Spatio‐spectral regularization to improve magnetic resonance spectroscopic imaging quantification

Magnetic resonance spectroscopic imaging (MRSI) is a non‐invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow‐up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal‐to‐noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio‐spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single‐voxel methods due to their lower concentrations. Copyright © 2016 John Wiley & Sons, Ltd.     

Exploiting spatial information to estimate metabolite levels in two-dimensional MRSI of heterogeneous brain lesions

MRSI provides MR spectra from multiple adjacent voxels within a body volume represented as a two- or three-dimensional matrix, allowing the measurement of the distribution of metabolites over this volume. The spectra of these voxels are usually analyzed one by one, without exploiting their spatial context. In this article, we present an advanced metabolite quantification method for MRSI data, in which the available spatial information is considered. A nonlinear least-squares algorithm is proposed in which prior knowledge is included in the form of proximity constraints on the spectral parameters within a grid and optimized starting values. A penalty term that promotes a spatially smooth spectral parameter map is added to the fitting algorithm. This method is adaptive, in the sense that several sweeps through the grid are performed and each solution may tune some hyperparameters at run-time. Simulation studies of MRSI data showed significantly improved metabolite estimates after the inclusion of spatial information. Improved metabolite maps were also demonstrated by applying the method to in vivo MRSI data. Overlapping peaks or peaks of compounds present at low concentration can be better quantified with the proposed method than with single-voxel approaches. The new approach compares favorably against the multivoxel approach embedded in the well-known quantification software LCModel.     

基于层间先验知识从脑MRI图像中自动提取脑组织

目的从脑MRI图像中提取脑组织,解决边缘模糊时脑和非脑组织难以分离的问题。方法首先利用各向异性扩散滤波的方法对脑MRI图像进行去噪处理;然后利用形态学的方法对初始脑MRI图像进行脑组织提取,在此分割结果的基础上,利用相邻层脑形态差异较小的特点,实现结构元素的自适应选取,完成从脑MRI图像中逐层准确、自动提取脑组织。结果采用不同来源的数据对算法性能进行了测试,结果优于经典背散射电子成像(BSE)方法的分割结果。结论利用层间先验知识有利于实现边缘模糊的脑组织自动准确提取,且适用性较强。     

Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique

Proton magnetic resonance spectroscopic imaging (¹H MRSI) has been used for the in vivo measurement of intramyocellular lipids (IMCLs) in human calf muscle for almost two decades, but the low spectral resolution between extramyocellular lipids (EMCLs) and IMCLs, partially caused by the magnetic field inhomogeneity, has hindered the accuracy of spectral fitting. The purpose of this paper was to enhance the spectral resolution of ¹H MRSI data from human calf muscle using the SPREAD (spectral resolution amelioration by deconvolution) technique and to assess the influence of improved spectral resolution on the accuracy of spectral fitting and on in vivo measurement of IMCLs. We acquired MRI and ¹H MRSI data from calf muscles of three healthy volunteers. We reconstructed spectral lineshapes of the ¹H MRSI data based on field maps and used the lineshapes to deconvolve the measured MRS spectra, thereby eliminating the line broadening caused by field inhomogeneities and improving the spectral resolution of the ¹H MRSI data. We employed Monte Carlo (MC) simulations with 200 noise realizations to measure the variations of spectral fitting parameters and used an F‐test to evaluate the significance of the differences of the variations between the spectra before SPREAD and after SPREAD. We also used Cramer–Rao lower bounds (CRLBs) to assess the improvements of spectral fitting after SPREAD. The use of SPREAD enhanced the separation between EMCL and IMCL peaks in ¹H MRSI spectra from human calf muscle. MC simulations and F‐tests showed that the use of SPREAD significantly reduced the standard deviations of the estimated IMCL peak areas (p < 10^?8), and the CRLBs were strongly reduced (by ~37%). Copyright © 2014 John Wiley & Sons, Ltd.     

Prior knowledge for time domain quantification of in vivo brain or liver 31P MR spectra

Prior knowledge is required when quantifying in vivo (31)P magnetic resonance spectra from the brain or liver. The prior knowledge system we have used models both the phosphomonoester and phosphodiester resonances as two peaks of equal linewidth and fixed relative chemical shift. The analysis of the data is carried out in the time domain, which allows the broad component of the spectra to be modelled. This prior knowledge method has been tested for analysis of in vivo (31)P MR spectra from the liver and brain and gives results consistent with other methods that are also used to analyse the spectra, but with reduced variability. This technique may be utilized for studies requiring serial MR spectroscopy examinations, before and after patient treatment.     

