Whole-Brain Magnetic Resonance Spectroscopy Reveals Distinct Alterations in Neurometabolic Profile in Progressive Supranuclear Palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized by supranuclear gaze palsy, early postural instability, and a frontal dysexecutive syndrome. Contrary to normal brain magnetic resonance imaging in Parkinson's disease (PD), PSP shows specific cerebral atrophy patterns and alterations, but these findings are not present in every patient, and it is still unclear if these signs are also detectable in early disease stages. OBJECTIVE: The aim of the present study was to analyze the metabolic profile of patients with clinically diagnosed PSP in comparison with matched healthy volunteers and PD patients using whole-brain magnetic resonance spectroscopic imaging (wbMRSI). METHODS: Thirty-nine healthy controls (HCs), 29 PD, and 22 PSP patients underwent wbMRSI. PSP and PD patients were matched for age and handedness with HCs. Clinical characterization was performed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale, PSP rating scale, and DemTect (test for cognitive assessment). RESULTS: In PSP patients a significant reduction in N-acetyl-aspartate (NAA) was detected in all brain lobes. Fractional volume of the cerebrospinal fluid significantly increased in PSP patients compared to PD and healthy volunteers. CONCLUSIONS: In PSP much more neuronal degeneration and cerebral atrophy have been detected compared with PD. The most relevant alteration is the decrease in NAA in all lobes of the brain, which also showed a partial correlation with clinical symptoms. However, more studies are needed to confirm the additional value of wbMRSI in clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

SLOW: A novel spectral editing method for whole-brain MRSI at ultra high magnetic field.

PURPOSE: At ultra-high field (UHF), B1+ -inhomogeneities and high specific absorption rate (SAR) of adiabatic slice-selective RF-pulses make spatial resolved spectral-editing extremely challenging with the conventional MEGA-approach. The purpose of the study was to develop a whole-brain resolved spectral-editing MRSI at UHF (UHF, B0 ≥ 7T) within clinical acceptable measurement-time and minimal chemical-shift-displacement-artifacts (CSDA) allowing for simultaneous GABA/Glx-, 2HG-, and PE-editing on a clinical approved 7T-scanner. METHODS: Slice-selective adiabatic refocusing RF-pulses (2π-SSAP) dominate the SAR to the patient in (semi)LASER based MEGA-editing sequences, causing large CSDA and long measurement times to fulfill SAR requirements, even using SAR-minimized GOIA-pulses. Therefore, a novel type of spectral-editing, called SLOW-editing, using two different pairs of phase-compensated chemical-shift selective adiabatic refocusing-pulses (2π-CSAP) with different refocusing bandwidths were investigated to overcome these problems. RESULTS: Compared to conventional echo-planar spectroscopic imaging (EPSI) and MEGA-editing, SLOW-editing shows robust refocusing and editing performance despite to B1+ -inhomogeneity, and robustness to B0 -inhomogeneities (0.2 ppm ≥ ΔB0  ≥ -0.2 ppm). The narrow bandwidth (∼0.6-0.8 kHz) CSAP reduces the SAR by 92%, RF peak power by 84%, in-excitation slab CSDA by 77%, and has no in-plane CSDA. Furthermore, the CSAP implicitly dephases water, lipid and all the other signals outside of range (≥ 4.6 ppm and ≤1.4 ppm), resulting in additional water and lipid suppression (factors ≥ 1000s) at zero SAR-cost, and no spectral aliasing artifacts. CONCLUSION: A new spectral-editing has been developed that is especially suitable for UHF, and was successfully applied for 2HG, GABA+, PE, and Glx-editing within 10 min clinical acceptable measurement time.

Cardiovascular risks impact human brain N-acetylaspartate in regionally specific patterns.

