Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers

White matter (WM) and gray matter (GM) were accurately measured using a technique based on a single standardized fuzzy classifier (FC) for each tissue. Fuzzy classifier development was based on experts' visual assessments of WM and GM boundaries from a set of T1 parametric MR images. The fuzzy classifier method's accuracy was validated and optimized by a set of T1 phantom images that were based on hand-detailed human brain cryosection images. Nine sets of axial T1 images of varying thickness equally distributed throughout the brain were simulated. All T1 data sets were mapped to the standardized FCs and rapidly segmented into WM and GM voxel fraction images. Resulting volumes revealed that, in most cases, the difference between measured and actual volumes was less than 5%. This was consistent throughout most of the brain, and as expected, the accuracy improved to generally less than 2% for the 1-mm simulated brain slices.     

Unsupervised,automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

The purpose of this study was the development and testing of a method for unsupervised, automated brain segmentation. Two spin-echo sequences were used to obtain relaxation rates and proton-density maps from 1.5 T MR studies, with two axial data sets including the entire brain. Fifty normal subjects (age range, 16 to 76 years) were studied. A Three-dimensional (3D) spectrum of the tissue voxels was used for automatic segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) and for calculation of their volumes. Accuracy and reproducibility were tested with a three-compartment phantom simulating GM, WM, and CSF. In the normal subjects, a significant decrease of GM fractional volume and increased CSF volume with age were observed (P < 0.0001), with no significant changes in WM. This multi-spectral segmentation method permits reproducible, operator-independent volumetric measurements.     

Interactive segmentation of cerebral gray matter, white matter, and CSF: Photographic and MR images

Digital photography of postmortem brain slices was compared with magnetic resonance imaging (MRI) for morphological analysis of human brain atrophy. In this study, we used two human brains obtained at autopsy: a cognitively defined nondemented control (70-yr-old male) and a demented Alzheimer's disease (AD) subject (82yr-old female). For each of two brains, interactive manual image segmentation was performed by two observers on two image sets: (a) four coronal T1-weighted MR images (5 mm slices); and (b) four digitized photographic images from comparable rostrocaudal levels. Microcomputer image analysis software was used to measure the areas of three segmented cerebral compartments—gray matter (GM), white matter (WM) and CSF—for both image types. Resegmentation error was defined as the absolute difference between the areas derived from two segmentation trials divided by the value from trial 1 and multiplied by 100. This yielded the percent difference between the area measurements from the two trials. We found intea-observer agreement was better (error rates 1–18%) than inter-observer agreement (3–70%) with best agreement for WM and least for CSF, the smallest object class. MRI overestimated GM area relative to digitized photographs in the control but not the AD brain. The results define limitations of manual image segmentations and comparison of MRI with pathologic section photographic images.     

Cortical lesions in multiple sclerosis: combined postmortem MR imaging and histopathology

BACKGROUND AND PURPOSE: Cortical lesions constitute a substantial part of the total lesion load in multiple sclerosis (MS) brain. They have been related to neuropsychological deficits, epilepsy, and depression. However, the proportion of purely cortical lesions visible on MR images is unknown. The aim of this study was to determine the proportion of intracortical and mixed gray matter (GM)-white matter (WM) lesions that can be visualized with postmortem MR imaging. METHODS: We studied 49 brain samples from nine cases of chronic MS. Tissue sections were matched to dual-echo T2-weighted spin-echo (T2SE) MR images. MS lesions were identified by means of myelin basic protein immunostaining, and lesions were classified as intracortical, mixed GM-WM, deep GM, or WM. Investigators blinded to the histopathologic results scored postmortem T2SE and 3D fluid-attenuated inversion recovery (FLAIR) images. RESULTS: Immunohistochemistry confirmed 70 WM, eight deep GM, 27 mixed GM-WM, and 63 purely cortical lesions. T2SE images depicted only 3% of the intracortical lesions, and 3D FLAIR imaging showed 5%. Mixed GM-WM lesions were most frequently detectable on T2SE and 3D FLAIR images (22% and 41%, respectively). T2SE imaging showed 13% of deep GM lesions versus 38% on 3D FLAIR. T2SE images depicted 63% of the WM lesions, whereas 3D FLAIR images depicted 71%. Even after side-by-side review of the MR imaging and histopathologic results, many of the intracortical lesions could not be identified retrospectively. CONCLUSION: In contrast to WM lesions and mixed GM-WM lesions, intracortical lesions remain largely undetected with current MR imaging resolution.     

