基于磁共振T1图像的正常人脑组织体积量化研究

目的 通过手动分割和自动分割得出正常人脑的总体积、脑脊液体积、灰质白质体积,分析不同性别的差异和随年龄变化的模式,同时给出可以得到较优结果的软件和参数。 方法 通过软件自动分割得到粗略的脑掩模后进行手动精细分割,然后用FSL-FAST分为灰质、白质和脑脊液,最后用SPSS 25.0。软件进行统计学分析。 结果 中国正常成人脑总体积约1263.24 ml,男性脑总体积（1313.84 ml）大于女性脑总体积（1173.11 ml）,差异有统计学意义。不同年龄的脑总体积、灰质、白质和脑内脑脊液体积差异有显著性。灰质白质比与年龄无相关性。男性脑总体积、灰质、白质体积均大于女性,三者随年龄增大而降低的模式相似。女性脑总体积、灰质、白质体积随年龄变化的模式相似,在约50岁时达到最大值,随后体积逐渐减小。 结论 不同性别人脑体积及灰质、白质、脑脊液体积随年龄变化作为鉴别正常脑体积变化与疾病导致的脑体积变化的依据。     

基于Gibbs随机场的有限混合模型改进与脑部MR图像的稳健分割

有限混合(FM)模型已经广泛地应用于图像分割，但是由于没有考虑空间信息，导致分割的结果对噪声很敏感，分割出的区域存在很多杂散的孤立点。本文Gibbs随机场理论的指导下，将空间信息引入FM模型，提出了改进的脑部MR图像分割算法。此外，由树形K平均聚类来估计初始参数，实现了全自动的图像分割。本研究进行了仿真MR图像和真实MR图像的分割实验，定量的数据分析表明，我们所提的改进算法对噪声不敏感，可以更精确地将脑部MR图像标记为灰质、白质与脑脊液三种组织类型。     

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.     

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 atrophy and cognition: Interaction with cerebrovascular pathology?

We examined the interaction of brain atrophy and white matter lesions (WML) with cognitive functioning in 605 patients (mean age, 58 ± 10; 76% men) with atherosclerotic disease from the Second Manifestations of ARTerial disease-MR substudy (SMART-MR study). Automated brain segmentation was used to quantify volumes of brain tissue, cerebrospinal fluid, and WML on MRI. Total brain, ventricular, and cortical gray matter volume were divided by intracranial volume (ICV). Neuropsychological tests assessing executive functioning and memory performance were performed and composite scores were calculated. We observed that smaller total brain volume, larger ventricular volume, and smaller cortical gray matter volume (all as % of ICV) were associated with worse executive performance and that this association became stronger with presence of brain infarcts or severe WML volume (P-values for interaction <0.05). No interaction between measures of brain volume and cerebrovascular pathology on memory performance was observed. Our findings suggest that patients with cerebrovascular pathology on MRI may be more vulnerable to impairment in executive functioning related to global as well as focal brain atrophy.     

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.     

Improvement of Partial Volume Segmentation for Brain Tissue on Diffusion Tensor Images Using Multiple-Tensor Estimation

To improve evaluations of cortical and subcortical diffusivity in neurological diseases, it is necessary to improve the accuracy of brain diffusion tensor imaging (DTI) data segmentation. The conventional partial volume segmentation method fails to classify voxels with multiple white matter (WM) fiber orientations such as fiber-crossing regions. Our purpose was to improve the performance of segmentation by taking into account the partial volume effects due to both multiple tissue types and multiple WM fiber orientations. We quantitatively evaluated the overall performance of the proposed method using digital DTI phantom data. Moreover, we applied our method to human DTI data, and compared our results with those of a conventional method. In the phantom experiments, the conventional method and proposed method yielded almost the same root mean square error (RMSE) for gray matter (GM) and cerebrospinal fluid (CSF), while the RMSE in the proposed method was smaller than that in the conventional method for WM. The volume overlap measures between our segmentation results and the ground truth of the digital phantom were more than 0.8 in all three tissue types, and were greater than those in the conventional method. In visual comparisons for human data, the WM/GM/CSF regions obtained using our method were in better agreement with the corresponding regions depicted in the structural image than those obtained using the conventional method. The results of the digital phantom experiment and human data demonstrated that our method improved accuracy in the segmentation of brain tissue data on DTI compared to the conventional method.     

Age-specific MRI templates for pediatric neuroimaging

This study created a database of pediatric age-specific magnetic resonance imaging (MRI) brain templates for normalization and segmentation. Participants included children from 4.5 through 19.5 years, totaling 823 scans from 494 subjects. Open-source processing programs (FMRIB Software Library, Statistical Parametric Mapping, Advanced Normalization Tools [ANTS]) constructed head, brain, and segmentation templates in 6-month intervals. The tissue classification (white matter [WM], gray matter [GM], cerebrospinal fluid) showed changes over age similar to previous reports. A volumetric analysis of age-related changes in WM and GM based on these templates showed expected increase/decrease pattern in GM and an increase in WM over the sampled ages. This database is available for use for neuroimaging studies (http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase).     