Cardiovascular risk factors such as dyslipidemia and hypertension increase the risk for white matter pathology and cognitive decline. We hypothesize that white matter levels of N-acetylaspartate (NAA), a chemical involved in the metabolic pathway for myelin lipid synthesis, could serve as a biomarker that tracks the influence of cardiovascular risk factors on white matter prior to emergence of clinical changes. To test this, we measured levels of NAA across white matter and gray matter in the brain using echo planar spectroscopic imaging (EPSI) in 163 individuals and examined the relationship of regional NAA levels and cardiovascular risk factors as indexed by the Framingham Cardiovascular Risk Score (FCVRS). NAA was strongly and negatively correlated with FCVRS across the brain, but, after accounting for age and sex, the association was found primarily in white matter regions, with additional effects found in the thalamus, hippocampus, and cingulate gyrus. FCVRS was also negatively correlated with creatine levels, again primarily in white matter. The results suggest that cardiovascular risks are related to neurochemistry with a predominantly white matter pattern and some subcortical and cortical gray matter involvement. NAA mapping of the brain may provide early surveillance for the potential subclinical impact of cardiovascular and metabolic risk factors on the brain.

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations.

Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

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.

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.

Lesion segmentation for MR spectroscopic imaging using the convolution difference method.

PURPOSE: Delineation of lesion boundaries from volumetric MRSI metabolite ratio maps using a method that accounts for the spatial response function of the acquisition and variable spectral quality and is robust to signal heterogeneity within the lesion. METHODS: A novel method for lesion segmentation, termed convolution difference, has been developed that is robust to signal heterogeneity within the lesion and to differences in the spatial response function. Procedures are described for processing metabolite ratio maps and to exclude regions of inadequate spectral quality. This method was evaluated using computer simulations, and the results were compared with an iterative thresholding technique that determines an optimal amplitude threshold, and with the use of a fixed amplitude threshold. These methods were evaluated for segmentation of volumetric MRSI studies of gliomas using maps of the choline to N-acetylaspartate ratio, and a qualitative comparison of lesion volumes carried out. RESULTS: Simulation studies indicated improved performance for the convolution difference method when applied to ratio maps. Variations in tumor volume were observed for the in vivo studies between the convolution difference and the iterative thresholding methods; however, visual analysis indicates that both showed improved accuracy in comparison to using a fixed amplitude threshold. CONCLUSION: This study reinforces previous reports indicating that the use of fixed threshold values for segmentation of maps with broad spatial response functions can result in errors in lesion volume definition. A novel segmentation method, termed the convolution difference, has been introduced and demonstrated to be robust for segmentation of volumetric MRSI metabolite data.

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectrum to the data. For high-resolution volumetric MRSI of the brain, which may have ~10,000 spectra, significant processing time is required for spectral analysis and generation of metabolite maps. METHODS: A novel unsupervised deep learning architecture that combines a convolutional neural network with a priori models of the spectrum is presented. This architecture, a convolutional encoder-model decoder (CEMD), combines the strengths of adaptive and unbiased convolutional networks with models of magnetic resonance and is readily interpretable. RESULTS: The CEMD architecture performs accurate spectral fitting for volumetric MRSI in patients with glioblastoma, provides whole-brain fitting in 1 min on a standard computer, and handles a variety of spectral artifacts. CONCLUSION: A new architecture combining physics domain knowledge with convolutional neural networks has been developed and is able to perform rapid spectral fitting of whole-brain data. Rapid processing is a critical step toward routine clinical practice.

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.

Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma.