Human gray matter: feasibility of single-slab 3D double inversion-recovery high-spatial-resolution MR imaging

The purpose of this study was to develop and prospectively evaluate the feasibility of a single-slab three-dimensional (3D) double inversion-recovery, or DIR, sequence for magnetic resonance imaging at 1.5 T. The study was approved by the local ethics committee, and informed consent was obtained from six healthy control subjects (one woman, five men; age range, 26-47 years) and two patients with multiple sclerosis (one woman, aged 39; one man, aged 56). Gray matter (GM)-only images were obtained by selectively suppressing cerebrospinal fluid (CSF) and white matter (WM) signals. Whole-brain high-spatial-resolution 3D images (1.2 x 1.2 x 1.3 mm) were acquired within 10 minutes. Cortical and deep GM structures were clearly delineated from WM and CSF, and there were regional differences in GM signal intensity. No flow artifacts from blood or CSF were observed. These GM images with high spatial resolution are suitable to identify cortical pathologic conditions and can potentially be used for segmentation purposes to determine cortical thickness or volume.     

Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes

Purpose:

To develop an automated lesion‐filling technique (LEAP; LEsion Automated Preprocessing) that would reduce lesion‐associated brain tissue segmentation bias (which is known to affect automated brain gray [GM] and white matter [WM] tissue segmentations in people who have multiple sclerosis), and a WM lesion simulation tool with which to test it.

Materials and Methods:

Simulated lesions with differing volumes and signal intensities were added to volumetric brain images from three healthy subjects and then automatically filled with values approximating normal WM. We tested the effects of simulated lesions and lesion‐filling correction with LEAP on SPM‐derived tissue volume estimates.

Results:

GM and WM tissue volume estimates were affected by the presence of WM lesions. With simulated lesion volumes of 15 mL at 70% of normal WM intensity, the effect was to increase GM fractional (relative to intracranial) volumes by ≈2.3%, and reduce WM fractions by ≈3.6%. Lesion filling reduced these errors to ≈0.1%.

Conclusion:

The effect of WM lesions on automated GM and WM volume measures may be considerable and thereby obscure real disease‐mediated volume changes. Lesion filling with values approximating normal WM enables more accurate GM and WM volume measures and should be applicable to structural scans independently of the software used for the segmentation. J. Magn. Reson. Imaging 2010. © 2010 Wiley‐Liss, Inc.     

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

Purpose:

To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images.

Materials and Methods:

Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICA+SVM method to analyze three sets of MR images, T1‐weighted, T2‐weighted, and proton density/fluid‐attenuated inversion recovery images.

Results:

The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICA+SVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICA+SVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra‐ and inter‐operator coefficient of variations.

Conclusion:

The experiments conducted provide evidence that the ICA+SVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI. J. Magn. Reson. Imaging 2010;32:24–34. © 2010 Wiley‐Liss, Inc.     

Tissue sodium concentration in human brain tumors as measured with 23Na MR imaging