ProFit: two-dimensional prior-knowledge fitting of J-resolved spectra

A two-dimensional fitting procedure is introduced, capable of extracting the full amount of information from 2D J-resolved magnetic resonance spectroscopic data. The fitting procedure uses a linear combination of 2D model spectra. For reducing the degrees of freedom and increasing robustness, it is divided into a non-linear outer loop and an inner linear least-squares fit for the concentrations. In vitro and in vivo experiments on a whole-body 3 T MR scanner show the detectability of a wide range of metabolites in the human brain, namely total creatine, N-acetylaspartate, N-acetylaspartylglutamate, choline-containing compounds, glutamate, myo-inositol, glutathione, scyllo-inositol, gamma-aminobutyric acid, alanine and ascorbic acid.     

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

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

DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi-supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.     

Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies

The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, I discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. I describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. I discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.     

Correcting frequency and phase offsets in MRS data using robust spectral registration

An algorithm for retrospective correction of frequency and phase offsets in MRS data is presented. The algorithm, termed robust spectral registration (rSR), contains a set of subroutines designed to robustly align individual transients in a given dataset even in cases of significant frequency and phase offsets or unstable lipid contamination and residual water signals. Data acquired by complex multiplexed editing approaches with distinct subspectral profiles are also accurately aligned. Automated removal of unstable lipid contamination and residual water signals is applied first, when needed. Frequency and phase offsets are corrected in the time domain by aligning each transient to a weighted average reference in a statistically optimal order using nonlinear least‐squares optimization. The alignment of subspectra in edited datasets is performed using an approach that specifically targets subtraction artifacts in the frequency domain. Weighted averaging is then used for signal averaging to down‐weight poorer‐quality transients. Algorithm performance was assessed on one simulated and 67 in vivo pediatric GABA‐/GSH‐edited HERMES datasets and compared with the performance of a multistep correction method previously developed for aligning HERMES data. The performance of the novel approach was quantitatively assessed by comparing the estimated frequency/phase offsets against the known values for the simulated dataset or by examining the presence of subtraction artifacts in the in vivo data. Spectral quality was improved following robust alignment, especially in cases of significant spectral distortion. rSR reduced more subtraction artifacts than the multistep method in 64% of the GABA difference spectra and 75% of the GSH difference spectra. rSR overcomes the major challenges of frequency and phase correction.     

The effects of the experience of forming visual images on the spatial organization of the EEG

The spatial organization of biopotentials in the cerebral cortex of 23 subjects who were students at the Faculty of Graphic Arts (“professionals”) as well as 39 subjects lacking systematic experience of visual images (“non-professionals”) was compared with the aim of identifying EEG correlates of the experience of visual images (image formation) in humans. Changes in measures of the spatial organization of biopotentials (spatial synchronization, spatial disordering, coherence, and spectral power) were analyzed as subjects mentally composed visual images consisting of two simple graphic elements — right angles and oblique lines. The total number of image elements increased in each of four sequential tasks, from a number which could be analyzed at the conscious level (4–7 elements) to a number exceeding analysis at the conscious level (8–16). Intergroup differences, particularly increases in the spatial disordering of biopotentials (non-linear processes), were detected when large numbers of elements were used (tasks 3 and 4). This measure increased more markedly in professionals than in non-professionals. Changes were significant in the anterior areas of the right hemisphere. Spatial synchronization of biopotentials (linear processes) increased in non-professionals in the posterior areas of the right hemisphere. Coherence and spectral power increased in professionals in a larger number of narrow-band EEG frequency subranges than in non-professionals. These data show that experience of visual imagery results in a more complex neurodynamic process during the activity, with non-linear dynamics and a multitude of EEG resonance systems at different frequencies. __________ Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 55, No. 6, pp. 812–821, November–December, 2005.     