Accurate differentiation of true progression (TP) from pseudoprogression (PsP) in patients with glioblastomas (GBMs) is essential for planning adequate treatment and for estimating clinical outcome measures and future prognosis. The purpose of this study was to investigate the utility of three-dimensional echo planar spectroscopic imaging (3D-EPSI) in distinguishing TP from PsP in GBM patients. For this institutional review board approved and HIPAA compliant retrospective study, 27 patients with GBM demonstrating enhancing lesions within six months of completion of concurrent chemo-radiation therapy were included. Of these, 18 were subsequently classified as TP and 9 as PsP based on histological features or follow-up MRI studies. Parametric maps of choline/creatine (Cho/Cr) and choline/N-acetylaspartate (Cho/NAA) were computed and co-registered with post-contrast T1 -weighted and FLAIR images. All lesions were segmented into contrast enhancing (CER), immediate peritumoral (IPR), and distal peritumoral (DPR) regions. For each region, Cho/Cr and Cho/NAA ratios were normalized to corresponding metabolite ratios from contralateral normal parenchyma and compared between TP and PsP groups. Logistic regression analyses were performed to obtain the best model to distinguish TP from PsP. Significantly higher Cho/NAA was observed from CER (2.69 ± 1.00 versus 1.56 ± 0.51, p = 0.003), IPR (2.31 ± 0.92 versus 1.53 ± 0.56, p = 0.030), and DPR (1.80 ± 0.68 versus 1.19 ± 0.28, p = 0.035) regions in TP patients compared with those with PsP. Additionally, significantly elevated Cho/Cr (1.74 ± 0.44 versus 1.34 ± 0.26, p = 0.023) from CER was observed in TP compared with PsP. When these parameters were incorporated in multivariate regression analyses, a discriminatory model with a sensitivity of 94% and a specificity of 87% was observed in distinguishing TP from PsP. These results indicate the utility of 3D-EPSI in differentiating TP from PsP with high sensitivity and specificity.

Metabolic counterparts of sodium accumulation in multiple sclerosis: A whole brain 23Na-MRI and fast 1H-MRSI study.

BACKGROUND: Increase of brain total sodium concentrations (TSC) is present in multiple sclerosis (MS), but its pathological involvement has not been assessed yet. OBJECTIVE: To determine in vivo the metabolic counterpart of brain sodium accumulation. MATERIALS/METHODS: Whole brain 23Na-MR imaging and 3D-1H-EPSI data were collected in 21 relapsing-remitting multiple sclerosis (RRMS) patients and 20 volunteers. Metabolites and sodium levels were extracted from several regions of grey matter (GM), normal-appearing white matter (NAWM) and white matter (WM) T2 lesions. Metabolic and ionic levels expressed as Z-scores have been averaged over the different compartments and used to explain sodium accumulations through stepwise regression models. RESULTS: MS patients showed significant 23Na accumulations with lower choline and glutamate-glutamine (Glx) levels in GM; 23Na accumulations with lower N-acetyl aspartate (NAA), Glx levels and higher Myo-Inositol (m-Ins) in NAWM; and higher 23Na, m-Ins levels with lower NAA in WM T2 lesions. Regression models showed associations of TSC increase with reduced NAA in GM, NAWM and T2 lesions, as well as higher total-creatine, and smaller decrease of m-Ins in T2 lesions. GM Glx levels were associated with clinical scores. CONCLUSION: Increase of TSC in RRMS is mainly related to neuronal mitochondrial dysfunction while dysfunction of neuro-glial interactions within GM is linked to clinical scores.

Spectral decomposition for resolving partial volume effects in MRSI.

PURPOSE: Estimation of brain metabolite concentrations by MR spectroscopic imaging (MRSI) is complicated by partial volume contributions from different tissues. This study evaluates a method for increasing tissue specificity that incorporates prior knowledge of tissue distributions. METHODS: A spectral decomposition (sDec) technique was evaluated for separation of spectra from white matter (WM) and gray matter (GM), and for measurements in small brain regions using whole-brain MRSI. Simulation and in vivo studies compare results of metabolite quantifications obtained with the sDec technique to those obtained by spectral fitting of individual voxels using mean values and linear regression against tissue fractions and spectral fitting of regionally integrated spectra. RESULTS: Simulation studies showed that, for GM and the putamen, the sDec method offers < 2% and 3.5% error, respectively, in metabolite estimates. These errors are considerably reduced in comparison to methods that do not account for partial volume effects or use regressions against tissue fractions. In an analysis of data from 197 studies, significant differences in mean metabolite values and changes with age were found. Spectral decomposition resulted in significantly better linewidth, signal-to-noise ratio, and spectral fitting quality as compared to individual spectral analysis. Moreover, significant partial volume effects were seen on correlations of neurometabolite estimates with age. CONCLUSION: The sDec analysis approach is of considerable value in studies of pathologies that may preferentially affect WM or GM, as well as smaller brain regions significantly affected by partial volume effects. Magn Reson Med 79:2886-2895, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A convolutional neural network to filter artifacts in spectroscopic MRI.

PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. METHODS: A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. RESULTS: When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. CONCLUSION: The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.

Comparison of reproducibility of single voxel spectroscopy and whole-brain magnetic resonance spectroscopy imaging at 3T.

To date, single voxel spectroscopy (SVS) is the most commonly used MRS technique. SVS is relatively easy to use and provides automated and immediate access to the resulting spectra. However, it is also limited in spatial coverage. A new and very promising MRS technique allows for whole-brain MR spectroscopic imaging (WB-MRSI) with much improved spatial resolution. Establishing the reproducibility of data obtained using SVS and WB-MRSI is an important first step for using these techniques to evaluate longitudinal changes in metabolite concentration. The purpose of this study was to assess and directly compare the reproducibility of metabolite quantification at 3T using SVS and WB-MRSI in 'hand-knob' areas of motor cortices and hippocampi in healthy volunteers. Ten healthy adults were scanned using both SVS and WB-MRSI on three occasions one week apart. N-acetyl aspartate (NAA), creatine (Cr), choline (Cho) and myo-inositol (mI) were quantified using SVS and WB-MRSI with reference to both Cr and H2 O. The reproducibility of each technique was evaluated using the coefficient of variation (CV), and the correspondence between the two techniques was assessed using Pearson correlation analysis. The measured mean (range) intra-subject CVs for SVS were 5.90 (2.65-10.66)% for metabolites (i.e. NAA, Cho, mI) relative to Cr, and 8.46 (4.21-21.07)% for metabolites (NAA, Cr, Cho, mI) relative to H2 O. The mean (range) CVs for WB-MRSI were 7.56 (2.78-11.41)% for metabolites relative to Cr, and 7.79 (4.57-14.11)% for metabolites relative to H2 O. Significant positive correlations were observed between metabolites quantified using SVS and WB-MRSI techniques when the Cr but not H2 O reference was used. The results demonstrate that reproducibilities of SVS and WB-MRSI are similar for quantifying the four major metabolites (NAA, Cr, Cho, mI); both SVS and WB-MRSI exhibited good reproducibility. Our findings add reference information for choosing the appropriate 1 H-MRS technique in future studies.

Value of diffusion kurtosis imaging in assessing low-grade gliomas.

BACKGROUND: Diffusion kurtosis imaging (DKI) measures have been shown to provide increased sensitivity relative to diffusion tensor imaging (DTI) in detecting pathologies. PURPOSE: To compare the sensitivity of DKI-derived kurtosis and diffusion maps for assessment of low-grade gliomas (LGG). STUDY TYPE: Prospective study. POPULATION: In all, 19 LGG patients and 26 healthy control subjects were recruited. FIELD STRENGTH/SEQUENCE: Echo-planar-imaging diffusion-weighted MR images (b-values = 0, 1000, and 2000 with 30 diffusion gradient directions) were acquired on a 3T scanner. ASSESSMENT: Maps for mean, axial, and radial diffusivity (MD, AD, and RD) and kurtosis (MK, AK, and RK), and fractional anisotropy (FA) were evaluated in the tumor, perilesional white matter, and contralateral normal-appearing white matter regions. STATISTICAL TESTING: General linear models (GLM), Cohen's d for effect size estimates, false discovery rate (FDR) for multiple corrections, Cochran Q-test. RESULTS: Pairwise differences were observed for all diffusion and kurtosis measures between the studied regions (FDR P < 0.001), except an FA map that failed to show significant differences between the lesion and perilesional white matter (FDR P = 0.373). Effect size analysis showed that kurtosis metrics were found to be 18.8% (RK, P = 0.144) to 29.1% (AK, P < 0.05) more sensitive in discriminating perilesional regions from the lesion than corresponding diffusion metrics, whereas AK provided a 25.0% (P < 0.05) increase in sensitivity in discriminating perilesional and contralateral white matter. RK was found to be the most sensitive to contralateral white matter differences between low-grade gliomas and controls, with MK and RK providing a significantly greater sensitivity of 587.2% (P < 0.001) and 320.7% (P < 0.001) than MD and RD, respectively. DATA CONCLUSION: Kurtosis maps showed increased sensitivity, as compared to counterpart diffusion maps, for evaluation of microstructural changes in gliomas with a 3-6-fold increment in assessing changes in contralateral white matter. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:1551-1558.

