Real-time MR artifacts filtering during continuous EEG/fMRI acquisition

The purpose of this study was the development of a real-time filtering procedure of MRI artifacts in order to monitor the EEG activity during continuous EEG/fMRI acquisition. The development of a combined EEG and fMRI technique has increased in the past few years. Preliminary “spike-triggered” applications have been possible because in this method, EEG knowledge was only necessary to identify a trigger signal to start a delayed fMRI acquisition. In this way, the two methods were used together but in an interleaved manner. In real simultaneous applications, like event-related fMRI study, artifacts induced by MRI events on EEG traces represent a substantial obstacle for a right analysis. Up until now, the methods proposed to solve this problem are mainly based on procedures to remove post-processing artifacts without the possibility to control electrophysiological behavior of the patient during fMRI scan. Moreover, these methods are not characterized by a strong “prior knowledge” of the artifact, which is an imperative condition to avoid any loss of information on the physiological signals recovered after filtering. In this work, we present a new method to perform simultaneous EEG/fMRI study with real-time artifacts filtering characterized by a procedure based on a preliminary analytical study of EPI sequence parameters-related EEG-artifact shapes. Standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes included: high-performance acquisition device; electrodes/cap/wires/cables materials and geometric design; shielding box for EEG signal receiver; optical fiber link; and software. The effects of the RF pulse and time-varying magnetic fields were minimized by using a correct head cap wires-locked environment montage and then removed during EEG/fMRI acquisition with a subtraction algorithm that takes in account the most significant EPI sequence parameters. The on-line method also allows a further post-processing utilization.     

Gradient induced artifacts in simultaneous EEG-fMRI: Effect of synchronization on spiral and EPI k-space trajectories

The nature of the gradient induced electroencephalography (EEG) artifact is analyzed and compared for two functional magnetic resonance imaging (fMRI) pulse sequences with different k-space trajectories: echo planar imaging (EPI) and spiral. Furthermore, the performance of the average artifact subtraction algorithm (AAS) to remove the gradient artifact for both sequences is evaluated. The results show that the EEG gradient artifact for spiral sequences is one order of magnitude higher than for EPI sequences due to the chirping spectrum of the spiral sequence and the dB/dt of its crusher gradients. However, in the presence of accurate synchronization, the use of AAS yields the same artifact suppression efficiency for both pulse sequences below 80 Hz. The quality of EEG signal after AAS is demonstrated for phantom and human data. EEG spectrogram and visual evoked potential (VEP) are compared outside the scanner and use both EPI and spiral pulse sequences. MR related artifact residues affect the spectra over 40 Hz (less than 0.2 μV up to 120 Hz) and modify the amplitude of P1, N2 and P300 in the VEP. These modifications in the EEG signal have to be taken into account when interpreting EEG data acquired in simultaneous EEG-fMRI experiments.     

Time-delay estimation of acoustic emission signals using ICA

Acoustic emission (AE) analysis is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. One difficult problem of AE analysis is the separation and characterization of signal components when the signals from various sources and the way in which the signals were mixed are unknown. Recently, blind source separation by independent component analysis (ICA) has been used to solve these problems. The main purpose of this paper is to demonstrate the applicability of ICA to time-delay (T-D) estimation of two independent continuous AE sources on an aluminum beam. It is shown that it is possible to estimate T-Ds by ICA, and thus to locate two independent simultaneously emitted sources.     

Optimizing ICA in fMRI using information on spatial regularities of the sources

Spatial independent component analysis (ICA) is a well-established technique for multivariate analysis of functional magnetic resonance imaging (fMRI) data. It blindly extracts spatiotemporal patterns of neural activity from functional measurements by seeking for sources that are maximally independent. Additional information on one or more sources (e.g., spatial regularity) is often available; however, it is not considered while looking for independent components. In the present work, we propose a new ICA algorithm based on the optimization of an objective function that accounts for both independence and other information on the sources or on the mixing model in a very general fashion. In particular, we apply this approach to fMRI data analysis and illustrate, by means of simulations, how inclusion of a spatial regularity term helps to recover the sources more effectively than with conventional ICA. The improvement is especially evident in high noise situations. Furthermore we employ the same approach on data sets from a complex mental imagery experiment, showing that consistency and physiological plausibility of relatively weak components are improved.     

