磁共振并行成像技术综述

介绍了常用的磁共振成像加速方法,其中并行成像尤为重要,因为它可以和其他加速方法互补;还介绍了5种常见的并行成像方法:利用局部灵敏度的部分并行成像、灵敏度编码、空间谐波同时获取、泛化自校正部分并行获取、与基于阵列线圈灵敏度的并行编码与复原.最后,讨论了这5种方法的互相联系,以及在工业上的应用,并展望了动态成像和非笛卡尔坐...     

瞬态磁共振弹性成像的仿真与算法研究

磁共振弹性成像技术可以量化人体组织弹性属性。磁共振弹性成像的弹性波激励方式有稳态和瞬态两种。由于人体组织的特殊性,弹性波在组织中传播情况复杂。文章通过建立一个磁共振弹性成像的有限元仿真平台,对瞬态磁共振弹性成像技术进行数值建模,并针对瞬态磁共振弹性成像设计了基于到达时间的弹性重构算法。通过 MATLAB编程处理仿真得到的瞬态磁共振弹性成像数据,最终导出瞬态磁共振弹性成像的结果。数值平台仿真的稳态和瞬态磁共振弹性成像对比实验表明,瞬态磁共振弹性成像对于反射和衍射具有较好的抗干扰能力,对于深处的小尺寸组织,具有较高的灵敏度和较好的精准度,而基于到达时间的算法能够实现瞬态磁共振弹性成像的弹性重构。     

基于磁敏感的磁兼容金属植入物的正对比磁共振成像

在磁共振成像中,磁共振兼容金属植入物的磁化率较高,从而导致图像信号的缺失与失真,严重影响装置的精准定位和评估。2014年,有学者提出一种正对比成像方法,利用偏移180°回聚脉冲的自旋回波序列,结合特有的定量磁化率重建算法,重建出近距离放射颗粒的正对比图像。在此基础上,文章对该方法进行进一步扩展,在西门子3 T人体磁共振成像系统上,将该方法应用到活检针和过滤器等较大金属介入装置的正对比成像。活检针和过滤器水模磁共振成像实验结果表明,这种正对比成像方法可应用于较大的金属装置,有效地抑制了金属伪影,获得清晰的正对比图像,提高了装置定位和评估的精准度。     

基于多通道并行采集的部分可分离函数高分辨动态磁共振成像

部分可分离函数(Partial Separability,PS)是一种高分辨动态磁共振稀疏成像模型,可以对心脏等运动目标实现高 分辨动态成像,然而该模型需要充足的扫描数据才能进行图像重建,因此存在扫描时间较长的缺点。文章在 PS 模型基础 上,利用并行成像原理对磁共振数据进行降采样,从而缩短 PS 模型的扫描时间。数学仿真和载体实验结果表明,该方法 可以准确重建出高时空分辨率磁共振图像并且将扫描速度提高 2～3 倍。     

基于鲁棒估计的并行磁共振成像中部分数据重建算法

部分傅立叶数据重建和多通道并行成像是两种有效的磁共振快速成像技术,两种技术都是通过减少梯度编码步数来缩短数据获取时间.结合技术相比于单项技术可以更加有效地提高扫描速度.但是,由于在部分数据恢复过程中由于共轭对称性的破坏会产生破坏数据并且带来相位偏移,使得在之后进行的并行重建过程产生误差,造成重建图像产生伪影.文中提出了...     

