Transient brain structure changes after high phenylalanine exposure in adults with phenylketonuria.

Phenylketonuria is a rare metabolic disease resulting from a deficiency of the enzyme phenylalanine hydroxylase. Recent cross-sectional evidence suggests that early-treated adults with phenylketonuria exhibit alterations in cortical grey matter compared to healthy peers. However, the effects of high phenylalanine exposure on brain structure in adulthood need to be further elucidated. In this double-blind, randomised, placebo-controlled crossover trial, we investigated the impact of a four-week high phenylalanine exposure on the brain structure and its relationship to cognitive performance and metabolic parameters in early-treated adults with phenylketonuria. Twenty-eight adult patients with early-treated classical phenylketonuria (19-48 years) underwent magnetic resonance imaging before and after the four-week phenylalanine and placebo interventions (four timepoints). Structural T1-weighted images were preprocessed and evaluated using DL+DiReCT, a deep-learning-based tool for brain morphometric analysis. Cortical thickness, white matter volume, and ventricular volume were compared between the phenylalanine and placebo periods. Brain phenylalanine levels were measured using 1H spectroscopy. Blood levels of phenylalanine, tyrosine, and tryptophan were assessed at each of the four timepoints, along with performance in executive functions and attention. Blood phenylalanine levels were significantly higher after the phenylalanine period (1441µmol/L) than after the placebo period (873µmol/L, P<0.001). Morphometric analyses revealed a statistically significant decrease in cortical thickness in 17 out of 60 brain regions after the phenylalanine period compared to placebo. The largest decreases were observed in the right pars orbitalis (point estimate=-0.095mm, P<0.001) and the left lingual gyrus (point estimate=-0.070mm, P<0.001). Bilateral white matter and ventricular volumes were significantly increased after the phenylalanine period. However, the structural alterations in the Phe-placebo group returned to baseline measures following the washout and placebo period. Additionally, elevated blood and brain phenylalanine levels were related to increased bilateral white matter volume (rs=0.43 to 0.51, P≤0.036) and decreased cortical thickness (rs=-0.62 to -0.39, not surviving FDR correction) after the phenylalanine and placebo periods. Moreover, decreased cortical thickness was correlated with worse cognitive performance after both periods (rs=-0.54 to -0.40, not surviving FDR correction). These findings provide evidence that a four-week high phenylalanine exposure in adults with phenylketonuria results in transient reductions of the cortical grey matter and increases in white matter volume. Further research is needed to determine the potential long-term impact of high phenylalanine levels on brain structure and function in adults with phenylketonuria.

Diffusion-weighted MR spectroscopy of the prostate.

PURPOSE: Prostate tissue has a complex microstructure, mainly composed of epithelial and stromal cells, and of extracellular (acinar-luminal) spaces. Diffusion-weighted MR spectroscopy (DW-MRS) is ideally suited to explore complex microstructure in vivo with metabolites selectively distributed in different subspaces. To date, this technique has been applied to brain and muscle. This study presents the development and pioneering utilization of 1H-DW-MRS in the prostate, accompanied by in vitro studies to support interpretations of in vivo findings. METHODS: Nine healthy volunteers underwent a prostate MR examination (mean age, 56 years; range, 31-66). Metabolic complexation was studied in vitro using solutions with major compounds found in prostatic fluid of the lumen. DW-MRS was performed at 3 T with a non-water-suppressed single-voxel sequence with metabolite-cycling to concurrently measure metabolite and water signals. The water signal was used in postprocessing as a reference in a motion-compensation scheme. The spectra were fitted simultaneously in the spectral and diffusion-weighting dimensions. Apparent diffusion coefficients (ADCs) were derived by fitting signal decays that were assumed to be mono-exponential for metabolites and biexponential for water. RESULTS: DW-MRS of the prostate revealed relatively low ADCs for Cho and Cr compounds, aligning with their intracellular location and higher ADCs for citrate and spermine supporting their luminal origin. In vitro assessments of the ADCs of citrate and spermine demonstrated their complex formation and protein binding. Tissue concentrations of MRS-detectable metabolites were as expected for the voxel location. CONCLUSIONS: This work successfully demonstrates the feasibility of 1H-DW-MRS of the prostate and its potential for providing valuable microstructural information.

Getting the phase consistent: The importance of phase description in balanced steady-state free precession MRI of multi-compartment systems.

