癫痫病灶检测技术的研究进展

癫痫病灶检测技术已成为治疗难治性癫痫的一种重要手段,病灶的准确检测是保证手术安全、取得良好疗效的关键。本文综述了癫痫病灶检测技术的新进展,包括脑电图(electroencephalogram,EEG)、常规磁共振(magnetic resonance imaging,MRI)、功能性磁共振(functional magnetic resonance imaging,f-MRI)、同步脑电联合功能磁共振成像(electroencephalogram-functional magnetic resonance imaging,EEG-f MRI)、磁共振波谱分析(magnetic resonance spectroscopy,MRS)、脑磁图(magnetoencephalogram,MEG)、磁源性影像(magnetic source imaging,MSI)、单光子发射计算机断层扫描(single photon emission computed tomography,SPECT)、正电子发射断层扫描-计算机断层成像(positron emission tomography-computed tomography,PET-CT)以及偶极子定位(dipole localization method,DLM)等检测方法,并分析了每种方法的优缺点及发展方向。     

脑白质高信号与阿尔茨海默病的相关性

脑白质高信号(white matter hyperintensity,WMH)为磁共振成像(magnetic resonance imaging,MRI)中T2加权像(T2 weighted imaging,T2WI)或T2液体衰减反转恢复序列(fluid attenuation inversion recovery,...     

脑机接口中基于SOBI的EEG预处理

目的针对脑机接口(brain computer interface,BCI)中脑电信号(electroencephalography,EEG)包含的伪迹以及信号源可能服从多个高斯分布,本文提出一种基于二阶盲辨识(second-order blindidentification,SOBI)的盲源分离去除伪迹方法。方法首先,含有伪迹的多个导联EEG信号采用联合近似对角化和数据白化,计算出混合矩阵,同时分解成数目相等的若干个独立分量。然后,根据伪迹信号特有的直观特性,将分解出含有伪迹的独立分量置零,剩余分量通过混合矩阵,进行逆向投影重构,得到去除伪迹后EEG信号。最后,对3名实验者的实验数据,从处理时间和识别精度两方面进行检验。结果本文中提出的SOBI方法相比于常用的独立成分分析(independent component analysis,ICA),在单个样本处理时间上,分别缩短了1691 ms、1770 ms和2308 ms;在识别精度上,分别提高33%、5%和10%。结论 SOBI能快速有效地去除伪迹信号,为BCI中EEG的在线处理奠定了基础。     

怎样产生氢核密度ρ和T1、T2加权图像

磁共振成像(magnetic resonance imaging, MRI)的主要成像参数是自旋核密度ρ、T1、T2,但ρ、T1、T2并不能在磁共振(magnetic resonance,MR)中直接测得,它们是隐含在弛豫过程(relaxation process)样品的磁化强度矢量M在感应线圈中感应出的电压信号中.成像参数ρ、T1、T2只能通过人为手段提高权重,以MR信号的形式表现出来,这种获得成像参数的方法与其它成像有很大不同.     

MRI技术中T1、T2的物理学意义及临床应用

磁共振成像(magnetic resonance imaging,MRI)是利用射频(radio frequency,RF)电磁波对置于磁场中的含有自旋不为零的原子核的物质进行激发,发生核磁共振(nuclear magnetic resonance,NMR),用感应线圈采集共振信号,经计算机处理,建立的数字图像.本文介绍MRI技术中,决定图像质量的组织特性重要参数T1、T2的物理学意义及临床应用.     

MRI常规扫描联合DWI技术对慢性肾脏病的评估分析

目的:分析磁共振(Magnetic resonance imaging,MRI)常规扫描联合扩散加权成像(Diffusion weighted imaging,DWI)评估慢性肾脏病(Chronic kidney disease,CKD)的价值.方法:选取2019年4月至2021年4月医院收治的80例CKD患者及40例...     

经颅磁刺激同步干预的头皮脑电信号伪迹离线去除方法综述

经颅磁刺激(TMS)同步脑电图(EEG)技术(TMS-EEG)已成为脑科学研究的一项重要工具,但两者在同步应用时会在EEG信号中形成复杂伪迹,如何去除这些伪迹一直是困扰研究者们的问题。本文归纳了TMS干预所造成的EEG信号伪迹的类型,并简单介绍了在线处理方法,重点总结了针对不同伪迹的特点可以采用的离线伪迹去除或最小化方法,主要包括减法、主成分分析、独立成分分析等。已有的文献研究表明,现有方法可以较好地处理大部分伪迹,但是对于大伪迹的去噪效果仍有待提高。本文系统总结了近年来TMS-EEG研究中关于伪迹去除问题的有效处理方法,期望对于TMS-EEG同步研究人员在选择伪迹去除的方法上有一定的指导意义。     

生物敏感MRI造影剂的研究新进展

<正>磁共振成像(magnetic resonance imaging,MRI)以其非侵入性,无辐射损伤,无骨质伪影,安全性好,空间分辨率和对比度高,可向任意方位断层扫描等技术灵活性,成为了临床医学诊断的重要手段。但是,某些不同组织或肿瘤组织的弛豫时间差别较小,成像时很难获得足够的信号差,造成MRI诊断困难,不能进行动态扫描和测定器官的功能等缺陷,因此需在被检测对象中加入一类能改变氢质子弛豫时间的物质来提高磁共振成像的敏感性和分辨率,这种物质被称为磁共振成像造影剂。临床上50%以上的磁共振扫描需     