Prior knowledge of category size impacts visual search

Prior research has shown that category search can be similar to one‐item search (as measured by the N2pc ERP marker of attentional selection) for highly familiar, smaller categories (e.g., letters and numbers) because the finite set of items in a category can be grouped into one unit to guide search. Other studies have shown that larger, more broadly defined categories (e.g., healthy food) also can elicit N2pc components during category search, but the amplitude of these components is typically attenuated. Two experiments investigated whether the perceived size of a familiar category impacts category and exemplar search. We presented participants with 16 familiar company logos: 8 from a smaller category (social media companies) and 8 from a larger category (entertainment/recreation manufacturing companies). The ERP results from Experiment 1 revealed that, in a two‐item search array, search was more efficient for the smaller category of logos compared to the larger category. In a four‐item search array (Experiment 2), where two of the four items were placeholders, search was largely similar between the category types, but there was more attentional capture by nontarget members from the same category as the target for smaller rather than larger categories. These results support a growing literature on how prior knowledge of categories affects attentional selection and capture during visual search. We discuss the implications of these findings in relation to assessing cognitive abilities across the lifespan, given that prior knowledge typically increases with age.     

Neural mechanisms for the effect of prior knowledge on audiovisual integration

Converging evidence indicates that prior knowledge plays an important role in multisensory integration. However, the neural mechanisms underlying the processes with which prior knowledge is integrated with current sensory information remains unknown. In this study, we measured event-related potentials (ERPs) while manipulating prior knowledge using a novel visual letter recognition task in which auditory information was always presented simultaneously. The color of the letters was assigned to a particular probability of being associated with audiovisual congruency (e.g., green = high probability (HP) and blue = low probability (LP)). Results demonstrate that this prior began affecting reaction times to the congruent audiovisual stimuli at about the 900th trial. Consequently, the ERP data was analyzed in two phases: the “early phase” (<trial 600) and the “late phase” (>trial 900). The effects of prior knowledge were revealed through difference waveforms generated by subtracting the ERPs for the congruent audiovisual stimuli in the LP condition from those in the HP condition. A frontal-central probability effect (90-120 ms) was observed in the early phase. A right parietal-occipital probability effect (40-96 ms) and a frontal-central probability effect (170-200 ms) were observed in the late phase. The results suggest that during the initial acquisition of the knowledge about the probability of congruency, the brain assigned more attention to audiovisual stimuli for the LP condition. Following the acquisition of this prior knowledge, it was then used during early stages of visual processing and modulated the activity of multisensory cortical areas.     

A sleep schedule incorporating naps benefits the transformation of hierarchical knowledge

Study ObjectivesThe learning brain establishes schemas (knowledge structures) that benefit subsequent learning. We investigated how sleep and having a schema might benefit initial learning followed by rearranged and expanded memoranda. We concurrently examined the contributions of sleep spindles and slow-wave sleep to learning outcomes.MethodsFifty-three adolescents were randomly assigned to an 8 h Nap schedule (6.5 h nocturnal sleep with a 90-minute daytime nap) or an 8 h No-Nap, nocturnal-only sleep schedule. The study spanned 14 nights, simulating successive school weeks. We utilized a transitive inference task involving hierarchically ordered faces. Initial learning to set up the schema was followed by rearrangement of the hierarchy (accommodation) and hierarchy expansion (assimilation). The expanded sequence was restudied. Recall of hierarchical knowledge was tested after initial learning and at multiple points for all subsequent phases. As a control, both groups underwent a No-schema condition where the hierarchy was introduced and modified without opportunity to set up a schema. Electroencephalography accompanied the multiple sleep opportunities.ResultsThere were main effects of Nap schedule and Schema condition evidenced by superior recall of initial learning, reordered and expanded memoranda. Improved recall was consistently associated with higher fast spindle density but not slow-wave measures. This was true for both nocturnal sleep and daytime naps.ConclusionA sleep schedule incorporating regular nap opportunities compared to one that only had nocturnal sleep benefited building of robust and flexible schemas, facilitating recall of the subsequently rearranged and expanded structured knowledge. These benefits appear to be strongly associated with fast spindles.Clinical Trial registration{"type":"clinical-trial","attrs":{"text":"NCT04044885","term_id":"NCT04044885"}}NCT04044885 (https://clinicaltrials.gov/ct2/show/{"type":"clinical-trial","attrs":{"text":"NCT04044885","term_id":"NCT04044885"}}NCT04044885).     

