PF-FICOTA-SENSE:一种MRI快速重构方法

如何实现快速磁共振成像(Magnetic resonance imaging, MRI)是MRI医学图像技术发展和应用的关键, 现有的快速MRI成像技术在成像速度及成像质量方面仍存在很大的提升空间.本文基于Contourlet变换, 对磁共振图像进行稀疏表示, 并结合传统PF-CS-SENSE框架, 提出一种基于Contourlet变换的组合MRI重构方法, 即PF-FICOTA-SENSE.考虑到组合MRI采样模式、低频数据的对称性以及Contourlet能更好地拟合曲线轮廓等因素, 进一步提出一种快速组合MRI方法, 该方法通过将低频部分重建由FICOTA重建替换为直接填零的傅里叶重建, 来实现快速重建.对比实验表明, 无论在MRI重构速度还是重构质量方面, 本文算法均能取得更好的性能.     

Diffusion, utilisation and regional variations in the use of CT and MRI in Sweden

To study the diffusion, access, use, and utilisation of Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) in Sweden a national survey was carried out by a written questionnaire mailed to all public and private hospitals and clinics in 1997. To get a survey of CT and MRI in other countries a request asking for number of units was sent to recognised authorities and individuals in selected countries. CT and MRI were introduced in Sweden in 1973 and 1984 respectively. The diffusion of CT and MRI in Sweden are amongst the highest in the world. Considering the small size of the country and the medical community (e.g. facilitating transparency in indications for use of CT and MRI) and the relatively homogeneous health care system (tax based finance system) the regional variations in access, use, and utilisation of CT and MRI were surprisingly large. For example: In 1996 the number of CT examinations per 100.000 inhabitants varied from 3.344 to 4.705 between the regions. The MRI examinations varied from 751 to 1.858. Some of the differences may be explained by demographic factors. Also, reimbursement policies by the county councils for CT and MRI examinations varied which gave different incentives for patient referrals. However, to explain most of the differences more research is needed. Factors like proportion of elderly amongst the populations in the regions and other socio-demographic factors, proportion of research orientation of examinations and national medical specialities, organisation of the health care, etc. may to a greater extent explain the differences.     

MRI脑肿瘤图像分割的深度学习方法综述

磁共振成像（MRI）作为一种典型的非侵入式成像技术，可产生高质量的无损伤和无颅骨伪影的脑影像，为脑肿瘤的诊断和治疗提供更为全面的信息，是脑肿瘤诊疗的主要技术手段。MRI脑肿瘤自动分割利用计算机技术从多模态脑影像中自动将肿瘤区（坏死区、水肿区、非增强肿瘤区和增强肿瘤区）和正常组织区进行分割和标注，对于辅助脑肿瘤的诊疗具有重要作用。本文对MRI脑肿瘤图像分割的深度学习方法进行了总结与分析，给出了各类方法的基本思想、网络架构形式、代表性改进方案以及优缺点总结等，并给出了部分典型方法在BraTS（multimodal brain tumor segmentation）数据集上的性能表现与分析结果。通过对该领域研究方法进行综述，对现有基于深度学习的MRI脑肿瘤分割研究方法进行了梳理，作为新的发展方向，MRI脑肿瘤图像分割的深度学习方法较传统方法已取得明显的性能提升，已成为领域主流方法并持续展现出良好的发展前景，有助于进一步推动MRI脑肿瘤分割在临床诊疗上的应用。     

用于神经外科手术的磁共振图像导航机器人的兼容性研究

分析了磁共振图像导航微创外科手术环境对机器人的要求,提出了磁共振图像导航机器人必须满足的结构和磁共振兼容性方面的特殊要求.探讨了MRI导航机器人的构型设计及结构优化设计问题,实现了机器人结构兼容性设计.将机器人部件进行分类,并设计了相应的磁共振兼容测试方法.通过在各部件兼容性测试中对水模信噪比的测试,实现了机器人磁共振兼容性.     

心脏MRI图像快速分割方法

在分析心脏MR图像特点的基础上,提出了先对心脏MRI图像进行K均值聚类,把K均值聚类后的图像作为特征图像,在特征上用Song和Chan提出的快速分割方法进行粗分割,再用粗分割的曲线作为水平集的初始曲线,在心脏MRI图像上用Chan和Vese方法进行细分割的心脏MR图像分割方法.并对Song和Chan快速算法中扫描图像的区域进行了改进,提高了分割速度.分割实验证明,用该方法能够快速、准确地分割心脏MRI图像.     