T1-T2双模纳米磁共振造影剂的研究进展

<正>磁共振成像(magnetic resonance imaging,MRI)是利用核磁共振原理,通过收集和分析水分子中氢质子的弛豫信号来对生物体内部结构进行成像的技术。与其他医学成像技术如荧光成像、超声成像、计算机断层扫描、单电子发射计算机断层扫描等相比,MRI具有许多独特的优势。     

多序列MRI扫描在腰椎压缩性骨折诊断中的应用价值研究

目的:研究多序列磁共振成像(multi-sequence magnetic resonance imaging,MRI)扫描在腰椎压缩性骨折诊断中的应用价值.方法:收集本院2019年3月至2020年5月收治的135例腰椎压缩性骨折患者临床资料.均行计算机断层摄影(Computed tomography,CT)与MRI检...     

A robust algorithm for removing artifacts in EEG recorded during FMRI/EEG study

The main purpose of this study was to propose a robust algorithm for removing artifacts from the electroencephalographic (EEG) data collected during magnetic resonance imaging (MRI). The core idea of the proposed method was to remove the main gradient artifacts by the maximum cross-correlation method and to remove the residual artifacts by the rolling-ball algorithm and lowpass filtering. The results showed that the proposed algorithm had a better performance and was robust in the sense that its performance was maintained when the sampling rate of EEG data was decreased from 10KHz to 200Hz.     

Development and evaluation of the sparse decomposition method with mixed over-complete dictionary for evoked potential estimation

A new method is developed to decompose a physiological signal into a summation of transient and oscillatory components, referred to as mixed over-complete dictionary based sparse component decomposition algorithm (MOSCA). Based on the characteristics of the transient evoked potential (EP) and the background noise, the mixed dictionary is constructed with an over-complete wavelet dictionary and an over-complete discrete cosine (DC) function dictionary, and the signal is separated by learning in this mixed dictionary with a matching pursuit (MP) algorithm. MOSCA is designed specifically for the separation of a desired transient EP from the existing spontaneous EEG or other background noise. The method was evaluated with several simulation tests in which EPs or simulated EPs were deeply masked in different strong noise backgrounds, and the recovered signal is similar to the original assumed EP with a high and stable correlation coefficient (CC). The method was then applied to estimate event related potential (ERP) in the classical oddball experiment, and the results confirmed that the trial number for a reliable ERP estimation might be greatly reduced by MOSCA.     

A pulse artifact removal method considering artifact variations in the simultaneous recording of EEG and fMRI

A simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can provide high spatiotemporal information of brain activity. However, a proper analysis of the EEG signals is often hindered by various artifacts. In particular, pulse artifact (PA) induced from the heartbeat of a subject interferes with reliable measurements of the EEG signal. A new PA removal method that takes into account the delay variation between the heartbeat and PA and the window size variation in PA is presented in order to improve the detection and suppression of PA in EEG signals. A PA is classified into either a normal PA or a deformed PA. Only normal PAs are averaged to generate a PA template that is used to remove PAs from the measured EEG signals. The performance of the proposed method was evaluated by simulated data and real EEG measurements from epilepsy patients. The results are compared with those from conventional methods.     

Removing electroencephalographic artifacts by blind source separation

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.     

基于变分模态分解的眼电伪迹去除

脑电信号可以反映人体大脑活动状态,精确地将脑内信息传递向外界,对脑科学研究具有重要的意义。在实际情况中,脑电信号采集的同时会带有一些噪声,而眼电伪迹的存在会严重干扰脑电信号。本研究尝试了一种基于变分模态分解的眼电伪迹去除方法。通过变分模态分解将采集到的脑电信号分解成K组模态分量;根据眼电伪迹的频率特点,选择出眼电伪迹所对应的模态分量,并将其去除后重新构建剩余的模态分量。结果表明通过对实验数据的处理,变分模态分解可以有效地将眼电伪迹去除,并维持脑电信号的特征。     

Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP

Eye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event‐related potential (ERP) waveforms. Different techniques have been suggested to remove these artifacts prior to ERP analysis. Independent component analysis (ICA) is suggested as an alternative method to “filter” eye movement artifacts out of the EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifact components is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on 10 s of EEG, on eye movement epochs, or on the complete EEG recording to the removal of eye movement artifacts by rejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. By selecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of components representing eye movement artifacts.     

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG–fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.     

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.     

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS?+?RLAF to be highly effective in improving EEG quality. Online AAS?+?RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3–45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25–63% improvement), and VEPs variability (16–44% improvement). Further, we found that EEG quality after online AAS?+?RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.     

磁共振成像中抑制伪影技术的研究进展

磁共振成像可对人体各部位多角度、多平面成像,具有高组织分辨力、空间分辨力、无硬性伪迹及无放射损伤等优点,在医学上得到了广泛的应用。然而,磁共振成像中所产生的伪影会严重影响MRI的质量及对病灶的精确定位。我们主要介绍了近年来抑制自旋回波平面成像技术中所产生的伪影及自主性运动伪影的研究进展。大量研究表明,怎样快速有效地抑制各种原因产生的伪影,仍然是研究人员所面临的一个非常棘手的问题。因此,还需不断寻求新的方法及思路来解决这一难题。