Automated tissue segmentation and blind recovery of (1)H MRS imaging spectral patterns of normal and diseased human brain

Constrained non-negative matrix factorization (cNMF) with iterative data selection is described and demonstrated as a data analysis method for fast and automatic recovery of biochemically meaningful and diagnostically specific spectral patterns of the human brain from (1)H MRS imaging ((1)H MRSI) data. To achieve this goal, cNMF decomposes in vivo multidimensional (1)H MRSI data into two non-negative matrices representing (a) the underlying tissue-specific spectral patterns and (b) the spatial distribution of the corresponding metabolite concentrations. Central to the proposed approach is automatic iterative data selection which uses prior knowledge about the spatial distribution of the spectra to remove voxels that are due to artifacts and undesired metabolites/tissues such as the strong lipid and water components. The automatic recovery of diagnostic spectral patterns is demonstrated for long-TE (1)H MRSI data on normal human brain, multiple sclerosis, and serial brain tumor. The results show the ability of cNMF with iterative data selection to automatically and simultaneously recover tissue-specific spectral patterns and achieve segmentation of normal and diseased human brain tissue, concomitant with simplification of information content. These features of cNMF, which permit rapid recovery, reduction and interpretation of the complex diagnostic information content of large multi-dimensional spectroscopic imaging data sets, have the potential to enhance the clinical utility of in vivo(1)H MRSI.     

能谱CT基物质分离技术在肝硬化与正常肝脏血流定量研究中的应用

目的:探讨能谱CT基物质分离技术在Child-Pugh A级肝硬化与正常肝脏血流动力学定量研究中的应用价值。方法:收集临床确诊肝硬化Child-Pugh A级并行上腹部增强CT检查患者30例作为肝硬化组,同时收集肝脏及门静脉增强CT检查无异常患者30例作为正常肝脏组。两组均采用能谱CT扫描并重建70 keV单能量图像、碘基物质分离图像,在70 keV、碘基图像上分别测量动脉期（AP）和门静脉期（PV）肝脏五叶（肝尾状叶、肝左外叶、肝左内叶、肝右前叶、肝右后叶）CT值和碘浓度（IC）,计算动脉期碘分数(AIF)和门静脉期碘含量（PVIC）。采用独立样本t检验比较两组能谱参数,并运用ROC曲线分析各参数诊断效能。结果:肝硬化组动脉期CT值和碘浓度与正常肝脏组无统计学差异（P>0.05）,而门静脉期CT值和碘浓度显著低于正常肝脏组（P<0.05）。肝硬化组肝脏AIF稍高于正常肝脏组（P>0.05）,而肝脏PVIC均显著低于正常肝脏组（P<0.05）。以门静脉期肝脏平均碘浓度21.47 mg/mL为阈值评价肝硬化与正常肝脏血流时,ROC曲线下面积（AUC）为0.790,敏感度为77.8%,特异度为83.3%,显著高于肝脏CT值和PVIC。结论:能谱CT基物质分离技术可以用来评价肝硬化与正常肝脏血流动力学的改变和差异,为肝硬化的早期诊断提供更多依据。     

Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge

Quantification of MRS spectra is a challenging problem when a large baseline is present along with a low signal to noise ratio. This work investigates a robust fitting technique that yields accurate peak areas under these conditions. Using simulated long echo time (1)H MRS spectra with low signal to noise ratio and a large baseline component, both the accuracy and reliability of the fit in the frequency domain were greatly improved by reducing the number of fitted parameters and making full use of all the known information concerning the Voigt lineshape. Using an appropriate first order approximation to a popular approximation of the Voigt lineshape, a significant improvement in the estimate of the area of a known spectral peak was obtained with a corresponding reduction in the residual. Furthermore, this improved parameter choice resulted in a large reduction in the number of iterations of the least-squares fitting routine. On the other hand, making use of the known centre frequency differences of the component resonances gave negligible improvement. A wavelet filter was used to remove the baseline component. In addition to performing a Monte Carlo study, these fitting techniques were also applied to a set of 10 spectra acquired from healthy human volunteers. Again, the same reduced parameter model gave the lowest value for chi(2) in each case.     

能谱CT最佳单能图像与肿瘤标志物联合检测在周围型肺癌患者临床诊断中的应用

目的：探析能谱CT最佳单能图像联合血清CEA、NSE、CYFRA21-1、SCCA检测在周围型肺癌（PLC）诊断中的应用价值。方法：选取中国医科大学附属第一医院2019年1月～2020年1月收治的240例疑似PLC患者为研究对象。患者入院后均行CT检查及血清肿瘤标志物检测,测定CEA、NSE、CYFRA21-1、SCCA水平。以病理结果为PLC判定的金标准,评价CT联合血清CEA、NSE、CYFRA21-1、SCCA检测在PLC诊断中的应用价值。结果：疑似PLC患者中恶性病变130例,良性病变110例。恶性病变组的CEA、NSE、CYFRA21-1、SCCA水平显著高于良性病变组（P<0.001）;腺癌患者的CEA、CYFRA21-1、SCCA水平与鳞癌患者相比存在统计学差异（P<0.001）;CT联合肿瘤标志物检测的敏感度和特异性显著优于血清肿瘤标志物检测或CT检查（P<0.001）。结论：能谱CT最佳单能图像联合血清CEA、NSE、CYFRA21-1、SCCA检测诊断PLC的价值较高,其准确度高于单一检测。     

