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Proton MRS (1 H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi-adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0 ) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

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, randomized, placebo-controlled crossover trial, we investigated the impact of a 4-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 4-week phenylalanine and placebo interventions (four time points). Structural T1-weighted images were preprocessed and evaluated using Direct Cortical Thickness Estimation using Deep Learning-based Anatomy Segmentation and Cortex Parcellation (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 time points, along with performance in executive functions and attention. Blood phenylalanine levels were significantly higher after the phenylalanine period (1441 µmol/l) than after the placebo period (873 µmol/l, P < 0.001). Morphometric analyses revealed a statistically significant decrease in cortical thickness in 17 of 60 brain regions after the phenylalanine period compared to placebo. The largest decreases were observed in the right pars orbitalis (point estimate = −0.095 mm, P < 0.001) and the left lingual gyrus (point estimate = −0.070 mm, P < 0.001). Bilateral white matter and ventricular volumes were significantly increased after the phenylalanine period. However, the structural alterations in the phenylalanine-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 false discovery rate (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 4-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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims: Roux‐en‐Y gastric bypass (RYGB) surgery alters postprandial glucose profiles, causing post‐bariatric hypoglycaemia (PBH) in some individuals. Due to the liver's central role in glucose homeostasis, hepatic glucose handling might differ in RYGB‐operated patients with PBH compared to non‐operated healthy controls (HC). Materials and Methods: We enrolled RYGB‐operated adults with PBH and HCs (n = 10 each). Participants ingested 60 g of [6,6′‐2H2]‐glucose (d‐glucose) after an overnight fast. Deuterium metabolic imaging (DMI) with interleaved 13C magnetic resonance spectroscopy was performed before and until 150 min post‐d‐glucose intake, with frequent blood sampling to quantify glucose enrichment and gluco‐regulatory hormones until 180 min. Glucose fluxes were assessed by mathematical modelling. Outcome trajectories were described using generalized additive models. Results: In RYGB subjects, the hepatic d‐glucose signal increased early, followed by a decrease, whereas HCs exhibited a gradual increase and consecutive stabilization. Postprandial hepatic glycogen accumulation and the suppression of endogenous glucose production were lower in RYGB patients than in HCs, despite higher insulin exposure, indicating lower hepatic insulin sensitivity. The systemic rate of ingested d‐glucose was faster in RYGB, leading to a higher, earlier plasma glucose peak and increased insulin secretion. Postprandial glucose disposal increased in RYGB patients, without between‐group differences in peripheral insulin sensitivity. Conclusions: Exploiting DMI with stable isotope flux analysis, we observed distinct postprandial hepatic glucose trajectories and parameters of glucose–insulin homeostasis in RYGB patients with PBH versus HCs. Despite altered postprandial glucose kinetics and higher insulin exposure, there was no evidence of impaired hepatic glucose uptake or output predisposing to PBH in RYGB patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A large body of published work shows that proton (hydrogen 1 [(1)H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of (1)H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of (1)H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which (1)H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units.

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. [ABSTRACT FROM AUTHOR]

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 T2s and concentrations, with improvements ranging from 5% to 30% for T2s 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]