定量磁共振图像的深度学习重建方法进展

磁共振成像是一种应用广泛的无创医学成像方法,因其丰富的软组织对比度可以成像人体几乎所有内部结构,包括器官、骨骼、肌肉和血管,已成为临床医学影像诊断的利器。然而磁共振成像存在两大公认的瓶颈：成像速度慢、扫描操作烦琐。深度学习给磁共振成像带来莫大的契机,给传统磁共振加速成像带来新的可能。鉴于该领域的快速发展性质,本文旨在总结文献中报道的大量深度学习和磁共振图像重建相结合的方法,以更好地了解该领域。本文简单介绍磁共振成像在多通道线圈接收的并行加速和压缩感知加速下的深度学习重建方法,其中单对比度图像可通过多通道接收线圈提供的冗余度用于加速,多对比度图像可额外使用不同对比度图像这一维度用于加速,动态图像与多对比度图像类似可额外使用时间维度用于加速,本文也将介绍深度学习在这些方面的发展。随着磁共振成像近年来由定性多对比度成像向定量多参数成像的发展,其中定量成像的图像中可内含多对比度图像,如何借用深度学习提供的能力将定性多对比度图像映射到参数图也是一个难点,近年来这一方向也获得了较快的发展,文中也将针对这方面内容进行调研并介绍。针对上述内容,分别介绍国际研究现状和国内研究现状,拟更好地总结国内外研究的发展的异同和趋势。最后对深度学习助力定量磁共振成像方面进行了展望。     

汉语认知数据库的设计与实现

随着磁共振成像技术在人类认知研究中的广泛应用,神经信息学家已经积累了大量的脑科学实验数据.如何有效地纽织和共享这些数据,并在此基础上发现新的数据结构、揭示隐藏的逻辑关系,成为当前科学研究的趋势.汉语认知数据库是2002年10月为此目的建立的,其主要目标是使从事神经科学研究者可以方便的共享功能磁共振脑成像数据.     

扩散磁共振图像的大脑微结构成像研究综述

微结构成像是基于扩散磁共振成像在活体内表征人脑组织微结构特性的技术，现已被广泛应用于神经科学研究和临床应用中.微结构成像模型将不同组织特性和磁共振信号相关联以估算组织的微观特性.本文首先介绍微结构成像的研究现状及不足，之后从信号模型与多隔室模型两个角度综述了经典的大脑微结构成像方法，总结这两类模型存在的优点及不足，并介绍微结构量化分析方法，最后对微结构成像技术进行总结和展望.     

采用贝叶斯决策概率型DT-MRI的脑白质纤维追踪成像

弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像可无创重建脑白质纤维的三维结构,而现有追踪成像方法一般仅考虑局部纤维的弥散倾向,对纤维束几何结构的综合考虑不足,为此提出一种贝叶斯决策概率型的纤维追踪成像算法.该算法通过纤维束当前体素的弥散张量方向和纤维束几何结构信息,利用贝叶斯决策理论估算追踪下一体素的方向概率分布;按照概率分布对纤维束进行加权采样,重建纤维束的三维结构图像.最后利用文中算法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验.仿真和实验结果表明,该算法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强.     

基于自适应稀疏表达的并行磁共振重建方法

为精确地进行并行磁共振成像,文章利用字典学习的强大捕捉细节和稀疏开发能力,提出了一种基于自适应稀疏表达的重建方法。该方法将并行磁共振重建问题转化为最小化由字典学习和数据拟合项构成的目标函数,并采用了分而治之的方案求解未知变量。为验证其有效性,将该方法与目前主流的两种方法在人体实际磁共振数据上进行了测试。测试结果显示,文章提出的方法能在抑制图像噪声的同时较好地保存图像细节。     

基于MRI图像的阿尔茨海默症患者脑网络特征识别算法

针对通过脑成像对阿尔茨海默症（AD）进行人工识别存在主观性、易误诊的问题,提出了一种基于核磁共振成像（MRI）图像构建脑网络对AD进行自动识别的方法。首先,把MRI图像叠加并进行结构块划分,并通过计算任意两个结构块之间的结构相似性（SSIM）来构造网络;然后,利用复杂网络理论提取结构参数,并将其作为机器学习算法的输入实现AD的自动识别。分析发现双参数特别是节点介数和边介数作为输入时分类效果最优,进一步研究发现MRI图像划分为27个结构块时分类效果最优,对于加权网络和无权网络的准确率分别最高可达91.04%和94.51%。实验结果表明,基于MRI结构块划分构建的结构相似性复杂网络能够对AD进行准确率更高的识别。     

T-test based Alzheimer’s disease diagnosis with multi-feature in MRIs

Multimedia Tools and Applications - Diagnosing Alzheimer’s disease (AD) with magnetic resonance imaging (MRI) has attracted increasing attention. In this paper, we propose a new feature...     