Evaluating gradient artifact correction of EEG data acquired simultaneously with fMRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has become a widely used application in spite of EEG perturbations due to electromagnetic interference in the MR environment. The most prominent and disturbing artifacts in the EEG are caused by the alternating magnetic fields (gradients) of the MR scanner. Different methods for gradient artifact correction have been developed. Here we propose an approach for the systematic evaluation and comparison of these gradient artifact correction methods. Exemplarily, we evaluate different algorithms all based on artifact template subtraction--the currently most established means of gradient artifact removal. We introduce indices for the degree of gradient artifact reduction and physiological signal preservation. The combination of both indices was used as a measure for the overall performance of gradient artifact removal and was shown to be useful in identifying problems during artifact removal. We demonstrate that the evaluation as proposed here allows to reveal frequency-band specific performance differences among the algorithms. This emphasizes the importance of carefully selecting the artifact correction method appropriate for the respective case.     

Integration of EEG source imaging and fMRI during continuous viewing of natural movies

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705–717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner.     

Independent component analysis in the presence of noise in fMRI

A noisy version of independent component analysis (noisy ICA) is applied to simulated and real functional magnetic resonance imaging (fMRI) data. The noise covariance is explicitly modeled by an autoregressive (AR) model of order 1. The unmixing matrix of the data is determined using a variant of the FastICA algorithm based on Gaussian moments. The sources are estimated using the principle of maximum likelihood by modeling the source densities as asymmetric exponential functions. Effect of dimensionality reduction on the effective noise covariance used, accuracy of the obtained mixing matrix and degree of improvement in estimating fMRI sources are investigated. The primary conclusions after using this method of evaluation are as follows: (a) weighting matrix estimates are similar for noisy and conventional ICA in the realm of typical fMRI data, and (b) source estimates are improved by 5% (as measured by the correlation coefficient) in realistic simulated data by explicitly modeling the source densities and the noise, even when just a simple white noise model is used.     

Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA

Combination of the wavelet transform and independent component analysis （ICA） was employed for blind source separation （BSS） of multichannel electroencephalogram （EEG）. After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.     

Simultaneous EEG and fMRI in the macaque monkey at 4.7 Tesla

Simultaneous electroencephalography (EEG)/functional magnetic resonance imaging (fMRI) acquisition can identify the brain networks involved in generating specific EEG patterns. Yet, the combination of these methodologies is hampered by strong artifacts that arise due to electromagnetic interference during magnetic resonance (MR) image acquisition. Here, we report corrections of the gradient-induced artifact in phantom measurements and in experiments with an awake behaving macaque monkey during fMRI acquisition at a magnetic field strength of 4.7 T. Ninety-one percent of the amplitude of a 10 microV, 10 Hz phantom signal could successfully be recovered without phase distortions. Using this method, we were able to extract the monkey EEG from scalp recordings obtained during MR image acquisition. Visual evoked potentials could also be reliably identified. In conclusion, simultaneous EEG/fMRI acquisition is feasible in the macaque monkey preparation at 4.7 T and holds promise for investigating the neural processes that give rise to particular EEG patterns.     

Separation of physiological very low frequency fluctuation from aliasing by switched sampling interval fMRI scans

Anesthetized children have dominant blood-oxygen-level-dependent (BOLD) signal sources presenting high-power fluctuations at very low frequencies (VLF <0.05 Hz). Aliasing of frequencies higher than critically sampled has been regarded as one probable origin of the VLF fluctuations. Aliased signal frequencies change when the sampling rate of the data is altered. In this study, the aliasing of VLF BOLD signal fluctuation was analysed by switching the repetition time (TR) of magnetic resonance (MR) images. Eleven anesthetized children were imaged at 1.5 T using TRs of 500 and 1200 ms. The BOLD signal sources were separated with independent component analysis (ICA). Occipital cortex signal sources had nonaliased VLF fluctuation ( approximately 0.03 Hz) in 9 of 11 subjects. Arterial signal sources failed to present stable power peaks at frequencies lower than 0.42 Hz presumably due to aliasing. Cerebrospinal fluid (CSF)-related signal sources showed nonaliased VLF in four subjects. In conclusion, the VLF BOLD signal fluctuation in the occipital cortex is a true physiological fluctuation, not a result of signal aliasing.     

