Segmentation of human brain using structural MRI

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.     

Classification of schizophrenic patients and healthy controls using multiple spatially independent components of structural MRI data

Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia, which could potentially act as disease markers. Based on the findings of these meta-analyses, we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging (sMRI) data. Independent component analysis (ICA) was used to decompose gray matter density images into a set of spatially independent components. Spatial multiple regression of a region of interest (ROI) mask with each of the components was then performed to determine pathological patterns, in which the voxels were taken as features for classification. After dimensionality reduction using principal component analysis (PCA), a nonlinear support vector machine (SVM) classifier was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a 10-fold cross-validation strategy. Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia, which mainly included the prefrontal cortex (PFC) and subcortical regions respectively. It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone. Moreover, the two pathological patterns constitute a prefronto-subcortical network, suggesting that schizophrenia involves abnormalities in networks of brain regions.     

Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits “small-world” properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer’s disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.     

基于水平集的弱边缘医学超声图像分割算法

图像分割在医学超声图像的定量、定性分析中均扮演着重要的角色,并直接影响到后续的分析、处理工作。但在医学超声图像中,待分割的图像目标经常包括弱边缘,这为利用传统图像分割算法对目标进行分割带来了困难。本文通过对Chan-Vese水平集方法进行研究,利用零水平集曲线的平均边缘能量来对其演化速度进行控制,实现了对图像中目标弱边缘的检测。本文方法在对包含弱边缘的医学超声图像进行分割时,得到了很好的效果。     

Measurement of anesthetic uptake kinetics in the brain using 19F MRI and cross-correlation analysis after pulsed application

Object

We present a pilot study based on ¹⁹F-MRI to measure fast and slow wash-in and wash-out kinetics of volatile anesthetics in pig brain.

Method

The periodic administration of anesthetics in pulsed mode is used to enhance the sensitivity of the anesthetic concentration detection by ¹⁹F-MRI signal. Temporal correlation analysis allows mapping the kinetics time constants.

Results

The clear correlation response to anesthetics concentration changes was found in the brain region in comparison with fatty tissues.

Conclusion

The methodology may yield important pharmacological findings on regional effect of the anesthetics in brain and be a step towards human studies.     

Measurement and analysis of field-induced crystallographic texture using curved position-sensitive diffraction detectors

This paper outlines measurement and analysis methodologies created for determining the structural responses of electroceramics to an electric field. A sample stage is developed to apply electric fields to ceramic materials at elevated temperatures during neutron diffraction experiments. The tested voltages and temperatures range from ?20 kV to +20 kV and room temperature to 200 °C, respectively. The use of the sample environment for measuring the response of ferroelectric ceramics to an electrical stimulus is demonstrated on the instrument Wombat, a monochromatic neutron diffractometer employing a curved positive sensitive detector. Methodologies are proposed to account for the geometrical effects when vector fields are applied to textured materials with angularly dispersive detector geometries. Representative results are presented for the ferroelectric (Bi_1/2Na_1/2)TiO₃-6%BaTiO₃ (BNT-6BT) which show both phase transformation and ferroelectric domain texturing under the application of an electric field. This experimental and analysis approach is well suited for time-resolved measurements such as stroboscopic and in situ studies on a variety of electro-active materials.     

Relative assessment of brain iron levels using MRI at 3 tesla

High field MR (magnetic resonance) images can be made sensitive to the relative concentration of tissue iron through the use ofT*₂-weighted contrast. This has enabled tissue iron levels to be assessed noninvasively by quantification of transverse relaxation rates. High field MRI may provide a new method to investigate neurological diseases which result in alteration of brain iron levels in specific areas of the human brain. Parkinson's disease (PD) results in an increase in iron concentration within the lateral region of the substantia nigra (SN), and provides one potential application of this methodology. Preliminary results of our findings are that there is a significant difference in SN iron levels in PD patients compared with age-matched controls, assessed by quantification of the reversible line-broadening component of transverse relaxation rate,R₂.     

Post mortem brain temperature and its influence on quantitative MRI of the brain

Objective

MRI temperature sensitivity presents a major issue in in situ post mortem MRI (PMMRI), as the tissue temperatures differ from living persons due to passive cooling of the deceased. This study aims at computing brain temperature effects on the MRI parameters to correct for temperature in PMMRI, laying the foundation for future projects on post mortem validation of in vivo MRI techniques.