基于计算机辅助诊断技术的阿尔兹海默症早期分类研究综述

阿尔兹海默症（AD）作为主要的神经退行性疾病之一,已成为导致痴呆问题最常见的原因。截至目前,尚缺乏有效的针对性治疗药物和阻止疾病发展的有效治疗方式。随着计算机技术的不断发展,将计算机辅助诊断技术工具用于AD早期分类研究将为临床医生提供重要帮助。综述近些年来将传统机器学习和深度学习技术等手段用于AD的早期诊断分类的研究,研究样本主要为脑部神经成像数据（如MRI、PET）、脑电图（EEG）等生物标记物,结合机器学习方法对AD早期诊断进行分类研究。首先分析了将机器学习方法用于AD早期分类的应用,对比了采用不同算法的分类情况;其次,对比了针对受试者不同生物标记物以及采用单模态或不同模态组合方式用于AD早期分类的研究;最后介绍了AD分类面临的挑战并提出了未来的研究方向。     

结合DCGAN与LSTM的阿兹海默症分类算法

针对传统的阿兹海默症（AD）分类3D模型参数过多以及2D模型缺乏连续性特征的问题,提出了一种结合2D卷积神经网络与长短时记忆网络的脑部核磁共振成像（MRI）图像分类算法。利用深度卷积生成对抗网络（DCGAN）,卷积层能够在无标签的情况下自动提取到图像特征。首先以无监督的方式训练卷积神经网络;然后将MRI图像序列转换为特征序列,再输入到长短时记忆网络进行训练;最后结合特征序列与LSTM的隐藏状态进行分类。实验结果显示,相比3D模型,该算法有着更少的参数,对于NC与AD的分类达到了93.93%的准确率,对于NC与MCI的分类达到了86.27%的准确率。     

基于3D-ResNet的阿尔兹海默症分类算法研究

阿尔兹海默症(AD)是一种不可逆的神经退行性大脑疾病，也是老年人群中最常见的痴呆症。人工分类阿尔兹海默症的核磁共振影像(MRI)存在分类延迟和分类耗时等问题。随着人口老龄化的日趋严重，准确而快速地分类出阿尔兹海默症患者具有重要的研究意义。将卷积神经网络(CNN)技术和核磁共振成像技术相结合，设计了一个3D-ResNet算法用于AD分类，在验证集上取得了98.39%的准确性、96.74%的敏感性和99.99%的特异性，在测试集上取得了97.43%的准确性、94.92%的敏感性和99.99%的特异性，每个患者的分类时间是0.23 s。此外，针对AD的发病机制尚不明确的问题，通过类激活映射(CAM)技术来可视化与AD相关的脑部区域。     

Ontology driven decision support for the diagnosis of mild cognitive impairment

In recent years, mild cognitive impairment (MCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease (AD), and the diagnosis of MCI can alert patient to carry out appropriate strategies to prevent AD. To avoid subjectivity in diagnosis, we propose an ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI). In this approach, we encode specialized MRI knowledge into an ontology and construct a rule set using machine learning algorithms. Then we apply these two parts in conjunction with reasoning engine to automatically distinguish MCI patients from normal controls (NC). The rule set is trained by MRI data of 187 MCI patients and 177 normal controls selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) using C4.5 algorithm. By using a 10-fold cross validation, we prove that the performance of C4.5 with 80.2% sensitivity is better than other algorithms, such as support vector machine (SVM), Bayesian network (BN) and back propagation (BP) neural networks, and C4.5 is suitable for the construction of reasoning rules. Meanwhile, the evaluation results suggest that our approach would be useful to assist physicians efficiently in real clinical diagnosis for the disease of MCI.     