带标记线的心脏MRI图像分割方法*

在分析带标记线的心脏MRI特点的基础上,提出了利用Gabor滤波与Chan和Vese模型相结合对带标记线的心脏MRI图像进行分割的方法.通过对带标记线的心脏MRI图像进行Gabor 变换,把标记线分离出来,并且扩大心肌和血液之间的差异,再对变换后的图像用Chan和Vese模型进行分割.实验结果表明了该方法的有效性.     

在线生物文献磁共振图像识别方法

为实现在线生物文献磁共振成像(MRI)图像库的构建,利用图像特征的塔式梯度方向直方图(PHOG)和塔式关键词直方图(PHOW)进行互补特征表示,使用支持向量机对MRI图像与非MRI图像以及脑部MRI与非脑部MRI图像进行自动分类。实验结果表明,空间形状信息与局部分布信息融合的特征能提高图像分类的准确率,为构建在线文献中MRI图像库的知识系统提供技术支持。     

Improving performance and stability of MPC relevant identification methods

Model Predictive Control (MPC) Relevant Identification (MRI) methods are a good option for identification, if there is model structure mismatch. Herein a new MRI method, named Enhanced Multistep Prediction Error Method (EMPEM), is proposed. EMPEM combines the best characteristics of others MRI methods in a single algorithm. It was developed to identify either closed-loop or open-loop systems; its convergence and stability make it perform better than the other presented methods. To show the advantages of EMPEM, a comparison is made against two other methods (one MRI and one PEM). The statistical analysis indicates that in the cases studied, the performance and the robustness of the new method is equal or better than the other ones.     

基于CT和MRI的医学图像融合研究

随着数字医疗和智慧医疗技术的不断进步,应用计算机视觉技术进行医学图像处理也随之在不断发展和进步,常见的医学影像例如CT、MRI以及超声波等解剖成像技术,PET、核磁共振等功能成像技术,但是解剖成像技术和功能成像技术未有效结合,通过将医学有用信息和计算机视觉成像技术结合,最大化的呈现病理信息,对于医生进行病情诊疗具有十分重要的意义。通常计算机视觉技术中图像融合技术通过有效算法可以对医学成像进行优势互补,发现医疗诊断中有用和有价值的信息,对于弥补医学图像呈现技术缺陷和图像信息的缺失是十分有效的。利用计算机视觉技术对CT医学图像和MRI医学图像进行融合,可将CT图像显示骨质信息清晰,图像分辨率高的的优点与MRI图像软结构显示清晰的优点结合,形成优势互补,从而使得医学图像显示信息更加丰富,本文基于当前医学图像融合技术,对于CT图像和MRI图像两类图像的进行融合,对其关键的融合算法进行深入研究。而本文主要是利用小波变换算法对CT和MRI两类解剖成像进行融合,实验结果证明,具有较强鲁棒性。     

基于窄带水平集的曲线演化与左心室MRI图像分割

文章研究了基于窄带水平集的曲线演化方法并应用于心脏的MRI图像分割。分析了窄带的生成技术,提出了基于模板的距离函数生成方法;针对MRI图像的特点,给出了一种分割MRI图像的水平集速度函数,以处理图像中出现的区域灰度不一致性以及弱边界现象。MRI图像的分割实验证明了该文算法的有效性。     

基于改进的小波阈值技术MRI图像去噪

提出了一种改进的小波阈值处理的核磁共振成像（MRI）医学图像的去噪方法。结合图像的特点并利用小波系数的区域相关性,对小波阈值处理方法进行了改进,根据信号和噪声系数的不同分别处理,克服了传统小波变换不足。结果表明该方法在有效去除噪声的同时,较好保留了MRI图像的细节,有利于医学的诊断。     

阿尔茨海默氏症研究中的磁共振成像数据分析

首先综述了当前结构磁共振成像、功能磁共振成像和扩散张量磁共振成像3种技术在阿尔茨海默氏症研究中的现状;其次介绍和分析了上述3种磁共振成像数据的主要处理方法;最后介绍了基于阿尔茨海默氏症的神经影像数据库及其诊断平台的建设状况.另外,也提到了此课题在该领域的一些研究进展.     

Secure visual cryptography for medical image using modified cuckoo search

Multimedia Tools and Applications - Optimal secure visual cryptography for brain MRI medical image is proposed in this paper. Initially, the brain MRI images are selected and then discrete wavelet...     

Significant LOOP with clustering approach and optimization enabled deep learning classifier for the brain tumor segmentation and classification

Multimedia Tools and Applications - Magnetic resonance images (MRI) is the imperative imaging modality utilized in medical diagnosis tool for detecting brain tumors. The MRI possess the capability...     