Materials and methods

Brain MRI parameters were assessed in vivo and in situ post mortem using a 3 T MRI scanner. Post mortem brain temperature was measured in situ transethmoidally. The temperature effect was computed by fitting a linear model to the MRI parameters and the corresponding brain temperature.

Results

Linear positive temperature correlations were observed for T₁, T₂* and mean diffusivity in all tissue types. A significant negative correlation was observed for T₂ in white matter. Fractional anisotropy revealed significant correlations in all gray matter regions except for the thalamus.

Discussion

The linear models will allow to correct for temperature in post mortem MRI. Comparing in vivo to post mortem conditions, the mean diffusivity, in contrast to T₁ and T₂, revealed additional effects besides temperature, such as cessation of perfusion and active diffusion.

     

MRI of ischemic lesions in gerbil brain

Brain temperature was varied during global forebrain ischemia in adult male Mongolian gerbils to produce a graded response to the ischemic insult. The severity of damage in the dorsal hippocampus was then quantified 4 days after the event withT ²-weighted 2DFT images and with histology. Statistically significant correlation was observed between magnetic resonance imaging (MRI) score and brain temperature and between MRI score and the area of the CA region in the dorsal hippocampus measured by histology.     

2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution

Objective

To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA).

Materials and methods

Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps.

Results

For LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA.

Conclusion

Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.

     

Presurgical brain mapping in epilepsy using simultaneous EEG and functional MRI at ultra-high field: feasibility and first results

Magnetic Resonance Materials in Physics, Biology and Medicine - The aim of this study was to demonstrate that eloquent cortex and epileptic-related hemodynamic changes can be safely and reliably...     

In vivo diffusion tensor mapping of the brain of squirrel monkey, rat, and mouse using single-shot STEAM MRI

The purpose was to assess the potential of half Fourier diffusion-weighted single-shot STEAM MRI for diffusion tensor mapping of animal brain in vivo. A STEAM sequence with image acquisition times of about 500 ms was implemented at 2.35 T using six gradient orientations and b values of 200, 700, and 1200 s mm^–2. The use of half Fourier phase-encoding increased the signal-to-noise ratio by 45% relative to full Fourier acquisitions. Moreover, STEAM-derived maps of the relative anisotropy and main diffusion direction were completely free of susceptibility-induced signal losses and geometric distortions. Within measuring times of 3 h, the achieved resolution varied from 600×700×1000 m³ for squirrel monkeys to 140×280×720 m³ for mice. While in monkeys the accessible white matter fiber connections were comparable to those reported for humans, detectable fiber structures in mice focused on the corpus callosum, anterior commissure, and hippocampal fimbria. In conclusion diffusion-weighted single-shot STEAM MRI allows for in vivo diffusion tensor mapping of the brain of squirrel monkeys, rats, and mice without motion artifacts and susceptibility distortions.     

Estimation of the number of EEG sources in the human brain using multidimensional directed information analysis

At present, various approaches are being used to analyze brain function. One of these is the analysis of EEGs (electroencephalogram), by which it is becoming possible to evaluate brain activity and abnormalities in the brain from the EEG observed on the scalp. There remain many unknown aspects of the source of EEGs and their mechanism of propagation. A clue to such an approach is causality analysis, whose objective is to analyze causality by quantifying correlation relations, including the directions of information flow, among measurement sites, based on multiple EEG series obtained from multiple sites on the scalp. The method should be applicable to the diagnosis of disease such as the detection of individual abnormalities and failure of information propagation. Consequently, we have proposed multidimensional directed information analysis as a means of causality analysis in which the flow of information among all signals is investigated. There are many multidimensional signals in nature, for which the number of information flow sources in unknown. It is very important to discover the number of sources. Therefore, we propose a method to estimate the number of information flow sources by multidimensional directed information analysis. In this paper, the effectiveness of the proposed method has been verified by a simulation, and the method has been applied to the EEG of a healthy subject and a patient with a cerebral organic disorder. As a result, we have found that the healthy subject had six information flow sources and the patient had nine. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 145(4): 38–45, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10218     