Age-Specific MRI Templates for Pediatric Neuroimaging

This study created a database of pediatric age-specific magnetic resonance imaging (MRI) brain templates for normalization and segmentation. Participants included children from 4.5 through 19.5 years, totaling 823 scans from 494 subjects. Open-source processing programs (FMRIB Software Library, Statistical Parametric Mapping, Advanced Normalization Tools [ANTS]) constructed head, brain, and segmentation templates in 6-month intervals. The tissue classification (white matter [WM], gray matter [GM], cerebrospinal fluid) showed changes over age similar to previous reports. A volumetric analysis of age-related changes in WM and GM based on these templates showed expected increase/decrease pattern in GM and an increase in WM over the sampled ages. This database is available for use for neuroimaging studies (http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase).     

Integrated Laplacian‐based phase unwrapping and background phase removal for quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) is a recently developed MRI technique that provides a quantitative measure of tissue magnetic susceptibility. To compute tissue magnetic susceptibilities based on gradient echoes, QSM requires reliable unwrapping of the measured phase images and removal of contributions caused by background susceptibilities. Typically, the two steps are performed separately. Here, we present a method that simultaneously performs phase unwrapping and HARmonic (background) PhasE REmovaL using the LAplacian operator (HARPERELLA). Both numerical simulations and in vivo human brain images show that HARPERELLA effectively removes both phase wraps and background phase, whilst preserving all low spatial frequency components originating from brain tissues. When compared with other QSM phase preprocessing techniques, such as path‐based phase unwrapping followed by background phase removal, HARPERELLA preserves the tissue phase signal in gray matter, white matter and cerebrospinal fluid with excellent robustness, providing a convenient and accurate solution for QSM. The proposed algorithm is provided, together with QSM and susceptibility tensor imaging (STI) tools, in a shared software package named ‘STI Suite’. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Partial volume mapping using magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary‐based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV‐MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary‐matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV‐MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T₁ resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi‐automatically constructed in vivo using k‐means clustering of MRF‐mapped relaxation times. Dictionary‐based PV‐MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV‐MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV‐MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.     

Amide proton transfer MRI differentiates between progressive multifocal leukoencephalopathy and malignant brain tumors: a pilot study

Background and purpose

Synthetic MR imaging (SyMRI) allows the reconstruction of various contrast images, including double inversion recovery (DIR), from a single scan. This study aimed to investigate the advantages of SyMRI by comparing synthetic DIR images with synthetic T2-weighted fluid-attenuated inversion recovery (T2W-FLAIR) and conventional DIR images.

Materials and methods

We retrospectively reviewed the imaging data of 100 consecutive patients who underwent brain MRI between December 2018 and March 2019. Synthetic DIR, T2W-FLAIR, T1-weighted, and phase-sensitive inversion recovery (PSIR) images were generated from SyMRI data. For synthetic DIR, the two inversion times required to suppress white matter and cerebrospinal fluid (CSF) were manually determined by two radiologists. Quantitative analysis was performed by manually tracing the region of interest (ROI) at the sites of the lesion, white matter, and CSF. Synthetic DIR, synthetic T2W-FLAIR, and conventional DIR images were compared on the basis of using the gray matter-to-white matter, lesion-to-white matter, and lesion-to-CSF contrast-to-noise ratios.

Results

The two radiologists showed no differences in setting inversion time (TI) values, and their evaluations showed excellent interobserver agreement. The mean signal intensities obtained with synthetic DIR were significantly higher than those obtained with synthetic T2W-FLAIR and conventional DIR.

Conclusion

Synthetic DIR images showed a higher contrast than synthetic T2WFLAIR and conventional DIR images.

     

一种基于ITK脑部MRI混合分割方法的设计与实现

背景：由于脑部MR图像中信息对比度不高,各种脑部组织的形状复杂等特点,分割方法的选择比较困难,单一的算法很难获得满意的分割结果。 目的：针对脑部MRI的特点综合利用现有的算法开发和定制有效的分割应用算法。 方法：根据邻域连接和Canny水平集分割算法的优缺点,结合图像特征,用邻域连接方法的分割结果作为Canny水平集分割算法的先验分割模型,借以确定出Canny算法的下限阈值,从而完成两种算法的混合分割。 结果与结论：采用实验所用混合方法得到的白质和灰质的分割结果,经与专家手工分割结果对比,证明该方法取得了较好的分割效果,从而证明综合利用现有的算法,不仅避免了重复劳动,还能开发和定制出更加有效的分割应用算法,具备很好的应用潜力。     