Restoration of low resolution metabolic images with a priori anatomic information: 23Na MRI in myocardial infarction

A new iterative extrapolation image reconstruction algorithm is presented, which enhances low resolution metabolic magnetic resonance images (MRI) with information about the bounds of signal sources obtained from a priori anatomic proton ((1)H) MRI. The algorithm ameliorates partial volume and ringing artefacts, leaving unchanged local metabolic heterogeneity that is present in the original dataset but not evident at (1)H MRI. Therefore, it is ideally suited to metabolic studies of ischemia, infarction and other diseases where the extent of the abnormality at (1)H MRI is uncertain. The performance of the algorithm is assessed by simulations, MRI of phantoms, and by surface coil 23Na MRI studies of canine myocardial infarction on a clinical scanner where the injury was not evident at (1)H MRI. The algorithm includes corrections for transverse field inhomogeneity, and for the leakage of intense signals into regions of interest such as 23Na MRI signals from ventricular blood ringing into the myocardium. The simulations showed that the algorithm reduced ringing artefacts by 15%, was stable at low SNR ( approximately 7), but is sensitive to the positioning of the (1)H MRI boundaries. The 23Na MRI showed hyperenhancement of regions identified as infarcted at post-mortem histological staining. The areas of hyperenhancement were measured by five independent observers in four 23Na images of infarction reconstructed with and without the algorithm. The infarct areas were correlated with areas determined by post-mortem histological staining with coefficient 0.85 for the enhanced images, compared to 0.58 with the conventional images. The scatter in the amplitude and in the area measurements of ischemia-associated hyper-enhancement in 23Na MRI was reduced by the algorithm by 1.6-fold and by at least 3-fold, respectively, demonstrating its ability to substantially improve quantification of the extent and intensity of metabolic changes in injured tissue that is not evident by (1)H MRI.     

Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing

A robust feature extraction scheme for the rolling element bearing (REB) fault diagnosis is proposed by combining the envelope extraction and the independent component analysis (ICA). In the present approach, the envelope extraction is not only utilized to obtain the impulsive component corresponding to the faults from the REB, but also to reduce the dimension of vibration sources included in the sensor-picked signals. Consequently, the difficulty for applying the ICA algorithm under the conditions that the sensor number is limited and the source number is unknown can be successfully eliminated. Then, the ICA algorithm is employed to separate the envelopes according to the independence of vibration sources. Finally, the vibration features related to the REB faults can be separated from disturbances and clearly exposed by the envelope spectrum. Simulations and experimental tests are conducted to validate the proposed method.     

基于独立成分分析的全波核磁共振信号噪声滤除方法研究

核磁共振测深(MRS)探水仪探测到的纳伏级微弱信号极易受到各种环境噪声的干扰, 严重影响信号特征参数的准确提取, 导致后续反演解释错误率增高. 针对这一难题, 提出了基于独立成分分析的快速固定点算法进行信噪分离. 首先分析了该算法用于全波MRS信号消噪的适用性; 其次, 采用数字正交法解决欠定盲源分离问题, 提出了频谱校正法实现分离信号幅值的有效恢复. 仿真结果表明, 该算法能够有效地实现全波MRS信号的信噪分离, 且数据拟合后初始振幅和弛豫时间的相对误差小于± 5.00%; 通过与其他经典算法的对比分析, 进一步证明了该算法消噪性能的优越性. 将该算法应用到野外实测信号处理, 结果证明其能有效滤除环境噪声.     

高光谱遥感图像光谱解混的独立成分分析技术

高光谱遥感在对地球陆地、海洋、大气的观测中发挥着重要作用,高光谱遥感图像分析的关键是提取像元光谱内部各物质成分及其含量,即光谱解混。独立成分分析提供了一种先进的技术手段,在很少先验知识的前提下,实现端元(物质成分)光谱及其丰度(含量)的同时提取。但丰度约束破坏了各成分独立的前提条件,导致了独立成分分析的局限性。针对这一问题,提出了丰度约束下总体相关性最小化的解决方案,并指出总体相关性最小化下的理想角度,通过设计角度修正的独立成分分析算法把各成分调整到理想角度上。利用模拟数据与真实数据算法进行检验,结果表明：经过角度修正后,独立成分分析突破了原有的局限性,有助于进一步提高独立成分分析技术在光谱分析中的有效性。     

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images.