A CAD system design to diagnosize alzheimers disease from MRI brain images using optimal deep neural network

Memory related issues in brain are mainly caused by Alzheimer disease (AD) which is the most common form of dementia. This disease must be diagnosed in its prodromal stage known as Mild Cognitive Impairment (MCI) also it needs an accurate detection and classification technique. In this paper, a computer-aided diagnosis (CAD) system is implemented on Magnetic resonance imaging (MRI) data from ADNI database. This disease highly affects the Hippocampus and cerebrum regions which are normally found in the grey matter region of brain. At first, MNI/ICBM atlas space of every three dimensional MRI images are constructed using normalization procedure, then grey matter region of brain is extracted. Subsequently, feature extraction is done by two dimensional Gabor filter in three scales and eight orientations. Then, the proposed optimal Deep Neural Network (DNN) classifier is used to classify the images as Cognitive normal (CN), Alzheimer disease (AD), and Mild Cognitive Impairment (MCI). Here, DNN classifier is optimized by selecting optimal weight parameter using Enhanced Squirrel Search Algorithm. The experimental results prove an efficiency of the proposed method using MR images. The proposed algorithm beats existing techniques in terms of accuracy, sensitivity, and specificity.

     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

Relationship between tau,neuroinflammation and atrophy in Alzheimer's disease: The NIMROD study

In addition to beta-amyloid accumulation, misfolded tau and activated microglia are also present in Alzheimer's disease (AD). It is important to study the relationship amongst these pathologies in vivo and their effects on the cognitive deficits for developing effective trails and future therapeutic or preventive strategies for AD. To investigate the relationships amongst different pathologies in AD, in particular how they interact resulting in cognitive impairments, we conducted a study of sixty-six subjects (15 AD, 24 Mild Cognitive Impairment (MCI) and 27 similarly aged healthy controls), who underwent standardised clinical and neuropsychological assessments followed by dynamic PET using [¹⁸F]AV1451 (tau) and [¹¹C]PK11195 (activated microglia) and multimodal 3T MRI. MCI patients also underwent [¹¹C]PIB (beta-amyloid) PET. We compared regional PET binding and grey matter atrophy amongst AD, amyloid positive MCI and controls, as well as their spatial distribution across different brain areas. We also applied a mediation analysis to infer the direct and indirect effects of tau, neuroinflammation and grey matter atrophy on cognitive functioning. We found increased [¹⁸F]AV1451 and [¹¹C]PK11195 binding as well as grey matter atrophy in AD, with a strong spatial overlap amongst these AD related biomarkers suggesting them interacting with each other. We demonstrated that both tau ([¹⁸F]AV1451) and neuroinflammation ([¹¹C]PK11195) have significant effects on cognition however their effects were fully mediated by grey matter atrophy. No mediation effect between tau and neuroinflammation were found with respect to cognition. In conclusion, grey matter atrophy not only spatially overlapped with tau and microglia activity in AD, but also mediate them in affecting cognitive impairments. The mediation analysis enabled data fusion across multiple imaging modalities (PET and MRI) and multiple PET tracers. Our results have significant implications for trials targeting tau and inflammation, and future therapeutic or preventive strategies for AD.     

大脑多模态成像技术定量研究进展

现代医学成像技术是脑科学研究和脑疾病诊断的利器,不同模态的成像技术提供不同的信息可协同表征脑部结构和功能。其中定量成像技术着眼于和生理、物理相关的内在参量,旨在提供更精准的信息。本文以正电子发射扫描成像(positron emission tomography,PET)和磁共振成像(magnetic resonance imaging,MRI)两种生物医学成像模态为例,针对性地讨论它们在定量刻画大脑微观结构和功能领域的发展状况,目前尚存的关键技术问题和未来的可能发展方向。围绕定量MRI,从表观参数定量开始,介绍其中的单参数定量的现状和不足,以及目前多参数同时定量的发展动态;围绕微观参数定量,介绍针对髓鞘成像的两大方法,包括多组分T2定量和基于超短回波时间髓鞘直接成像,介绍磁共振定量成像特别是磁共振扩散成像的可比较性和可重复性研究。围绕定量PET,从最广泛的代谢动力学模型——房室模型开始介绍,对生理参数与示踪剂摄取量的关系进行了详细描述,展开到定量的误差来源包括模型选择、图像质量以及输入函数测量误差3个方面进行分析,介绍最新进展包括硬件设备、图像重建方法以及定量分析方法。最后对MRI定量、PET定量以及PET/MRI定量领域进行了展望。