Effects of tissue susceptibility on brain temperature mapping.

A method for mapping of temperature over a large volume of the brain using volumetric proton MR spectroscopic imaging has been implemented and applied to 150 normal subjects. Magnetic susceptibility-induced frequency shifts in gray- and white-matter regions were measured and included as a correction in the temperature mapping calculation. Additional sources of magnetic susceptibility variations of the individual metabolite resonance frequencies were also observed that reflect the cellular-level organization of the brain metabolites, with the most notable differences being attributed to changes of the N-Acetylaspartate resonance frequency that reflect the intra-axonal distribution and orientation of the white-matter tracts with respect to the applied magnetic field. These metabolite-specific susceptibility effects are also shown to change with age. Results indicate no change of apparent brain temperature with age from 18 to 84 years old, with a trend for increased brain temperature throughout the cerebrum in females relative for males on the order of 0.1°C; slightly increased temperatures in the left hemisphere relative to the right; and a lower temperature of 0.3°C in the cerebellum relative to that of cerebral white-matter. This study presents a novel acquisition method for noninvasive measurement of brain temperature that is of potential value for diagnostic purposes and treatment monitoring, while also demonstrating limitations of the measurement due to the confounding effects of tissue susceptibility variations.

Physiological neuronal decline in healthy aging human brain - An in vivo study with MRI and short echo-time whole-brain (1)H MR spectroscopic imaging.

Knowledge of physiological aging in healthy human brain is increasingly important for neuroscientific research and clinical diagnosis. To investigate neuronal decline in normal aging brain eighty-one healthy subjects aged between 20 and 70years were studied with MRI and whole-brain (1)H MR spectroscopic imaging. Concentrations of brain metabolites N-acetyl-aspartate (NAA), choline (Cho), total creatine (tCr), myo-inositol (mI), and glutamine+glutamate (Glx) in ratios to internal water, and the fractional volumes of brain tissue were estimated simultaneously in eight cerebral lobes and in cerebellum. Results demonstrated that an age-related decrease in gray matter volume was the largest contribution to changes in brain volume. Both lobar NAA and the fractional volume of gray matter (FVGM) decreased with age in all cerebral lobes, indicating that the decreased NAA was predominantly associated with decreased gray matter volume and neuronal density or metabolic activity. In cerebral white matter Cho, tCr, and mI increased with age in association with increased fractional volume, showing altered cellular membrane turn-over, energy metabolism, and glial activity in human aging white matter. In cerebellum tCr increased while brain tissue volume decreased with age, showing difference to cerebral aging. The observed age-related metabolic and microstructural variations suggest that physiological neuronal decline in aging human brain is associated with a reduction of gray matter volume and neuronal density, in combination with cellular aging in white matter indicated by microstructural alterations and altered energy metabolism in the cerebellum.

Phased-array combination for MR spectroscopic imaging using a water reference.

PURPOSE: To evaluate methods for multichannel combination of three-dimensional MR spectroscopic imaging (MRSI) data with a focus on using information from a water-reference spectroscopic image. METHODS: Volumetric MRSI data were acquired for a phantom and for human brain using 8- and 32-channel detection. Acquisition included a water-reference dataset that was used to determine the weights for several multichannel combination methods. Results were compared using the signal-to-noise ratio (SNR) of the N-acetylaspartate resonance. RESULTS: Performance of all methods was very similar for the phantom study, with the whitened singular value decomposition (WSVD) and signal magnitude (S) weighting combination having a small advantage. For in vivo studies, the S weighting, SNR weighting and signal to noise squared (S/N(2) ) weighting were the three best methods and performed similarly. Example spectra and SNR maps indicated that the SVD and WSVD methods tend to fail for voxels at the outer edges of the brain that include strong lipid signal contributions. CONCLUSION: For data combination of MRSI data using water-reference information, the S/N(2) weighting, SNR and S weighting were the best methods in terms of spectral quality SNR. These methods are also computationally efficient and easy to implement. Magn Reson Med 76:733-741, 2016. © 2015 Wiley Periodicals, Inc.