PURPOSE: Determine the correct mathematical phase description for balanced steady-state free precession (bSSFP) signals in multi-compartment systems. THEORY AND METHODS: Based on published bSSFP signal models, different phase descriptions can be formulated: one predicting the presence and the other predicting the absence of destructive interference effects in multi-compartment systems. Numerical simulations of bSSFP signals of water and acetone were performed to evaluate the predictions of these different phase descriptions. For experimental validation, bSSFP profiles were measured at 3T using phase-cycled bSSFP acquisitions performed in a phantom containing mixtures of water and acetone, which replicates a system with two signal components. Localized single voxel MRS was performed at 7T to determine the relative chemical shift of the acetone-water mixtures. RESULTS: Based on the choice of phase description, the simulated bSSFP profiles of water-acetone mixtures varied significantly, either displaying or lacking destructive interference effects, as predicted theoretically. In phantom experiments, destructive interference was consistently observed in the measured bSSFP profiles of water-acetone mixtures, supporting the theoretical description that predicts such interference effects. The connection between the choice of phase description and predicted observation enables unambiguous experimental identification of the correct phase description for multi-compartment bSSFP profiles, which is consistent with the Bloch equations. CONCLUSION: The study emphasizes that consistent phase descriptions are crucial for accurately describing multi-compartment bSSFP signals, as incorrect phase descriptions result in erroneous predictions.

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling.

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

Interleaved trinuclear MRS for single-session investigation of carbohydrate and lipid metabolism in human liver at 7T.

The liver plays a central role in metabolic homeostasis, as exemplified by a variety of clinical disorders with hepatic and systemic metabolic disarrays. Of particular interest are the complex interactions between lipid and carbohydrate metabolism in highly prevalent conditions such as obesity, diabetes, and fatty liver disease. Limited accessibility and the need for invasive procedures challenge direct investigations in humans. Hence, noninvasive dynamic evaluations of glycolytic flux and steady-state assessments of lipid levels and composition are crucial for basic understanding and may open new avenues toward novel therapeutic targets. Here, three different MR spectroscopy (MRS) techniques that have been combined in a single interleaved examination in a 7T MR scanner are evaluated. 1H-MRS and 13C-MRS probe endogenous metabolites, while deuterium metabolic imaging (DMI) relies on administration of deuterated tracers, currently 2H-labelled glucose, to map the spatial and temporal evolution of their metabolic fate. All three techniques have been optimized for a robust single-session clinical investigation and applied in a preliminary study of healthy subjects. The use of a triple-channel 1H/2H/13C RF coil enables interleaved examinations with no need for repositioning. Short-echo-time STEAM spectroscopy provides well resolved spectra to quantify lipid content and composition. The relative benefits of using water saturation versus metabolite cycling and types of respiratory synchronization were evaluated. 2H-MR spectroscopic imaging allowed for registration of time- and space-resolved glucose levels following oral ingestion of 2H-glucose, while natural abundance 13C-MRS of glycogen provides a dynamic measure of hepatic glucose storage. For DMI and 13C-MRS, the measurement precision of the method was estimated to be about 0.2 and about 16 mM, respectively, for 5 min scanning periods. Excellent results were shown for the determination of dynamic uptake of glucose with DMI and lipid profiles with 1H-MRS, while the determination of changes in glycogen levels by 13C-MRS is also feasible but somewhat more limited by signal-to-noise ratio.

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.

PURPOSE: The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution. METHODS: Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF). RESULTS: Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value. CONCLUSION: MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF.

Denoising single MR spectra by deep learning: Miracle or mirage?

PURPOSE: The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate uncertainties or whether it essentially reduces noise in signal-free areas only. METHODS: Noise removal based on supervised DL with U-nets was implemented using simulated 1 H MR spectra of human brain in two approaches: (1) via time-frequency domain spectrograms and (2) using 1D spectra as input. Quality of denoising was evaluated in three ways: (1) by an adapted fit quality score, (2) by traditional model fitting, and (3) by quantification via neural networks. RESULTS: Visually appealing spectra were obtained; hinting that denoising is well-suited for MRS. However, an adapted denoising score showed that noise removal is inhomogeneous and more efficient for signal-free areas. This was confirmed by quantitative analysis of traditional fit results as well as DL quantitation following DL denoising. DL denoising, although apparently successful as judged by mean squared errors, led to substantially biased estimates in both implementations. CONCLUSION: The implemented DL-based denoising techniques may be useful for display purposes, but do not help quantitative evaluations, confirming expectations based on estimation theory: Cramér Rao lower bounds defined by the original data and the appropriate fitting model cannot be circumvented in an unbiased way for single data sets, unless additional prior knowledge can be incurred in the form of parameter restrictions/relations or applicable substates.