Methods for brain tissue classification or segmentation of structural magnetic resonance imaging (MRI) data should ideally be independent of human operators for reasons of reliability and tractability. An algorithm is described for fully automated segmentation of dual echo, fast spin-echo MRI data. The method is used to assign fuzzy-membership values for each of four tissue classes (gray matter, white matter, cerebrospinal fluid and dura) to each voxel based on partition of a two dimensional feature space. Fuzzy clustering is modified for this application in two ways. First, a two component normal mixture model is initially fitted to the thresholded feature space to identify exemplary gray and white matter voxels. These exemplary data protect subsequently estimated cluster means against the tendency of unmodified fuzzy clustering to equalize the number of voxels in each class. Second, fuzzy clustering is implemented in a moving window scheme that accommodates reduced image contrast at the axial extremes of the transmitting/receiving coil. MRI data acquired from 5 normal volunteers were used to identify stable values for three arbitrary parameters of the algorithm: feature space threshold, relative weight of exemplary gray and white matter voxels, and moving window size. The modified algorithm incorporating these parameter values was then used to classify data from simulated images of the brain, validating the use of fuzzy-membership values as estimates of partial volume. Gray:white matter ratios were estimated from 20 twenty normal volunteers (mean age 32.8 years). Processing time for each three-dimensional image was approximately 30 min on a 170 MHz workstation. Mean cerebral gray and white matter volumes estimated from these automatically segmented images were very similar to comparable results previously obtained by operator dependent methods, but without their inherent unreliability.     

Special designed RF-antenna with integrated non-invasive carbon electrodes for simultaneous magnetic resonance imaging and electroencephalography acquisition at 7T

The construction of a high quality MR RF-antenna with incorporated EEG electrodes for simultaneous MRI and EEG acquisition is presented. The antenna comprises an active decoupled surface coil for receiving the MR signal and a whole body coil for transmitting the excitation RF pulses. The surface coil offers a high signal-to-noise ratio required for fMRI application and the whole body coil has a good B(1) excitation profile, which enables the application of homogeneous RF pulses. Non-invasive carbon electrodes are used in order to minimise the magnetic susceptibility artefacts that occur upon application of conductive materials. This dedicated set-up is compared to a standard set-up being a linear birdcage coil and commercially available Ag/AgCl electrodes. As the acquired EEG signals are heavily disturbed by the gradient switching, intelligent filtering is applied to obtain a clean EEG signal.     

Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment

Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed.     

In vivo determination of the time and location of mucoadhesive drug delivery systems disintegration in the gastrointestinal tract

OBJECTIVE: The objective of this study was to use magnetic resonance imaging (MRI) to detect the time when and the location at which orally delivered mucoadhesive drugs are released. MATERIALS AND METHODS: Drug delivery systems comprising tablets or capsules containing a mucoadhesive polymer were designed to deliver the polymer to the intestine in dry powder form. Dry Gd-DTPA [diethylenetriaminepentaacetic acid gadolinium(III) dihydrogen salt hydrate] powder was added to the mucoadhesive polymer, resulting in a susceptibility artifact that allows tracking of the application forms before their disintegration and that gives a strong positive signal on disintegration. Experiments were performed with rats using T(1)-weighted spin-echo imaging on a standard 1.5-T MRI system. RESULTS: The susceptibility artifact produced by the dry Gd-DTPA powder in tablets or capsules was clearly visible within the stomach of the rats and could be followed during movement towards the intestine. Upon disintegration, a strong positive signal was unambiguously observed. The time between ingestion and observation of a positive signal was significantly different for different application forms. Quantification of the remaining mucoadhesive polymer in the intestine 3 h after observed release showed significant differences in mucoadhesive effectiveness. CONCLUSION: MRI allows detection of the exact time of release of the mucoadhesive polymer in vivo, which is a prerequisite for a reliable quantitative comparison between different application forms.     

基于ICA的X射线医学图像目标提取

X射线医学成像能观察到患者体内病变组织,对医学诊断有重要参考价值。针对传统医学X射线图像噪声强、层次感差和器官组织重叠的问题,提出利用多能谱X射线成像结合独立成分分析(independent component analysis, ICA)进行图像去噪和目标提取。首先ICA结合稀疏编码收缩法对图像降噪预处理以保证目标提取精度;然后根据图像中各目标组成特性,分离图像中每个像素对应的目标厚度矩阵;最后ICA以盲分离理论获得收敛矩阵重建出目标对象。在ICA算法中,借助于主观评价标准,发现当收敛次数大于40时目标分离成功;当幅值尺度在[25, 45]区间内,目标图像对比度高且失真较小。同时,通过观测实验得到的三维峰值信噪比图表明：ICA算法中收敛次数和幅值对图像质量有较大影响,当重建图像的对比度和边缘信息均达到较好效果时,收敛次数与幅值为85和35。