The segmented regional volumes of the cerebrum and cerebellum in boys with Tourette syndrome

Neuropathological deficits are an etiological factor in Tourette syndrome (TS), and implicate a network linking the basal ganglia and the cerebrum, not a particular single brain region. In this study, the volumes of 20 cerebral and cerebellar regions and their symmetries were measured in normal boys and TS boys by brain magnetic resonance imaging. Brain magnetic resonance images were obtained prospectively in 19 boys with TS and 17 age-matched normal control boys. Cerebral and cerebellar regions were segmented to gray and white fractions using algorithm for semi-automated fuzzy tissue segmentation. The frontal, parietal, temporal, and the occipital lobes and the cerebellum were defined using the semiautomated Talairach atlas-based parcellation method. Boys with TS had smaller total brain volumes than control subjects. In the gray matter, although the smaller brain volume was taken into account, TS boys had a smaller right frontal lobe and a larger left frontal lobe and increased normal asymmetry (left>right). In addition, TS boys had more frontal lobe white matter. There were no significant differences in regions of interest of the parietal, temporal, or the occipital lobes or the cerebellum. These findings suggest that boys with TS may have neuropathological abnormalities in the gray and the white matter of the frontal lobe.     

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。     

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

The diffusion‐weighted (DW) MR signal sampled over a wide range of b‐values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T₂ relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW‐MRI datasets, and to determine the optimal sub‐set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b‐values and 20 diffusion‐encoding directions. The pixel‐wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T₁‐weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI‐based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over‐fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b‐values of 5/10/500/1200 s/mm² and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information.     

Automatic segmentation of true color sectioned images using FMRIB Software Library: First trial in brain,gray matter,and white matter

Three-dimensional (3D) models of the brain made from magnetic resonance images (MRI) are used in various medical fields. 3D models assembled from grayscale color and low-resolution can be complemented with true color sectioned images of the Visible Korean. The purpose of this study is to apply the MRI automatic segmentation technique to the sectioned images. 3D models of the sectioned images, which have true color and high resolution, can be produced without manual segmentation. The Brain Extraction Tool and the Automated Segmentation Tool of the FMRIB Software Library (FSL) were chosen for automatic segmentation. Using those tools, true color sectioned images were reconstructed from gray 3D models of brain, gray matter, and white matter. Color 3D models of those structures were generated from the gray 3D models using MRIcroGL. The color 3D models made from the sectioned images revealed details of brain anatomy that could not be observed on the 3D models from MRI. This trial suggests that convergence of the MRI segmentation technique with color sectioned images is a time-efficient method for producing color 3D models of various structures. In future, the method of this study will be used for various sectioned images of cadavers. The resulting color sectioned images and 3D models will be made available to other researchers.     

基于数据融合的脑组织图像分割

随着医学影像技术的发展,我们可以用不同的成像方法对同一个脑断层得到多模态的核磁共振图像,针对脑组织分割的需要,文中介绍了一种基于数据融合的多模分割方法.算法首先用基于模糊C均值聚类(Fuzzy C-Means,FCM)的方法分别对单一模态的图像聚类进行分割,然后采用数据融合的方法得出最终的分割结果.实验结果表明,此方法能有效地分割出白质、灰质和脑脊液,并且分割精度要明显高于对单一模态图像的分割结果.     

Decreased cortical gray and cerebral white matter in male patients with familial bipolar I disorder

BACKGROUND: Previous MRI studies of bipolar disorder have failed to consistently demonstrate cortical gray or cerebral white matter tissue loss, as well as sulcal or ventricular enlargement. The inconsistencies are most likely due to the clinical and gender heterogeneity of the study populations as well as the different MRI acquisition and processing techniques. The objective of this study was to determine if there was a cortical gray matter and cerebral white matter deficit as well as sulcal and ventricular enlargement in a homogeneous sample of euthymic male patients with familial bipolar I disorder. METHODS: MRI tissue segmentation was utilized to obtain cortical gray matter, cerebral white matter, ventricular cerebrospinal fluid (CSF), and sulcal CSF volumes in 22 euthymic males with familial bipolar I disorder and 32 healthy male control subjects. RESULTS: Relative to the controls, the familial bipolar I patients demonstrated: (1) significant reductions of both cortical gray matter and cerebral white matter volumes; and (2) significant increases in both sulcal and ventricular CSF volumes. In the bipolar group, there was a significant negative correlation between cortical gray matter volume and sulcal CSF volume. LIMITATIONS: Small sample size, retrospective interviews, possible medication effects. CONCLUSIONS: These results provide evidence for significant cortical gray matter and cerebral white matter deficits and associated sulcal and ventricular enlargement in euthymic males with familial bipolar I disorder.