Regional distributions of brain glutamate and glutamine in normal subjects.

Glutamate (Glu) and glutamine (Gln) play an important role in neuronal regulation and are of value as MRS-observable diagnostic biomarkers. In this study the relative concentrations of these metabolites have been measured in multiple regions in the normal brain using a short-TE whole-brain MRSI measurement at 3 T combined with a modified data analysis approach that used spatial averaging to obtain high-SNR spectra from atlas-registered anatomic regions or interest. By spectral fitting of high-SNR spectra this approach yielded reliable measurements across a wide volume of the brain. Spectral averaging also demonstrated increased SNR and improved fitting accuracy for the sum of Glu and Gln (Glx) compared with individual voxel fitting. Results in 26 healthy controls showed relatively constant Glu/Cr and Gln/Cr throughout the cerebrum, although with increased values in the anterior cingulum and paracentral lobule, and increased Gln/Cr in the superior motor area. The deep gray-matter regions of thalamus, putamen, and pallidum show lower Glu/Cr compared with cortical white-matter regions. Lobar measurements demonstrated reduced Glu/Cr and Gln/Cr in the cerebellum as compared with the cerebrum, where white-matter regions show significantly lower Glu/Cr and Gln/Cr as compared with gray-matter regions across multiple brain lobes. Regression analysis showed no significant effect of gender on Glu/Cr or Gln/Cr measurement; however, Glx/Cr ratio was found to be significantly negatively correlated with age in some lobar brain regions. In summary, this methodology provides the spectral quality necessary for reliable separation of Glu and Gln at 3 T from a single MRSI acquisition enabling generation of regional distributions of metabolites over a large volume of the brain, including cortical regions. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolic voxel-based analysis of the complete human brain using fast 3D-MRSI: Proof of concept in multiple sclerosis.

PURPOSE: To detect local metabolic abnormalities over the complete human brain in multiple sclerosis (MS) patients, we used optimized fast volumic echo planar spectroscopic imaging (3D-EPSI). MATERIALS AND METHODS: Weighted mean combination of two 3D-EPSI covering the whole brain acquired at 3T in AC-PC and AC-PC+15° axial planes was performed to obtain high-quality metabolite maps for five metabolites: N-acetyl aspartate (NAA), glutamate+glutamine (Glx), choline (Cho), myo-inositol (m-Ins), and creatine+phosphocreatine (tCr). After spatial normalization, maps from 19 patients suffering from relapsing-remitting MS were compared to 19 matched controls using statistical mapping analyses to determine the topography of metabolic abnormalities. Probabilistic white matter (WM) T2 lesion maps and gray matter (GM) atrophy maps were also generated. RESULTS: Two-group analysis of variance (ANOVA) (SPM8, P < 0.005, false discovery rate [FDR]-corrected P < 0.05 at the cluster level with age and sex as confounding covariates) comparing patients and controls matched for age and sex showed clusters of abnormal metabolite levels with 1) decreased NAA (around -15%) and Glx (around 20%) predominantly in GM within prefrontal cortices, motor cortices, bilateral thalami, and mesial temporal cortices in line with neuronal/neuro-astrocytic dysfunction; 2) increased m-Ins (around + 20%) inside WM T2 lesions and in the normal-appearing WM of temporal-occipital lobes, suggesting glial activation. CONCLUSION: We demonstrate the ability to noninvasively map over the complete brain-from vertex to cerebellum-with a validated sequence, the metabolic abnormalities associated with MS, for characterizing the topography of pathological processes affecting widespread areas of WM and GM and its functional impact. J. Magn. Reson. Imaging 2016;44:411-419.