PURPOSE: To use combined proton (1H) and sodium 23 (23Na) magnetic resonance (MR) imaging to noninvasively quantify total tissue sodium concentration and to determine if concentration is altered in malignant human brain tumors. MATERIALS AND METHODS: Absolute tissue sodium concentration in malignant gliomas was measured on quantitative three-dimensional 23Na MR images with tissue identification from registered 1H MR images. Concentration was determined in gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and vitreous humor in 20 patients with pathologically proven malignant brain tumors (astrocytoma, n = 17; oligodendroglioma, n = 3) and in nine healthy volunteers. Sodium concentration in tumors and edema was determined from 23Na image signal intensities in regions that were contrast material enhanced on T1-weighted 1H images (tumors) or regions that were only hyperintense on fluid-attenuated inversion recovery (FLAIR) 1H images (edema). Sodium concentrations were measured noninvasively from 23Na images obtained with short echo times (0.4 msec) by using external saline solution phantoms for reference. Differences in mean sodium concentration of all healthy tissue and lesions in patients were tested with a paired t test. Concentration in uninvolved tissues in patients was compared with that in the same tissue types in the volunteers with an independent samples two-tailed t test. RESULTS: Mean concentration (in millimoles per kilogram wet weight) was 61 +/- 8 (SD) for GM, 69 +/- 10 for WM, 135 +/- 10 for CSF, 113 +/- 14 for vitreous humor, 103 +/- 36 for tumor, 68 +/- 11 for unaffected contralateral tissue, and 98 +/- 12 for FLAIR hyperintense regions surrounding tumors. Significant differences (P <.002) in sodium concentration were demonstrated by using a t test for both tumors and surrounding FLAIR hyperintense tissues versus GM, WM, CSF, and contralateral brain tissue. CONCLUSION: 23Na MR imaging with short echo times can be used to quantify absolute tissue sodium concentration in patients with brain tumors and shows increased sodium concentration in tumors relative to that in normal brain structures.     

Improving MRI differentiation of gray and white matter in epileptogenic lesions based on nonlinear feedback.

A new method for enhancing MRI contrast between gray matter (GM) and white matter (WM) in epilepsy surgery patients with symptomatic lesions is presented. This method uses the radiation damping feedback interaction in high-field MRI to amplify contrast due to small differences in resonance frequency in GM and WM corresponding to variations in tissue susceptibility. High-resolution radiation damping-enhanced (RD) images of in vitro brain tissue from five patients were acquired at 14 T and compared with corresponding conventional T(1)-, T(2) (*)-, and proton density (PD)-weighted images. The RD images yielded a six times better contrast-to-noise ratio (CNR = 44.8) on average than the best optimized T(1)-weighted (CNR = 7.92), T(2) (*)-weighted (CNR = 4.20), and PD-weighted images (CNR = 2.52). Regional analysis of the signal as a function of evolution time and initial pulse flip angle, and comparison with numerical simulations confirmed that radiation damping was responsible for the observed signal growth. The time evolution of the signal in different tissue regions was also used to identify subtle changes in tissue composition that were not revealed in conventional MR images. RD contrast is compared with conventional MR methods for separating different tissue types, and its value and limitations are discussed.     

基于阈值分割的K-最近邻规则的磁共振脑图像分割算法的研究

目的:提取脑组织中的白质(WhiteMatter)、灰质(GreyMatter)和脑脊髓液(CSF)。方法:针对去除颅骨和肌肉等非脑部组织的磁共振脑图像,根据解剖学知识采用最大类别方差法(Otsu法)自动寻找阈值,以此为依据进行分类标记,再通过K-最近邻(K-NearestNeighbor,简称KNN)规则对大脑组织结构进行划分。结果:在脑部T1加权像中分割算法分别提取脑组织中的白质、灰质和脑脊髓液。结论:结果表明该算法具有较好的自动性和稳定性。     

Fast method for brain image segmentation: application to proton magnetic resonance spectroscopic imaging.

The interpretation of brain metabolite concentrations measured by quantitative proton magnetic resonance spectroscopic imaging (MRSI) is assisted by knowledge of the percentage of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) within each MRSI voxel. Usually, this information is determined from T(1)-weighted magnetic resonance images (MRI) that have a much higher spatial resolution than the MRSI data. While this approach works well, it is time-consuming. In this article, a rapid data acquisition and analysis procedure for image segmentation is described, which is based on collection of several, thick slice, fast spin echo images (FSE) of different contrast. Tissue segmentation is performed with linear "Eigenimage" filtering and normalization. The method was compared to standard segmentation techniques using high-resolution 3D T(1)-weighted MRI in five subjects. Excellent correlation between the two techniques was obtained, with voxel-wise regression analysis giving GM: R2 = 0.893 +/- 0.098, WM: R2 = 0.892 +/- 0.089, ln(CSF): R2 = 0.831 +/- 0.082). Test-retest analysis in one individual yielded an excellent agreement of measurements with R2 higher than 0.926 in all three tissue classes. Application of FSE/EI segmentation to a sample proton MRSI dataset yielded results similar to prior publications. It is concluded that FSE imaging in conjunction with Eigenimage analysis is a rapid and reliable way of segmenting brain tissue for application to proton MRSI.     

Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection

PURPOSE: To improve the reliability, accuracy, and computational efficiency of tissue classification with multispectral sequences [T1, T2, and proton density (PD)], we developed an automated method for identifying training classes to be used in a discriminant function analysis. We compared it with a supervised operator-dependent method, evaluating its reliability and validity. We also developed a fuzzy (continuous) classification to correct for partial voluming. METHOD: Images were obtained on a 1.5 T GE Signa MR scanner using three pulse sequences that were co-registered. Training classes for the discriminant analysis were obtained in two ways. The operator-dependent method involved defining circular ROIs containing 5-15 voxels that represented "pure" samples of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a total of 150-300 voxels for each tissue type. The automated method involved selecting a large number of samples of brain tissue with sufficiently low variance and randomly placed throughout the brain ("plugs"), partitioning these samples into GM, WM, and CSF, and minimizing the amount of variance within each partition of samples to optimize its "purity." The purity of the plug was estimated by calculating the variance of 8 voxels in all modalities (T1, T2, and PD). We also compared "sharp" (discrete) measurements (which classified tissue only as GM, WM, or CSF) and "fuzzy" (continuous) measurements (which corrected for partial voluming by weighting the classification based on the mixture of tissue types in each voxel). RESULTS: Reliability was compared for the operator-dependent and automated methods as well as for the fuzzy versus sharp classification. The automated sharp classifications consistently had the highest interrater and intrarater reliability. Validity was assessed in three ways: reproducibility of measurements when the same individuals were scanned on multiple occasions, sensitivity of the method to detecting changes associated with aging, and agreement between the automated segmentation values and those produced through expert manual segmentation. The sharp automated classification emerged as slightly superior to the other three methods according to each of these validators. Its reproducibility index (intraclass r) was 0.97, 0.98, and 0.98 for total CSF, total GM, and total WM, respectively. Its correlations with age were 0.54, -0.61, and -0.53, respectively. Its percent agreement with the expert manually segmented tissue for the three tissue types was 93, 90, and 94%, respectively. CONCLUSION: Automated identification of training classes for discriminant analysis was clearly superior to a method that required operator intervention. A sharp (discrete) classification into three tissue types was also slightly superior to one that used "fuzzy" classification to produce continuous measurements to correct for partial voluming. This multispectral automated discriminant analysis method produces a computationally efficient, reliable, and valid method for classifying brain tissue into GM, WM, and CSF. It corrects some of the problems with reliability and computational inefficiency previously observed for operator-dependent approaches to segmentation.     

Improved cerebellar tissue classification on magnetic resonance images of brain

Purpose

To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.

Materials and Methods

Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices.

Results

The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (±2.34) and 93.04 (±2.41), demonstrating excellent performance of the proposed methodology.

Conclusion

The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object. J. Magn. Reson. Imaging 2009;29:1035–1042. © 2009 Wiley‐Liss, Inc.     

Contrast-enhanced MR imaging of metastatic brain tumor at 3 tesla: utility of T(1)-weighted SPACE compared with 2D spin echo and 3D gradient echo sequence.