Covariance J‐resolved spectroscopy: Theory and application in vivo

Magnetic resonance spectroscopy (MRS) is a powerful tool capable of investigating the metabolic status of several tissues in vivo. In particular, single‐voxel‐based ¹H spectroscopy provides invaluable biochemical information from a volume of interest (VOI) and has therefore been used in a variety of studies. Unfortunately, typical one‐dimensional MRS data suffer from severe signal overlap and thus important metabolites are difficult to distinguish. One method that is used to disentangle overlapping resonances is the two‐dimensional J‐resolved spectroscopy (JPRESS) experiment. Due to the long acquisition duration of the JPRESS experiment, a limited number of points are acquired in the indirect dimension, leading to poor spectral resolution along this dimension. Poor spectral resolution is problematic because proper peak assignment may be hindered, which is why the zero‐filling method is often used to improve resolution as a post‐processing step. However, zero‐filling leads to spectral artifacts, which may affect visualization and quantitation of spectra. A novel method utilizing a covariance transformation, called covariance J‐resolved spectroscopy (CovJ), was developed in order to improve spectral resolution along the indirect dimension (F₁). Comparison of simulated data demonstrates that peak structures remain qualitatively similar between JPRESS and the novel method along the diagonal region (F₁ = 0 Hz), whereas differences arise in the cross‐peak (F₁≠0 Hz) regions. In addition, quantitative results of in vivo JPRESS data acquired on a 3T scanner show significant correlations (r²>0.86, p<0.001) when comparing the metabolite concentrations between the two methods. Finally, a quantitation algorithm, ‘COVariance Spectral Evaluation of ¹H Acquisitions using Representative prior knowledge’ (Cov‐SEHAR), was developed in order to quantify γ‐aminobutyric acid and glutamate from the CovJ spectra. These preliminary findings indicate that the CovJ method may be used to improve spectral resolution without hindering metabolite quantitation for J‐resolved spectra.     

Pitfalls and advantages of different strategies for the absolute quantification of N‐acetyl aspartate,creatine and choline in white and grey matter by 1H‐MRS

This study extensively investigates different strategies for the absolute quantitation of N‐acetyl aspartate, creatine and choline in white and grey matter by ¹H‐MRS at 1.5 T. The main focus of this study was to reliably estimate metabolite concentrations while reducing the scan time, which remains as one of the main problems in clinical MRS. Absolute quantitation was based on the water‐unsuppressed concentration as the internal standard. We compared strategies based on various experimental protocols and post‐processing strategies. Data were obtained from 30 control subjects using a PRESS sequence at several TE to estimate the transverse relaxation time, T₂, of the metabolites. Quantitation was performed with the algorithm QUEST using two different metabolite signal basis sets: a whole‐metabolite basis set (WhoM) and a basis set in which the singlet signals were split from the coupled signals (MSM). The basis sets were simulated in vivo for each TE used. Metabolites' T₂s were then determined by fitting the estimated signal amplitudes of the metabolites obtained at different TEs. Then the absolute concentrations (mM) of the metabolites were assessed for each subject using the estimated signal amplitudes and either the mean estimated relaxation times of all subjects (mean protocol, MP) or the T₂ estimated from the spectra derived from the same subject (individual protocol, IP). Results showed that MP represents a less time‐consuming alternative to IP in the quantitation of brain metabolites by ¹H‐MRS in both grey and white matter, with a comparable accuracy when performed by MSM. It was also shown that the acquisition time might be further reduced by using a variant of MP, although with reduced accuracy. In this variant, only one water‐suppressed and one water‐unsuppressed spectra were acquired, drastically reducing the duration of the entire MRS examination. However, statistical analysis highlights the reduced accuracy of MP when performed using WhoM, particularly at longer echo times. Copyright © 2009 John Wiley & Sons, Ltd.