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.

MP-PCA denoising for diffusion MRS data: promises and pitfalls.

Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.

Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM.

PURPOSE: Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness. METHODS: Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with. RESULTS: Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR. CONCLUSIONS: Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.

Quantification of NAD+ in human brain with 1 H MR spectroscopy at 3 T: Comparison of three localization techniques with different handling of water magnetization.

PURPOSE: The detection of nicotinamide-adenine-dinucleotide (NAD+ ) is challenging using standard 1 H MR spectroscopy, because it is of low concentration and affected by polarization-exchange with water. Therefore, this study compares three techniques to access NAD+ quantification at 3 T-one with and two without water presaturation. METHODS: A large brain volume in 10 healthy subjects was investigated with three techniques: semi-LASER with water-saturation (WS) (TE = 35 ms), semi-LASER with metabolite-cycling (MC) (TE = 35 ms), and the non-water-excitation (nWE) technique 2D ISIS-localization with chemical-shift-selective excitation (2D I-CSE) (TE = 10.2 ms). Spectra were quantified with optimized modeling in FiTAID. RESULTS: NAD+ could be well quantified in cohort-average spectra with all techniques. Obtained apparent NAD+ tissue contents are all lower than expected from literature confirming restricted visibility by 1 H MRS. The estimated value from WS-MRS (58 µM) was considerably lower than those obtained with non-WS techniques (146 µM for MC-semi-LASER and 125 µM for 2D I-CSE). The nWE technique with shortest TE gave largest NAD+ signals but suffered from overlap with large amide signals. MC-semi-LASER yielded best estimation precision as reflected in relative Cramer-Rao bounds (14%, 21 µM/146 µM) and also best robustness as judged by the coefficient-of-variance over the cohort (11%, 10 µM/146 µM). The MR-visibility turned out as 16% with WS and 41% with MC. CONCLUSION: Three methods to assess NAD+ in human brain at 3 T have been compared. NAD+ could be detected with a visibility of ∼41% for the MC method. This may open a new window for the observation of pathological changes in the clinical research setting.

Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting.

PURPOSE: Definition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non-Gaussian diffusion of brain metabolites with strongly diffusion-weighted MR spectroscopy. METHODS: Short echo time MRS with strong diffusion-weighting with b-values up to 25 ms/µm2 at two diffusion times was implemented on a Connectom system and applied in combination with simultaneous spectral and diffusion decay modeling. Motion-compensation was performed with a combined method based on the simultaneously acquired water and a macromolecular signal. RESULTS: The motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non-Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum. CONCLUSION: The determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure.

Combining chemical exchange saturation transfer and 1 H magnetic resonance spectroscopy for simultaneous determination of metabolite concentrations and effects of magnetization exchange.

PURPOSE: A new sequence combining chemical-exchange saturation-transfer (CEST) with traditional MRS is used to simultaneously determine metabolite content and effects of magnetization exchange. METHODS: A CEST saturation block consisting of a train of RF-pulses is placed before a metabolite-cycled semi-LASER single-voxel spectroscopy sequence. The saturation parameters are adjustable to allow optimization of the saturation for a specific target. Data were collected in brain from 20 subjects in experiments with different B1 -settings (0.4-2.0 µT) on a 3T MR scanner. CEST Z-spectra were calculated from water intensities and fitted with a multi-pool Lorentzian model. Interrelated metabolite spectra were fitted in fitting tool for arrays of interrelated datasets (FiTAID). RESULTS: Evaluation of traditional Z-spectra from water revealed exchange effects from amides, amines, and hydroxyls as well as an upfield nuclear Overhauser effect. The magnetization transfer effect was evaluated on metabolites and macromolecules for the whole spectral range and for the different B1 levels. A correction scheme for direct saturation on metabolites is proposed. Both magnetization-transfer and direct saturation proved to differ for individual metabolites. CONCLUSION: Using non-water-suppressed spectroscopy offers time-saving simultaneous recording of the traditional CEST Z-spectrum from water and the metabolite spectrum under frequency-selective saturation. In addition, exchange and magnetization-transfer effects on metabolites and macromolecules can be detected, which might offer additional possibilities for quantification or give further insight into the composition of the traditional CEST Z-spectrum. Apparent magnetization-transfer effects on macromolecular signals in the 1 H-MR spectrum have been found. Detailed knowledge of magnetization-transfer effects is also relevant for judging the influence of water-suppression on the quantification of metabolite signals.

Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations.

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.

Water and lipid suppression techniques for advanced 1 H MRS and MRSI of the human brain: Experts' consensus recommendations.

The neurochemical information provided by proton magnetic resonance spectroscopy (MRS) or MR spectroscopic imaging (MRSI) can be severely compromised if strong signals originating from brain water and extracranial lipids are not properly suppressed. The authors of this paper present an overview of advanced water/lipid-suppression techniques and describe their advantages and disadvantages. Moreover, they provide recommendations for choosing the most appropriate techniques for proper use. Methods of water signal handling are primarily focused on the VAPOR technique and on MRS without water suppression (metabolite cycling). The section on lipid-suppression methods in MRSI is divided into three parts. First, lipid-suppression techniques that can be implemented on most clinical MR scanners (volume preselection, outer-volume suppression, selective lipid suppression) are described. Second, lipid-suppression techniques utilizing the combination of k-space filtering, high spatial resolutions and lipid regularization are presented. Finally, three promising new lipid-suppression techniques, which require special hardware (a multi-channel transmit system for dynamic B1+ shimming, a dedicated second-order gradient system or an outer volume crusher coil) are introduced.

Proton magnetic resonance spectroscopy in skeletal muscle: Experts' consensus recommendations.

1 H-MR spectroscopy of skeletal muscle provides insight into metabolism that is not available noninvasively by other methods. The recommendations given in this article are intended to guide those who have basic experience in general MRS to the special application of 1 H-MRS in skeletal muscle. The highly organized structure of skeletal muscle leads to effects that change spectral features far beyond simple peak heights, depending on the type and orientation of the muscle. Specific recommendations are given for the acquisition of three particular metabolites (intramyocellular lipids, carnosine and acetylcarnitine) and for preconditioning of experiments and instructions to study volunteers.

Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts' consensus recommendations.

The translation of MRS to clinical practice has been impeded by the lack of technical standardization. There are multiple methods of acquisition, post-processing, and analysis whose details greatly impact the interpretation of the results. These details are often not fully reported, making it difficult to assess MRS studies on a standardized basis. This hampers the reviewing of manuscripts, limits the reproducibility of study results, and complicates meta-analysis of the literature. In this paper a consensus group of MRS experts provides minimum guidelines for the reporting of MRS methods and results, including the standardized description of MRS hardware, data acquisition, analysis, and quality assessment. This consensus statement describes each of these requirements in detail and includes a checklist to assist authors and journal reviewers and to provide a practical way for journal editors to ensure that MRS studies are reported in full.

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.

Non-water-excitation MR spectroscopy techniques to explore exchanging protons in human brain at 3 T.

PURPOSE: To develop localization sequences for in vivo MR spectroscopy (MRS) on clinical scanners of 3 T to record spectra that are not influenced by magnetization transfer from water. METHODS: Image-selected in vivo spectroscopy (ISIS) localization and chemical-shift-selective excitation (termed I-CSE) was combined in two ways: first, full ISIS localization plus a frequency-selective spin-echo and second, two-dimensional (2D) ISIS plus a frequency-selective excitation and slice-selective refocusing. The techniques were evaluated at 3 T in phantoms and human subjects in comparison to standard techniques with water presaturation or metabolite-cycling. ISIS included gradient-modulated offset-independent adiabatic (GOIA)-type adiabatic inversion pulses; echo times were 8-10 ms. RESULTS: The novel 2D and 3D I-CSE methods yield upfield spectra that are comparable to those from standard MRS, except for shorter echo times and a limited frequency range. On the downfield/high-frequency side, they yield much more signal for exchangeable protons when compared to MRS with water presaturation or metabolite-cycling and longer echo times. CONCLUSION: Novel non-water-excitation MRS sequences offer substantial benefits for the detection of metabolite signals that are otherwise suppressed by saturation transfer from water. Avoiding water saturation and using very short echo times allows direct observation of faster exchanging moieties than was previously possible at 3 T and additionally makes the methods less susceptible to fast T2 relaxation.

Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling.

Macromolecular signals are crucial constituents of short echo-time 1 H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion-recovery (2DJ-IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono-exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three-dimensional spectra acquired with metabolite-cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono-exponential decay model appears to be inadequate to represent TE-dependent signal features of the MMBG. TE-adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ-IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér-Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.