Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification.

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.

Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

Measurement of brain glutathione with magnetic Resonance spectroscopy in Schizophrenia-Spectrum disorders - A systematic review and Meta-Analysis.

Oxidative stress may contribute to declining course and poor outcomes in psychosis. However, in vivo Magnetic Resonance Spectroscopy studies yield disparate results due to clinical stage, sample demographics, neuroanatomical focus, sample size, and acquisition method variations. We investigated glutathione in brain regions from participants with psychosis, and the relation of glutathione to clinical features and spectroscopy protocols. Meta-analysis comprised 21 studies. Glutathione levels did not differ between total psychosis patients (N = 639) and controls (N = 704) in the Medial Prefrontal region (k = 21, d = -0.09, CI = -0.28 to 0.10, p = 0.37). Patients with stable schizophrenia exhibited a small but significant glutathione reduction compared to controls (k = 14, d = -0.20, CI = -0.40 to -0.00, p = 0.05). Meta-regression showed older studies had greater glutathione reductions, possibly reflecting greater accuracy related to spectroscopy advancements in more recent studies. No significant effects of methodological variables, such as voxel size or echo time were found. Reduced glutathione in patients with stable established schizophrenia may provide novel targets for precision medicine. Standardizing MRS acquisition methods in future studies may help address discrepancies in glutathione levels.

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits.

BACKGROUND: 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration- which metabolites are included in the basis set used for analysis- would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. METHODS: To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. RESULTS: Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. CONCLUSIONS: Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting.

SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data.

1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals - in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

Addressing the Challenge of Polypharmacy.

Polypharmacy describes the concomitant use of multiple medicines and represents a growing global challenge attributable to aging populations with an increasing prevalence of multimorbidity. Polypharmacy can be appropriate but is problematic when the increased risk of harm from interactions between drugs or between drugs and diseases or the burden of administering and monitoring medicines outweighs plausible benefits. Polypharmacy has a substantial economic impact in service demand and hospitalization as well as a detrimental impact on patients' quality of life. Apart from causing avoidable harm, polypharmacy can also lead to therapeutic failure, with up to 50% of patients who take four or more medications not taking them as prescribed. Guidance is needed to support patients and clinicians in defining and achieving realistic goals of drug treatment, and system change is necessary to aid implementation. This article outlines lessons from two programs that aim to address these challenges: the Scottish polypharmacy guidance on realistic prescribing and the European Union SIMPATHY project.

Outpatient neurology diagnostic coding: a proposed scheme for standardised implementation.

Clinical coding uses a classification system to assign standard codes to clinical terms and so facilitates good clinical practice through audit, service design and research. However, despite clinical coding being mandatory for inpatient activity, this is often not so for outpatient services, where most neurological care is delivered. Recent reports by the UK National Neurosciences Advisory Group and NHS England's 'Getting It Right First Time' initiative recommend implementing outpatient coding. The UK currently has no standardised system for outpatient neurology diagnostic coding. However, most new attendances at general neurology clinics appear to be classifiable with a limited number of diagnostic terms. We present the rationale for diagnostic coding and its benefits, and the need for clinical engagement to develop a system that is pragmatic, quick and easy to use. We outline a scheme developed in the UK that could be used elsewhere.

Evidence for distinct neuro-metabolic phenotypes in humans.

Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with 1H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.

NIfTI-MRS: A standard data format for magnetic resonance spectroscopy.

PURPOSE: Multiple data formats in the MRS community currently hinder data sharing and integration. NIfTI-MRS is proposed as a standard spectroscopy data format, implemented as an extension to the Neuroimaging informatics technology initiative (NIfTI) format. This standardized format can facilitate data sharing and algorithm development as well as ease integration of MRS analysis alongside other imaging modalities. METHODS: A file format using the NIfTI header extension framework incorporates essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to convert single-voxel and spectroscopic imaging data to NIfTI-MRS. Visualization of data in NIfTI-MRS is provided by development of a dedicated plugin for FSLeyes, the FMRIB Software Library (FSL) image viewer. RESULTS: Online documentation and 10 example datasets in the proposed format are provided. Code examples of NIfTI-MRS readers are implemented in common programming languages. Conversion software, spec2nii, currently converts 14 formats where data is stored in image-space to NIfTI-MRS, including Digital Imaging and Communications in Medicine (DICOM) and vendor proprietary formats. CONCLUSION: NIfTI-MRS aims to solve issues arising from multiple data formats being used in the MRS community. Through a single conversion point, processing and analysis of MRS data are simplified, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. Greater standardization and harmonization become possible. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, for visualization.

Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy.

Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.

Metabolite selection for machine learning in childhood brain tumour classification.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC=0.81±0.01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC=0.78±0.01 ) and total creatine (P < 0.05, AUC=0.77±0.01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC=0.79±0.01 ), total N-acetylaspartate (P < 0.05, AUC=0.79±0.01 ) and total choline (P < 0.05, AUC=0.75±0.01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Adaptive baseline fitting for 1H MR spectroscopy analysis.

PURPOSE: Accurate baseline modeling is essential for reliable MRS analysis and interpretation-particularly at short echo-times, where enhanced metabolite information coincides with elevated baseline interference. The degree of baseline smoothness is a key analysis parameter for metabolite estimation, and in this study, a new method is presented to estimate its optimal value. METHODS: An adaptive baseline fitting algorithm (ABfit) is described, incorporating a spline basis into a frequency-domain analysis model, with a penalty parameter to enforce baseline smoothness. A series of candidate analyses are performed over a range of smoothness penalties, as part of a 4-stage algorithm, and the Akaike information criterion is used to estimate the appropriate penalty. ABfit is applied to a set of simulated spectra with differing baseline features and experimentally acquired 2D MRSI-both at a field strength of 3 Tesla. RESULTS: Simulated analyses demonstrate metabolite errors result from 2 main sources: bias from an inflexible baseline (underfitting) and increased variance from an overly flexible baseline (overfitting). In the case of an ideal flat baseline, ABfit is shown to correctly estimate a highly rigid baseline, and for more realistic spectra a reasonable compromise between bias and variance is found. Analysis of experimentally acquired data demonstrates good agreement with known correlations between metabolite ratios and the contributing volumes of gray and white matter tissue. CONCLUSIONS: ABfit has been shown to perform accurate baseline estimation and is suitable for fully automated routine MRS analysis.

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.

In vivo Glx and Glu measurements from GABA-edited MRS at 3 T.

In vivo quantification of glutamate (Glu) and γ-aminobutyric acid (GABA) using MRS is often achieved using two separate sequences: a short-echo point resolved spectroscopy (PRESS) acquisition for Glu and a Mescher-Garwood PRESS (MEGA-PRESS) acquisition for GABA. The purpose of this study was to examine the agreement of Glu and Glx (the combined signal of glutamate + glutamine) quantified from two different GABA-edited MEGA-PRESS acquisitions (GABA plus macromolecules, GABA+, TE = 68 ms, and macromolecule suppressed, MMSup, TE = 80 ms) with Glu and Glx quantified from a short-echo PRESS (PRESS-35, TE = 35 ms) acquisition. Fifteen healthy male volunteers underwent a single scan session, in which data were acquired using the three acquisitions (GABA+, MMSup and PRESS-35) in both the sensorimotor and anterior cingulate cortices using a voxel size of 3 × 3 × 3 cm3 . Glx and Glu were quantified from the MEGA-PRESS data using both the OFF sub-spectra and the difference (DIFF) spectra. Agreement was assessed using correlation analyses, Bland-Altman plots and intraclass correlation coefficients. Glx quantified from the OFF sub-spectra from both the GABA+ and MMSup acquisitions showed poor agreement with PRESS-35 in both brain regions. In the sensorimotor cortex, Glu quantified from the OFF sub-spectra of GABA+ showed moderate agreement with PRESS-35 data, but this finding was not replicated in the anterior cingulate cortex. Glx and Glu quantified using the DIFF spectra of either MEGA-PRESS sequence were in poor agreement with the PRESS-35 data in both brain regions. In conclusion, Glx and Glu measured from MEGA-PRESS data generally showed poor agreement with Glx and Glu measured using PRESS-35.

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.

Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations.

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 specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.

A cross-sectional survey of the perspectives of older people in the Scottish Highlands on the management of their chronic pain.