基于PCNN和指数型模糊加权熵的医学图像融合

为了更好地对CT和MRI图像进行融合,提出了一种指数型模糊加权熵自适应融合规则和改进的PCNN区域信息融合规则在多小波基的框架下进行CT和MRI医学图像的融合方法。对待融合图像进行多小波基的分解,对不同频率分量采用不同融合算法。实验表明,算法明显优于其他融合算法。它提高了图像的清晰度,较大程度保留了细节信息,具有边缘信息突出,亮度对比度高的优点。     

A distributed computing system for magnetic resonance imaging: Java-based processing and binding of XML

Recently we have developed a Java-based heterogeneous distributed computing system for the field of magnetic resonance imaging (MRI). It is a software system for embedding the various image reconstruction algorithms that we have created for handling MRI data sets with sparse sampling distributions. Since these data sets may result from multi-dimensional MRI measurements our system has to control the storage and manipulation of large amounts of data. In this paper we describe how we have employed the extensible markup language (XML) to realize this data handling in a highly structured way. To that end we have used Java packages, recently released by Sun Microsystems, to process XML documents and to compile pieces of XML code into Java classes. We have effectuated a flexible storage and manipulation approach for all kinds of data within the MRI system, such as data describing and containing multi-dimensional MRI measurements, data configuring image reconstruction methods and data representing and visualizing the various services of the system. We have found that the object-oriented approach, possible with the Java programming environment, combined with the XML technology is a convenient way of describing and handling various data streams in heterogeneous distributed computing systems.     

Gaussian mixture model based segmentation methods for brain MRI images

Image segmentation is at a preliminary stage of inclusion in diagnosis tools and the accurate segmentation of brain MRI images is crucial for a correct diagnosis by these tools. Due to in-homogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A review of the Gaussian Mixture Model based segmentation algorithms for brain MRI images is presented. The review covers algorithms for segmentation algorithms and their comparative evaluations based on reported results.     

An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction

This paper propose a computerized method of magnetic resonance imaging (MRI) of brain binarization for the uses of preprocessing of features extraction and brain abnormality identification. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to extensive black background or large variation in contrast between background and foreground of MRI. We have proposed a binarization that uses mean, variance, standard deviation and entropy to determine a threshold value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.     

Depression detection from sMRI and rs-fMRI images using machine learning

Major Depression Disorder (MDD) is a common mental disorder that negatively affects many people’s lives worldwide. Developing an automated method to find useful diagnostic biomarkers from brain imaging data would help clinicians to detect MDD in its early stages. Depression is known to be a brain connectivity disorder problem. In this paper, we present a brain connectivity-based machine learning (ML) workflow that utilizes similarity/dissimilarity of spatial cubes in brain MRI images as features for depression detection. The proposed workflow provides a unified framework applicable to both structural MRI images and resting-state functional MRI images. Several cube similarity measures have been explored, including Pearson or Spearman correlations, Minimum Distance Covariance, or inverse of Minimum Distance Covariance. Discriminative features from the cube similarity matrix are chosen with the Wilcoxon rank-sum test. The extracted features are fed into machine learning classifiers to train MDD prediction models. To address the challenge of data imbalance in MDD detection, oversampling is performed to balance the training data. The proposed workflow is evaluated through experiments on three independent public datasets, all imbalanced, of structural MRI and resting-state fMRI images with depression labels. Experimental results show good performance on all three datasets in terms of prediction accuracy, specificity, sensitivity, and area under the Receiver Operating Characteristic (ROC) curve. The use of features from both structured MRI and resting state functional MRI is also investigated.

     

基于K-means和图割的脑部MRI分割算法

为了克服原始图割算法在用户选定的像素种子点较少情况下,目标边界容易出现错分这一现象,本文提出了基于K-means和图割(Graph cut,GC)算法相结合的交互式K-均值图割（K-means and graph cut,KMGC）算法,对脑部核磁共振图像（Magnetic resonance image,MRI） 进行交互式操作,该算法通过K-means聚类,对脑部MRI的灰度不均匀性进行了处理,在此基础上,再使用图割算法进一步对脑部MRI进行细化,从而达到有效地分割脑白质和脑 灰质的目的。本文分别在仿真和真实的脑部MRI数据上进行了大量的实验,分别从定量分析和定性分析两个角度对实验结果进行了分析,并与其他分割算法进行了对比,对比实验结果标明,KMGC算法能够有效地对脑部MRI进行分割,并在分割效果上优于其他算法。