We evaluated the newly developed whole-brain, isotropic, 3-dimensional turbo spin-echo imaging with variable flip angle echo train (SPACE) for contrast-enhanced T(1)-weighted imaging in detecting brain metastases at 3 tesla (T). Twenty-two patients with suspected brain metastases underwent postcontrast study with SPACE, magnetization-prepared rapid gradient-echo (MP-RAGE), and 2-dimensional T(1)-weighted spin echo (2D-SE) imaging at 3T. We quantitatively compared SPACE, MP-RAGE, and 2D-SE images by using signal-to-noise ratios (SNRs) for gray matter (GM) and white matter (WM) and contrast-to-noise ratios (CNRs) for GM-to-WM, lesion-to-GM, and lesion-to-WM. Two blinded radiologists evaluated the detection of brain metastases by segment-by-segment analysis and continuously-distributed test. The CNR between GM and WM was significantly higher on MP-RAGE images than on SPACE images (P<0.01). The CNRs for lesion-to-GM and lesion-to-WM were significantly higher on SPACE images than on MP-RAGE images (P<0.01). There was no significant difference in each sequence in detection of brain metastases by segment-by-segment analysis and the continuously-distributed test. However, in some cases, the lesions were easier to detect in SPACE images than in other sequences, and also the vascular signals, which sometimes mimic lesions in MP-RAGE and 2D-SE images, were suppressed in SPACE images. In detection of brain metastases at 3T magnetic resonance (MR) imaging, SPACE imaging may provide an effective, alternative approach to MP-RAGE imaging for 3D T(1)-weighted imaging.     

Comparison of longitudinal metabolite relaxation times in different regions of the human brain at 1.5 and 3 Tesla.

In vivo longitudinal relaxation times of N-acetyl compounds (NA), choline-containing substances (Cho), creatine (Cr), myo-inositol (mI), and tissue water were measured at 1.5 and 3 T using a point-resolved spectroscopy (PRESS) sequence with short echo time (TE). T(1) values were determined in six different brain regions: the occipital gray matter (GM), occipital white matter (WM), motor cortex, frontoparietal WM, thalamus, and cerebellum. The T(1) relaxation times of water protons were 26-38% longer at 3 T than at 1.5 T. Significantly longer metabolite T(1) values at 3 T (11-36%) were found for NA, Cho, and Cr in the motor cortex, frontoparietal WM, and thalamus. The amounts of GM, WM, and cerebrospinal fluid (CSF) within the voxel were determined by segmentation of a 3D image data set. No influence of tissue composition on metabolite T(1) values was found, while the longitudinal relaxation times of water protons were strongly correlated with the relative GM content.     

Measurement of glycine in gray and white matter in the human brain in vivo by 1H MRS at 7.0 T

The concentration of glycine (Gly) was measured in gray matter (GM) and white matter (WM) in the human brain using single‐voxel localized ¹H MRS at 7 T. A point‐resolved spectroscopy sequence with echo time = 150 ms was used for measuring Gly levels in various regions of the frontal and occipital lobes in 11 healthy volunteers and one subject with a glioblastoma. The point‐resolved spectroscopy spectra were analyzed with LCModel using basis functions generated from density matrix simulations that included the effects of volume localized radio‐frequency and gradient pulses. The fraction of GM and white matter within the voxels was obtained from T₁‐weighted image segmentation. The metabolite concentrations within the voxels, estimated with respect to the GM + WM water concentrations, were fitted to a linear function of fractional GM content. The Gly concentrations in pure GM and white matter were estimated to be 1.1 and 0.1 mM, with 95% confidence intervals 1.0–1.2 and 0.0–0.2, respectively. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.     

Measurement of global brain atrophy in Alzheimer's disease with unsupervised segmentation of spin-echo MRI studies

In 16 patients with probable Alzheimer's disease (AD; NINDS criteria, age range 56-78 years), gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) absolute and fractional volumes were measured with an unsupervised multiparametric post-processing segmentation method based on estimates of relaxation rates R1, R2 (R1 = 1/T1; R2 = 1/T2) and proton density [N(H)] from conventional spin-echo studies (Alfano et al. Magn. Reson. Med. 1997;37:84-93). Global brain atrophy, and GM and WM fractions significantly correlated with Mini-Mental Status Examination and Blessed Dementia Scale scores. Compared with normals, brain compartments in AD patients showed decreased GM (-6.84 +/- 1.58%) and WM fractions (-9.79 +/- 2.47%) and increased CSF fractions (+58.80 +/- 10.37%). Changes were more evident in early-onset AD patients. In AD, measurement of global brain atrophy obtained by a computerized procedure based on routine magnetic resonance studies could complement the information provided by neuropsychological tests for the assessment of disease severity.     