BACKGROUND: Although there is evidence of suboptimal outcomes in older people with chronic pain, little emphasis has been placed on those in remote and rural settings. OBJECTIVE: To describe the perspectives of older people in the Scottish Highlands on their chronic pain management. DESIGN: Cross-sectional survey. SETTING: NHS Highland, the most remote and rural geographical health board in Scotland. SUBJECTS: Home-dwelling members of the public aged ≥70 years. METHODS: Anonymised questionnaires were mailed to a random sample of 1800 older people. Questionnaire items were demographics, nature of any chronic pain, management regimens and perceived effectiveness. Validated scales were the Pain Disability Questionnaire and the Tampa Scale for Kinesiophobia. RESULTS: Adjusted response rate was 39.3% (709/1755). One-quarter (25.0%, n = 177) were experiencing chronic pain, being more likely to live in deprived areas (P < 0.05). Median pain intensity was 6 (IQR 4-7, 10 high), causing distress (median 5, IQR 3-7). Respondents largely consulted GPs (66.1%, n = 117) with a minority (16.4%, n = 29) referred to a specialist pain clinic and few consulting other health professionals. Over three quarters (78.0%, n = 138) were receiving prescribed medicines, most commonly paracetamol, alone (35.6%, n = 63) or in combination with opioids (16.4%, n = 29). One-third (31.6%, n = 56) expressed a desire for more effective medicines; few reported using any non-pharmacological therapies. The median scores for the Pain Disability Questionnaire and Tampa Scale for Kinesiophobia were 74 (IQR 34-104.5, 150 high) and 40 (IQR 35-45, 68 high). CONCLUSIONS: Evidence of provision of appropriate integrated and person-centred chronic pain care is lacking.

Defining the characteristics of intermediate care models including transitional care: an international Delphi study.

BACKGROUND: Although there is growing utilisation of intermediate care to improve the health and well-being of older adults with complex care needs, there is no international agreement on how it is defined, limiting comparability between studies and reducing the ability to scale effective interventions. AIM: To identify and define the characteristics of intermediate care models. METHODS: A scoping review, a modified two-round electronic Delphi study involving 27 multi-professional experts from 13 countries, and a virtual consensus meeting were conducted. RESULTS: Sixty-six records were included in the scoping review, which identified four main themes: transitions, components, benefits and interchangeability. These formed the basis of the first round of the Delphi survey. After Round 2, 16 statements were agreed, refined and collapsed further. Consensus was established for 10 statements addressing the definitions, purpose, target populations, approach to care and organisation of intermediate care models. DISCUSSION: There was agreement that intermediate care represents time-limited services which ensure continuity and quality of care, promote recovery, restore independence and confidence at the interface between home and acute services, with transitional care representing a subset of intermediate care. Models are best delivered by an interdisciplinary team within an integrated health and social care system where a single contact point optimises service access, communication and coordination. CONCLUSIONS: This study identified key defining features of intermediate care to improve understanding and to support comparisons between models and studies evaluating them. More research is required to develop operational definitions for use in different healthcare systems.

Robust retrospective frequency and phase correction for single-voxel MR spectroscopy.

PURPOSE: Subject motion and static field (B0 ) drift are known to reduce the quality of single voxel MR spectroscopy data due to incoherent averaging. Retrospective correction has previously been shown to improve data quality by adjusting the phase and frequency offset of each average to match a reference spectrum. In this work, a new method (RATS) is developed to be tolerant to large frequency shifts (>7 Hz) and baseline instability resulting from inconsistent water suppression. METHODS: In contrast to previous approaches, the variable-projection method and baseline fitting is incorporated into the correction procedure to improve robustness to fluctuating baseline signals and optimization instability. RATS is compared to an alternative method, based on time-domain spectral registration (TDSR), using simulated data to model frequency, phase, and baseline instability. In addition, a J-difference edited glutathione in-vivo dataset is processed using both approaches and compared. RESULTS: RATS offers improved accuracy and stability for large frequency shifts and unstable baselines. Reduced subtraction artifacts are demonstrated for glutathione edited MRS when using RATS, compared with uncorrected or TDSR corrected spectra. CONCLUSIONS: The RATS algorithm has been shown to provide accurate retrospective correction of SVS MRS data in the presence of large frequency shifts and baseline instability. The method is rapid, generic and therefore readily incorporated into MRS processing pipelines to improve lineshape, SNR, and aid quality assessment.

Methodological consensus on clinical proton MRS of the brain: Review and recommendations.

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.