments.

CONCLUSION

T 2 app 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.