Smoke detection in open areas with texture analysis and support vector machines

Early and certain fire detection is one of the important issues to keep infrastructures safe. Especially, it becomes an urgent problem for open places such as port facilities, large factories, and power plants, due to its large harmful effect to the surrounding areas. In these places, direct detection of fire or flame has some difficulties because they are open and hence have problem to set sensor devices. Therefore, smoke is an important and useful sign to detect fire or flames robustly even in such cases. In this paper, we present a novel smoke detection method based on image information. First, we extract moving objects in an image sequence as smoke candidate regions in a preprocessing step. Since smoke has a characteristic pattern as image information, we focus on the texture pattern of smoke. Here, we use texture analysis to extract feature vectors of the images. To classify extracted areas of moving objects to smoke or nonsmoke, we use support vector machines (SVMs) with texture features as an input feature vector. Extraction of moving objects is sometimes easily and greatly affected by environmental conditions such as wind, background objects, and so forth. It obviously causes bad classification results. To solve this problem, we additionally accumulate the results of classification with SVM about time to obtain accurate extraction result of smoke regions under these conditions. Experimental results using real‐scene data show that our method works effectively under several different environmental conditions. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于粗糙集理论-主成分分析的Elman神经网络短期风速预测

为了解决传统静态前馈神经网络(FNN)在短期风速预测中易陷入局部最优值及动态性能的不足,引入Elman动态神经网络建立风速预测模型,采用主成分分析法(PCA)对原始风速数据进行特征提取以优化神经网络的输入,改进激励函数和网络结构以寻求函数收敛速度和预测精度的最优解。针对Elman神经网络预测模型在风速波动的峰值处预测误差较大及预测精度存在波动性,提出采用粗糙值理论对模型预测值进行修正与补偿,进一步提高预测精度。实验证明:所提出的方法能有效提高预测精度,增强神经网络模型的泛化能力,具有较好的实用性。     

小型水轮发电机组运行中的振动分析

结合石龙坝电厂水轮机组的实际,从各个角度阐述了水轮机组产生振动的原因.     

A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.     

混合BIST对象建模及结构分析

介绍一种基于谱分析的混合信号BIST分析方法,包括被测电路模型建立,即利用Volterra级数来描述,阐述这种BIST测试方案。以硬件开销较小的伪随机信号作为输入激励,通过谱估计方差较小的Welch算法进行谱分析,计算出Volterra频域核来进行故障诊断,可使测量过程高效、准确。最后利用MATLAB仿真分析验证了方案的可行性,使测试方案具有较好的实用性和通用性。     

Segmentation of medical images using a geometric deformable model and its visualization

An automatic segmentation method for medical images that uses a geometric deformable model is presented, and the segmented results are visualized with the help of a modified marching cubes algorithm. The geometric deformable model is based on evolution theory and the level set method. In particular, the level set method utilizes a new derived speed function to improve the segmentation performance. This function is defined by the linear combination of three terms, namely, the alignment term, the minimal-variance term, and the smoothing term. The alignment term makes a level set as close as possible to the boundary of an object. The minimal-variance term best separates the interior and exterior of the contour. The smoothing term renders a segmented boundary less sensitive to noise. The use of the proposed speed function can improve the segmentation accuracy while making the boundaries of each object much smoother. Finally, it is demonstrated that the design of the speed function plays an important role in the reliable segmentation of synthetic and computed tomography (CT) images, and the segmented results are visualized effectively with the help of a modified marching cubes algorithm.     

Functional and metabolic evaluation of the hypertrophied heart using MRI and31P-MRS

4. Conclusions Diastolic LV function and myocardial HEP metabolism are impaired only when LVH is caused by permanent pressure or volume overload, and not by a temporary increase in cardiac workload during part of the day as in elite athletes. Therefore, training-induced and pressure/volume-overload-induced LVH seem to represent different phenotypes of LVH, possibly related to genetic reprogramming which only occurs during permanent cardiac overload [17]. Moreover, there is an association between impaired LV diastolic function and altered myocardial HEP metabolism in patients with hypertension and in patients with aortic valve disease. Finally we did not find a correlation between myocardial HEP metabolism and LV mass in any of the groups studied. The latter indicates that LVH should be regarded as an epiphenomenon to cardiac overload, and not as a primary factor causing abnormal HEP metabolism.