Proton MRS (1H 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.

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. Abbreviations: 1 H, proton; 13 C, carbon-13; B 0 , main magnetic field; B 1 , RF field; Cr, creatine; CRMVB, Cramér-Rao minimum variance bound; CSF, cerebrospinal fluid; d GM , water density of grey matter; d WM , water density of white matter; ERETIC, Electric Reference to Access in vivo Concentrations; f CSF , volume fraction of cerebrospinal fluid inside the MRS voxel; fCSF H2O , water mole fraction in cerebrospinal fluid; fGM, volume fraction of gray matter inside the MRS voxel; fGM H2O , water mole fraction in gray matter; FFT, fast Fourier transform; FID, free induction decay; FQN, fit quality number; FWHM, full width at half maximum; f WM , volume fraction of white matter inside the MRS voxel; fWM H2O , water mole fraction in white matter; GM, grey matter; GPC, glycerophosphocholine; [H 2 O] molal , water concentration in moles of water per kilogram of tissue water = 55.49 moles/kg; [H 2 O] molar , water concentration in moles of water per liter of tissue water; HERMES, Hadamard encoding and reconstruction of MEGA-edited spectroscopy; MEGA-PRESS, Mescher-Garwood point resolved spectroscopy; [M] GM /[M] WM , assumed ratio of grey matter to white matter metabolite concentrations; MM, macromolecules; [M]molal, metabolite concentration in moles of metabolite per kilogram of tissue water; [M]molar, metabolite concentration in moles of metabolite per liter of tissue water; MRSI, magnetic resonance spectroscopic imaging; NAA, N-acetylaspartate; NAAG, N-acetylaspartylglutamate; N M , number of protons contributing to metabolite signal; N P , number of points in FID/spectrum; N pc , number of phase encoding steps in one phase cycle; N RF , number of RF channels; N tra , number of transients;PCh, phosphocholine; PCr, phosphocreatine; RH2O CSF , relaxation scaling factor for water in cerebrospinal fluid; RH2O GM , relaxation scaling factor for water in grey matter; RH2O WM , relaxation scaling factor for water in white matter; RM, relaxation scaling factor for tissue metabolite signal; RM GM , relaxation scaling factor for metabolite in grey matter; RM WM , relaxation scaling factor for metabolite in white matter; S H2O , water signal intensity; SH2O obs , observed water signal intensity in the presence of relaxation; S M , metabolite signal intensity; SM obs , observed metabolite signal intensity in the presence of relaxation; SNR, signal-to-noise r...

Highlights d NR supplementation in aged subjects augments the skeletal muscle NAD + metabolome d NR supplementation does not affect skeletal muscle mitochondrial bioenergetics d NR supplementation reduces levels of circulating inflammatory cytokines

Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo 1 H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. computationally efficient (HLSVD; Ref. 4) for in vivo data, and are effective at extracting peak parameters from simple spectra. One drawback of black-box methods is that additional knowledge of spectral features cannot be incorporated into the algorithm allowing infeasible results to be possible for more complex data. For example, an incorrect ratio between peaks originating from the same molecule is possible. The AMARES (5) algorithm was developed to address this issue by extending the VARPRO (6) peak-fitting method to allow a greater level of prior knowledge to be incorporated into the fitting model.Black-box and peak-fitting methods have been shown to be highly effective for sparse spectra such as long echo time (TE) 1 H or 31 P MRS; however, the complex patterns of some metabolites seen in short TE 1 H MRS data are cumbersome to model as a series of single peaks. Although long TE 1 H MRS is still popular, there is a growing trend to shorter TE (7) because of the increase in metabolic information. Therefore, analysis methods that are suited to this data type are becoming increasingly important. For complex data, methods that incorporate a metabolite basis set have been shown to be more effective than peak-fitting methods (8).LCModel™ (9) was one of the first algorithms to incorporate a metabolite basis set into the fitting model and is widely used for the analysis of short TE 1 H MRS data. The algorithm models data in the frequency domain using a linear combination of metabolite, lipid, and macromolecule signals combined with a smoothing splines to account for baseline signals. More recently, the Quantitation Based on Quantum Estimation (QUEST) (10) algorithm has been developed that uses a combination of time-domain fitting and HSVD to model background signals. An alternative approach is taken by Automated Quantitation of Short Echo time MRS Spectra (AQSES) (11) that uses a combination of time-domain fitting and penalized splines to model the baseline. AQSES also differs from LCModel™ and QUEST as it uses the variable projection method to estimate the amplitudes of the metabolite basis set resulting in a reduc...

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.

The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences.An ice–water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters.ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately.ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials. © 2015 The Authors NMR in Biomedicine Published by John Wiley & Sons Ltd.

1 H MRS has great potential for the clinical investigation of childhood brain tumours, but the low incidence in, and difficulties of performing trials on, children have hampered progress in this area. Most studies have used a long-TE, thus limiting the metabolite information obtained, and multivariate analysis has been largely unexplored. Thirty-five children with untreated cerebellar tumours (18 medulloblastomas, 12 pilocytic astrocytomas and five ependymomas) were investigated using a single-voxel short-TE PRESS sequence on a 1.5 T scanner. Spectra were analysed using LCModel TM to yield metabolite profiles, and key metabolite assignments were verified by comparison with high-resolution magic-angle-spinning NMR of representative tumour biopsy samples. In addition to univariate metabolite comparisons, the use of multivariate classifiers was investigated. Principal component analysis was used for dimension reduction, and linear discriminant analysis was used for variable selection and classification. A bootstrap cross-validation method suitable for estimating the true performance of classifiers in small datasets was used. The discriminant function coefficients were stable and showed that medulloblastomas were characterised by high taurine, phosphocholine and glutamate and low glutamine, astrocytomas were distinguished by low creatine and high N-acetylaspartate, and ependymomas were differentiated by high myo-inositol and glycerophosphocholine. The same metabolite features were seen in NMR spectra of ex vivo samples. Successful classification was achieved for glial-cell (astrocytoma þ ependymoma) versus non-glial-cell (medulloblastoma) tumours, with a bootstrap 0.632 þ error, e B.632þ , of 5.3%. For astrocytoma vs medulloblastoma and astrocytoma vs medulloblastoma vs ependymoma classification, the e B.632þ was 6.9% and 7.1%, respectively. The study showed that 1 H MRS detects key differences in the metabolite profiles for the main types of childhood cerebellar tumours and that discriminant analysis of metabolite profiles is a promising tool for classification. The findings warrant confirmation by larger multi-centre studies.

With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest