Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly

The adult human brain shrinks slowly with age, but the regional specificity and tissue class specificity of this loss is unclear. Subjects (n=122) were healthy aged participants in a longitudinal cohort who undergo periodic standardized cognitive and clinical examination. Multi-spectral segmentation of magnetic resonance images into grey matter (GM), white matter (WM) and CSF was performed on cross-sectional image data using a custom template and calculated prior probability maps. Global differences were evaluated by fitting a regression model for absolute and normalized subject GM, WM, and CSF values. Global and regional patterns of GM, WM and CSF differences were assessed using optimized voxel-based morphometry (VBM). GM volume decreased with age at a rate of 2.4 cm(3)/year (-0.18%/year); CSF increased by 2.5 cm(3)/year (0.20%/year). Regression analyses showed no significant decrease in WM volume, but a focal WM decrease with age was detected in the anterior corpus callosum using VBM. Diffuse reductions of GM volume were seen with age in the frontal, parietal, and temporal cortex, cerebellum and basal ganglia. Relative regional differences in cortical GM volume with age occurred in the frontal, parietal and temporal lobes, but not in medial temporal lobe or in posterior cingulate. We did not observe significant gender effects. These findings establish a baseline for comparison with pathologic changes in human brain volume between ages 58 and 95 years.     

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.     

Measurement of regional variation of GABA in the human brain by optimized point‐resolved spectroscopy at 7 T in vivo

The ¹H resonances of γ‐aminobutyric acid (GABA) in the human brain in vivo are extensively overlapped with the neighboring abundant resonances of other metabolites and remain indiscernible in short‐TE MRS at 7 T. Here we report that the GABA resonance at 2.28 ppm can be fully resolved by means of echo time optimization of a point‐resolved spectroscopy (PRESS) scheme. Following numerical simulations and phantom validation, the subecho times of PRESS were optimized at (TE, TE₂) = (31, 61) ms for detection of GABA, glutamate (Glu), glutamine (Gln), and glutathione (GSH). The in vivo feasibility of the method was tested in several brain regions in nine healthy subjects. Spectra were acquired from the medial prefrontal, left frontal, medial occipital, and left occipital brain and analyzed with LCModel. Following the gray and white matter (GM and WM) segmentation of T₁‐weighted images, linear regression of metabolite estimates was performed against the fractional GM contents. The GABA concentration was estimated to be about seven times higher in GM than in WM. GABA was overall higher in frontal than in occipital brain. Glu was about twice as high in GM as in WM in both frontal and occipital brain. Gln was significantly different between frontal GM and WM while being similar between occipital GM and WM. GSH did not show significant dependence on tissue content. The signals from N‐acetylaspartylglutamate were clearly resolved, giving the concentration more than 10 times higher in WM than in GM. Our data indicate that the PRESS TE = 92 ms method provides an effective means for measuring GABA and several challenging J‐coupled spin metabolites in human brain at 7 T. Copyright © 2014 John Wiley & Sons, Ltd.     

Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images

During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI.     

基于模糊K-近邻规则的多谱磁共振脑图像分割方法的研究

本文在K 近邻 (K nearestneighbor ,简称KNN)规则的基础上 ,基于模糊C 均值聚类 (FuzzyC meansclustering ,简称FCM)技术 ,提出了模糊K 近邻算法 (FuzzyK nearestneighbor ,简称FKNN) ,并利用该算法对磁共振脑图像进行分割研究。首先对磁共振颅脑图像进行预分割 ,剔除颅骨和肌肉等非脑组织 ,只保留大脑结构 ;然后利用FKNN算法对大脑结构进行分割 ,从脑组织中分别提取出白质、灰质和脑脊液。实验结果表明 ,FKNN方法能有效地从大脑结构中分割出白质、灰质和脑脊液 ,分割效果明显优于KNN方法。     

结合脑图谱和水平集的MR图像分割的研究

本文利用脑图谱的先验知识并结合水平集等算法实现对脑MR图像的初步分割。主要步骤：（1）选取数字脑图谱，对图谱进行预处理；（2）实现图谱与脑MR图像的配准；（3）利用图谱提供的轮廓信息对水平集算法进行初始化，完成颅骨和脑脊液的提取以及脑白质和脑灰质的分割。实验结果表明，利用脑图谱提供的信息可有效解决水平集算法初始化问题，缩小求解空间，减少迭代次数，该方法具有较好的鲁棒性。     

基于改进空间模糊聚类的DTI图像分割算法

针对模糊C均值(FCM)聚类算法初始聚类中心选择的随机性和噪声的敏感性等问题,提出一种基于改进空间模糊聚类的图像分割算法来分割人脑DTI图像。使用局部密度核函数和中心距离函数精确选取初始聚类中心,不仅可以解决因聚类中心随机选取造成的聚类效果不稳定的问题,而且还可以使目标函数迅速收敛,提高分割效率;通过将正态分布空间信息融入模糊隶属度函数,能减小图像噪声以及人为因素对分割结果的影响。用该方法与FCM、SFCM方法对人脑DTI数据进行分割,以评价算法的聚类效果。实验对美国明尼苏达大学生物医学功能成像与神经工程实验室提供的58例DTI数据、3例FA参数图像以及6例迭加过噪声的人脑DTI图像进行分割,结果表明:该算法分割系数最高,可达到0.984 1;在同一图像中,该算法在划分系数上比FCM最高提升20.2%,并且在划分熵上比SFCM最高下降19.8%;该算法目标函数平均迭代次数为32,较FCM的52次与空间FCM的76次有明显降低。实验证明,该算法能够准确、快速地分割出重要目标,且对图像噪声不敏感。     

Partial volume correction of brain perfusion estimates using the inherent signal data of time‐resolved arterial spin labeling

Quantitative perfusion MRI based on arterial spin labeling (ASL) is hampered by partial volume effects (PVEs), arising due to voxel signal cross‐contamination between different compartments. To address this issue, several partial volume correction (PVC) methods have been presented. Most previous methods rely on segmentation of a high‐resolution T₁‐weighted morphological image volume that is coregistered to the low‐resolution ASL data, making the result sensitive to errors in the segmentation and coregistration. In this work, we present a methodology for partial volume estimation and correction, using only low‐resolution ASL data acquired with the QUASAR sequence. The methodology consists of a T₁‐based segmentation method, with no spatial priors, and a modified PVC method based on linear regression. The presented approach thus avoids prior assumptions about the spatial distribution of brain compartments, while also avoiding coregistration between different image volumes. Simulations based on a digital phantom as well as in vivo measurements in 10 volunteers were used to assess the performance of the proposed segmentation approach. The simulation results indicated that QUASAR data can be used for robust partial volume estimation, and this was confirmed by the in vivo experiments. The proposed PVC method yielded probable perfusion maps, comparable to a reference method based on segmentation of a high‐resolution morphological scan. Corrected gray matter (GM) perfusion was 47% higher than uncorrected values, suggesting a significant amount of PVEs in the data. Whereas the reference method failed to completely eliminate the dependence of perfusion estimates on the volume fraction, the novel approach produced GM perfusion values independent of GM volume fraction. The intra‐subject coefficient of variation of corrected perfusion values was lowest for the proposed PVC method. As shown in this work, low‐resolution partial volume estimation in connection with ASL perfusion estimation is feasible, and provides a promising tool for decoupling perfusion and tissue volume. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic diffusion‐tensor measurements in muscle tissue using the single‐line multiple‐echo diffusion‐tensor acquisition technique at 3T

When diffusion biomarkers display transient changes, i.e. in muscle following exercise, traditional diffusion‐tensor imaging (DTI) methods lack the temporal resolution to resolve the dynamics. This article presents an MRI method for dynamic diffusion‐tensor acquisitions on a clinical 3T scanner. This method, the Single‐Line Multiple‐Echo Diffusion‐Tensor Acquisition Technique (SL‐MEDITATE), achieves a high temporal resolution (4 s) by 1 rapid diffusion encoding through the acquisition of multiple echoes with unique diffusion sensitization and 2 limiting the readout to a single line volume. The method is demonstrated in a rotating anisotropic phantom, a flow phantom with adjustable flow speed and in vivo skeletal calf muscle of healthy volunteers following a plantar flexion exercise. The rotating and flow‐varying phantom experiments show that SL‐MEDITATE correctly identifies the rotation of the first diffusion eigenvector and the changes in diffusion‐tensor parameter magnitudes, respectively. Immediately following exercise, the in vivo mean diffusivity (MD) time courses show, before the well‐known increase, an initial decrease that is not typically observed in traditional DTI. In conclusion, SL‐MEDITATE can be used to capture transient changes in tissue anisotropy in a single line. Future progress might allow for dynamic DTI when combined with appropriate k‐space trajectories and compressed sensing reconstruction. Copyright © 2015 John Wiley & Sons, Ltd.     

Brain volumetry: an active contour model-based segmentation followed by SVM-based classification

In this paper a novel automatic approach to identify brain structures in magnetic resonance imaging (MRI) is presented for volumetric measurements. The method is based on the idea of active contour models and support vector machine (SVM) classifiers. The main contributions of the presented method are effective modifications on brain images for active contour model and extracting simple and beneficial features for the SVM classifier. The segmentation process starts with a new generation of active contour models, i.e., vector field convolution (VFC) on modified brain images. VFC results are brain images with the least non-brain regions which are passed on to the SVM classification. The SVM features are selected according to the structure of brain tissues, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). SVM classifiers are trained for each brain tissue based on the set of extracted features. Although selected features are very simple, they are both sufficient and tissue separately effective. Our method validation is done using the gold standard brain MRI data set. Comparison of the results with the existing algorithms is a good indication of our approach's success.     

Brain tissue volumes in relation to cognitive function and risk of dementia

We investigated in a population-based cohort study the association of global and lobar brain tissue volumes with specific cognitive domains and risk of dementia. Participants (n=490; 60-90 years) were non-demented at baseline (1995-1996). From baseline brain MRI-scans we obtained global and lobar volumes of CSF, GM, normal WM, white matter lesions and hippocampus. We performed neuropsychological testing at baseline to assess information processing speed, executive function, memory function and global cognitive function. Participants were followed for incident dementia until January 1, 2005. Larger volumes of CSF and WML were associated with worse performance on all neuropsychological tests, and an increased risk of dementia. Smaller WM volume was related to poorer information processing speed and executive function. In contrast, smaller GM volume was associated with worse memory function and increased risk of dementia. When investigating lobar GM volumes, we found that hippocampal volume and temporal GM volume were most strongly associated with risk of dementia, even in persons without objective and subjective cognitive deficits at baseline, followed by frontal and parietal GM volumes.     

Right lateralized white matter abnormalities in first-episode,drug-naive paranoid schizophrenia

Numerous studies in first-episode schizophrenia suggest the involvement of white matter (WM) abnormalities in multiple regions underlying the pathogenesis of this condition. However, there has never been a neuroimaging study in patients with first-episode, drug-naive paranoid schizophrenia by using tract-based spatial statistics (TBSS) method. Here, we used diffusion tensor imaging (DTI) with TBSS method to investigate the brain WM integrity in patients with first-episode, drug-naive paranoid schizophrenia. Twenty patients with first-episode, drug-naive paranoid schizophrenia and 26 healthy subjects matched with age, gender, and education level were scanned with DTI. An automated TBSS approach was employed to analyze the data. Voxel-wise statistics revealed that patients with paranoid schizophrenia had decreased fractional anisotropy (FA) values in the right superior longitudinal fasciculus (SLF) II, the right fornix, the right internal capsule, and the right external capsule compared to healthy subjects. Patients did not have increased FA values in any brain regions compared to healthy subjects. There was no correlation between the FA values in any brain regions and patient demographics and the severity of illness. Our findings suggest right-sided alterations of WM integrity in the WM tracts of cortical and subcortical regions may play an important role in the pathogenesis of paranoid schizophrenia.     

3D cerebral MR image segmentation using multiple-classifier system

The three soft brain tissues white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) identified in a magnetic resonance (MR) image via image segmentation techniques can aid in structural and functional brain analysis, brain’s anatomical structures measurement and visualization, neurodegenerative disorders diagnosis, and surgical planning and image-guided interventions, but only if obtained segmentation results are correct. This paper presents a multiple-classifier-based system for automatic brain tissue segmentation from cerebral MR images. The developed system categorizes each voxel of a given MR image as GM, WM, and CSF. The algorithm consists of preprocessing, feature extraction, and supervised classification steps. In the first step, intensity non-uniformity in a given MR image is corrected and then non-brain tissues such as skull, eyeballs, and skin are removed from the image. For each voxel, statistical features and non-statistical features were computed and used a feature vector representing the voxel. Three multilayer perceptron (MLP) neural networks trained using three different datasets were used as the base classifiers of the multiple-classifier system. The output of the base classifiers was fused using majority voting scheme. Evaluation of the proposed system was performed using Brainweb simulated MR images with different noise and intensity non-uniformity and internet brain segmentation repository (IBSR) real MR images. The quantitative assessment of the proposed method using Dice, Jaccard, and conformity coefficient metrics demonstrates improvement (around 5 % for CSF) in terms of accuracy as compared to single MLP classifier and the existing methods and tools such FSL-FAST and SPM. As accurately segmenting a MR image is of paramount importance for successfully promoting the clinical application of MR image segmentation techniques, the improvement obtained by using multiple-classifier-based system is encouraging.     

An algorithm for automatic segmentation and classification of magnetic resonance brain images

In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magentic resonance (MR) images were acquired ino 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.     

White matter integrity is associated with cerebrospinal fluid markers of Alzheimer's disease in normal adults

We explored whether white matter (WM) integrity in cognitively normal (CN) older adults is associated with cerebrospinal fluid (CSF) markers of Alzheimer's disease pathology. Twenty CN older adults underwent lumbar puncture and magnetic resonance imaging within a few days of each other. Analysis of diffusion tensor imaging data involved a priori region of interest and voxelwise approaches. The region of interest results revealed a positive correlation between CSF measures of amyloid-beta (Aβ₄₂ and Aβ₄₂/p-Tau₁₈₁) and WM integrity in the fornix, a relationship which persisted after controlling for hippocampal volume and fornix volume. Lower WM integrity in the same portion of the fornix was also associated with reduced performance on the Digit Symbol test. Subsequent exploratory voxelwise analyses indicated a positive correlation between CSF Aβ₄₂/p-Tau₁₈₁ and WM integrity in bilateral portions of the fornix, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and in the corpus callosum and left inferior longitudinal fasciculus. Our results link lower WM microstructural integrity in CN older adults with CSF biomarkers of Alzheimer's disease and suggest that this association in the fornix may be independent of volumetric measures.     

Brain tissue volumes in the general elderly population. The Rotterdam Scan Study

We investigated how volumes of cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) varied with age, sex, small vessel disease and cardiovascular risk factors in the Rotterdam Scan Study. Participants (n = 490; 60–90 years) were non-demented and 51.0% had hypertension, 4.9% had diabetes mellitus, 17.8% were current smoker and 54.0% were former smoker. We segmented brain MR-images into GM, normal WM, white matter lesion (WML) and CSF. Brain infarcts were rated visually. Volumes were expressed as percentage of intra-cranial volume. With increasing age, volumes of total brain, normal WM and total WM decreased; that of GM remained unchanged; and that of WML increased, in both men and women. Excluding persons with infarcts did not alter these results. Persons with larger load of small vessel disease had smaller brain volume, especially normal WM volume. Diastolic blood pressure, diabetes mellitus and current smoking were also related to smaller brain volume. In the elderly, higher age, small vessel disease and cardiovascular risk factors are associated with smaller brain volume, especially WM volume.     

Structural brain alterations in bipolar disorder II: A combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) study

Background

Brain structural changes have been described in bipolar disorder (BP), but usually studies focused on both I and II subtypes indiscriminately and investigated changes in either brain volume or white matter (WM) integrity. We used combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) analysis to track changes in the grey matter (GM) and WM in the brains of patients affected by BPII, as compared to healthy controls.

Methods

Using VBM and DTI, we scanned 20 DSM-IV-TR BPII patients in their euthymic phase and 21 healthy, age- and gender-matched volunteers with no psychiatric history.

Results

VBM showed decreases in GM of BPII patients, compared to controls, which were diffuse in nature and most prominent in the right middle frontal gyrus and in the right superior temporal gurus. DTI showed significant and widespread FA reduction in BPII patients in all major WM tracts, including cortico-cortical association tracts.

Limitations

The small sample size limits the generalisability of our findings.

Conclusions

Reduced GM volumes and WM integrity changes in BPII patients are not prominent like those previously reported in bipolar disorder type-I and involve cortical structures and their related association tracts.     

Brain tissue volumes by APOE genotype and leisure activity-the AGES-Reykjavik Study

This study investigates the association of the APOE ε4 allele and leisure activity with brain tissue volumes, including white matter hyperintensities (WMH), in a population-based cohort of 4303 nondemented individuals, aged 66-96 years. APOE ε4 carriers were shown to have greater WMH and cerebrospinal fluid (CSF) volumes than noncarriers but smaller gray matter (GM) volumes. There was no significant difference in white matter (WM) and total brain parenchymal (TBP) volumes between APOE ε4 carriers and noncarriers. Tests for linear trend showed that individuals with lower leisure activity levels had greater WMH and CSF volumes, smaller TBP, WM and GM volumes than those with the highest levels of participation. The significant positive trend of the leisure activity with the brain tissue volumes was observed in the APOE ε4 carriers as well as in noncarriers after adjustment for demographic and health factors. These cross-sectional data suggest leisure activity is associated with tissue volumes in the brain irrespective of the APOE ε4 risk allele status.     

Region‐specific impairment of the cervical spinal cord (SC) in amyotrophic lateral sclerosis: A preliminary study using SC templates and quantitative MRI (diffusion tensor imaging/inhomogeneous magnetization transfer)

In this preliminary study, our objective was to investigate the potential of high‐resolution anatomical imaging, diffusion tensor imaging (DTI) and conventional/inhomogeneous magnetization transfer imaging [magnetization transfer (MT)/inhomogeneous magnetization transfer (ihMT)] at 3 T, analyzed with template‐extracted regions of interest, to measure the atrophy and structural changes of white (WM) and gray (GM) matter spinal cord (SC) occurring in patients with amyotrophic lateral sclerosis (ALS). Ten patients with ALS and 20 age‐matched healthy controls were recruited. SC GM and WM areas were automatically segmented using dedicated templates. Atrophy indices were evaluated from T ₂*‐weighted images at each vertebral level from cervical C1 to C6. DTI and ihMT metrics were quantified within the corticospinal tract (CST), posterior sensory tract (PST) and anterior GM (aGM) horns at the C2 and C5 levels. Clinical disabilities of patients with ALS were evaluated using the Revised ALS Functional Rating Scale, upper motor neuron (UMN) and Medical Research Council scorings, and correlated with MR metrics. Compared with healthy controls, GM and WM atrophy was observed in patients with ALS, especially at lower cervical levels, where a strong correlation was also observed between GM atrophy and the UMN score (R  = ?0.75, p  = 0.05 at C6). Interestingly, a significant decrease in ihMT ratio was found in all regions of interest (p  < 0.0008), fractional anisotropy (FA) and MT ratios decreased significantly in CST, especially at C5 (p  < 0.005), and λ_// (axial diffusivity) decreased significantly in CST (p  = 0.0004) and PST (p  = 0.003) at C2. Strong correlations between MRI metrics and clinical scores were also found (0.47 < |R | < 0.87, p  < 0.05). Altogether, these preliminary results suggest that high‐resolution anatomical imaging and ihMT imaging, in addition to DTI, are valuable for the characterization of SC tissue impairment in ALS. In this study, in addition to an important SC WM demyelination, we also observed, for the first time in ALS, impairments of cervical aGM.     

Measurement of glycine in healthy and tumorous brain by triple‐refocusing MRS at 3 T in vivo

Glycine (Gly) has been implicated in several neurological disorders, including malignant brain tumors. The precise measurement of Gly is challenging largely as a result of the spectral overlap with myo‐inositol (mI). We report a new triple‐refocusing sequence for the reliable co‐detection of Gly and mI at 3 T and for the evaluation of Gly in healthy and tumorous brain. The sequence parameters were optimized with density‐matrix simulations and phantom validation. With a total TE of 134 ms, the sequence gave complete suppression of the mI signal between 3.5 and 3.6 ppm and, consequently, well‐defined Gly (3.55 ppm) and mI (3.64 ppm) peaks. In vivo ¹H magnetic resonance spectroscopy (MRS) data were acquired from the gray matter (GM)‐dominant medial occipital and white matter (WM)‐dominant left parietal regions in six healthy subjects, and analyzed with LCModel using in‐house‐calculated basis spectra. Tissue segmentation was performed to obtain the GM and WM contents within the MRS voxels. Metabolites were quantified with reference to GM‐rich medial occipital total creatine at 8 mM. The Gly and mI concentrations were estimated to be 0.63 ± 0.05 and 8.6 ± 0.6 mM for the medial occipital and 0.34 ± 0.05 and 5.3 ± 0.8 mM for the left parietal regions, respectively. From linear regression of the metabolite estimates versus fractional GM content, the concentration ratios between pure GM and pure WM were estimated to be 2.6 and 2.1 for Gly and mI, respectively. Clinical application of the optimized sequence was performed in four subjects with brain tumor. The Gly levels in tumors were higher than those of healthy brain. Gly elevation was more extensive in a post‐contrast enhancing region than in a non‐enhancing region. The data indicate that the optimized triple‐refocusing sequence may provide reliable co‐detection of Gly and mI, and alterations of Gly in brain tumors can be precisely evaluated.