Segmentation and volumetric analysis of the caudate nucleus in Alzheimer's disease

Objectives

A quantitative volumetric analysis of caudate nucleus can provide valuable information in early diagnosis and prognosis of patients with Alzheimer's diseases (AD). Purpose of the study is to estimate the volume of segmented caudate nucleus from MR images and to correlate the variation in the segmented volume with respect to the total brain volume. We have also tried to evaluate the caudate nucleus atrophy with the age related atrophy of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) in a group of Alzheimer's disease patients.

Methods

3D fast low angle shot (3D FLASH) brain MR images of 15 AD patients, 15 normal volunteers and 15 patients who had normally diagnosed MR images were included in the study. Brain tissue and caudate nuclei were segmented using the statistical parametric mapping package and a semi-automatic tool, respectively and the volumes were estimated. Volume of segmented caudate nucleus is correlated with respect to the total brain volume. Further, the caudate nucleus atrophy is estimated with the age related atrophy of WM, GM and CSF in a group of AD patients.

Results

Significant reduction in the caudate volume of AD patients was observed compared to that of the normal volunteers. Statistical analysis also showed significant variation in the volume of GM and CSF of AD patients. Among the patients who had normal appearing brain, 33% showed significant changes in the caudate volume. We hypothesize that these changes can be considered as an indication of early AD.

Conclusion

The method of volumetric analysis of brain structures is simple and effective way of early diagnosis of neurological disorders like Alzheimer's disease. We have illustrated this with the observed changes in the volume of caudate nucleus in a group of patients. A detailed study with more subjects will be useful in correlating these results for early diagnosis of AD.     

Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects

A highly reproducible automated procedure for quantitative analysis of serial brain magnetic resonance (MR) images was developed for use in patients with multiple sclerosis (MS). The intracranial cavity (ICC) was identified on standard dual-echo spin-echo brain MR images using a supervised automated procedure. MR images obtained from one MS patient at 24 time points in the course of a 1-year follow-up were aligned with the images of one of the time points. Next, the contents of the ICC in each MR exam were segmented into four tissues, using a self-adaptive statistical algorithm. Misclassifications due to partial voluming were corrected using a combination of morphologic operators and connectivity criteria. Finally, a connectivity detection algorithm was used to separate the tissue classified as lesions into individual entities. Registration, classification of the contents of the ICC, and identification of individual lesions are fully automatic. Only identification of the ICC requires operator interaction. In each MR exam, the program estimated volumes for the ICC, gray matter (GM), white matter (WM), white matter lesions (WML), and cerebrospinal fluid (CSF). The reproducibility of the system was superior to that of supervised segmentation, as evidenced by the coefficient of variation: CSF supervised 45.9% vs. automated 7.7%, GM 16.0% vs. 1.4%, WM 15.7% vs. 1.3%, and WML 39.5% vs 52.0%. Our results demonstrate that this computerized procedure allows routine reproducible quantitative analysis of large serial MRI data sets.     

Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers

PURPOSE: To quantitate neuroanatomic parameters in healthy volunteers and to compare the values with normative values from postmortem studies. MATERIALS AND METHODS: Magnetic resonance (MR) images of 116 volunteers aged 19 months to 80 years were analyzed with semiautomated procedures validated by means of comparison with manual tracings. Volumes measured included intracranial space, whole brain, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Results were compared with values from previous postmortem studies. RESULTS: Whole brain and intracranial space grew by 25%-27% between early childhood (mean age, 26 months; age range, 19-33 months) and adolescence (mean age, 14 years; age range, 12-15 years); thereafter, whole-brain volume decreased such that volunteers (age range, 71-80 years) had volumes similar to those of young children. GM increased 13% from early to later (6-9 years) childhood. Thereafter, GM increased more slowly and reached a plateau in the 4th decade; it decreased by 13% in the oldest volunteers. The GM-WM ratio decreased exponentially from early childhood through the 4th decade; thereafter, it gradually declined. In vivo patterns of change in the intracranial space, whole brain, and GM-WM ratio agreed with published postmortem data. CONCLUSION: MR images accurately depict normal patterns of age-related change in intracranial space, whole brain, GM, WM, and CSF. These quantitative MR imaging data can be used in research studies and clinical settings for the detection of abnormalities in fundamental neuroanatomic parameters.