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.     

Electrocardiographic gating and monitoring in NMR imaging

ECG gating and monitoring during NMR imaging may be achieved reliably by applying the principles in this tutorial. In order to use the ECG signal both for triggering and for patient monitoring it must have a prominent R-wave, while at the same time must have little artifact from gradient switches or the Lorentz voltage across the aorta, and not be significantly distorted by gradient switching artifacts. The twin goals of no image artifacts and minimal ECG artifacts may be achieved by the following means: (1) using ECG electrodes with minimal metal, (2) selecting electrodes and cables with no ferrous metals, (3) placing the limb electrodes close together, (4) placing the line between the limb electrodes and the leg electrode parallel to the magnetic flux lines and, if possible, parallel to the transverse component of the gradient flux lines, (5) keeping the area between the limb electrodes and the leg electrode small, (6) placing that area in the center of the imager and (7) twisting or braiding the cables. Following these principles allows artifact-free images and reliable ECG monitoring during ECG-gated NMR imaging examinations.     

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.     

DC artifact correction for arbitrary phase-cycling sequence

In magnetic resonance imaging (MRI), a non-zero offset in the receiver baseline signal during acquisition results in a bright spot or a line artifact in the center of the image known as a direct current (DC) artifact. Several methods have been suggested in the past for the removal or correction of DC artifacts in MR images, however, these methods cannot be applied directly when a specific phase-cycling technique is used in the imaging sequence. In this work, we proposed a new, simple technique that enables correction of DC artifacts for any arbitrary phase-cycling imaging sequences. The technique is composed of phase unification, DC offset estimation and correction, and phase restoration. The feasibility of the proposed method was demonstrated via phantom and in vivo experiments with a multiple phase-cycling balanced steady-state free precession (bSSFP) imaging sequence. Results showed successful removal of the DC artifacts in images acquired using bSSFP with phase-cycling angles of 0°, 90°, 180°, and 270°, indicating potential feasibility of the proposed method to any imaging sequence with arbitrary phase-cycling angles.     

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.     

Adaptive Filtration of Physiological Artifacts in EEG Signals in Humans Using Empirical Mode Decomposition

A new method for adaptive filtration of experimental EEG signals in humans and for removal of different physiological artifacts has been proposed. The algorithm of the method includes empirical mode decomposition of EEG, determination of the number of empirical modes that are considered, analysis of the empirical modes and search for modes that contains artifacts, removal of these modes, and reconstruction of the EEG signal. The method was tested on experimental human EEG signals and demonstrated high efficiency in the removal of different types of physiological EEG artifacts.     

超声全聚焦成像中等声程线伪影剔除方法

针对超声全聚焦成像算法中等声程线扩散产生的原理性伪影问题,提出了一种基于超声回波声场有效等声程线和图像强度分布特征相结合的伪影剔除方法。首先分析了全聚焦成像算法中等声程线产生伪影的机理,根据数据均方根误差自适应辨识有效等声程线;通过Canny算子获取全聚焦图像待处理候选区域,再根据候选区域中有效等声程线相交次数和图像强度的分布特征辨识缺陷图像和伪影,利用图像中强度最低像素值扩展填充剔除伪影后的区域,进而达到剔除伪影的目的。通过在不同直径通孔类和槽类标准缺陷试块上声场模拟实验,结果表明伪影剔除效果显著。同时,该方法对噪声也有一定的抑制能力,在不加滤波的条件下信号信噪比高于15 dB时就可获得较好的去伪影效果。     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

Artifact removal in Fourier-domain optical coherence tomography with a piezoelectric fiber stretcher

We describe an artifact removal setup swept-source optical coherence tomography (OCT) system that enables high-speed full-range imaging. We implement a piezoelectric fiber stretcher to generate a periodic phase shift between successive A-scans, thus introducing a transverse modulation. The depth ambiguity is then resolved by performing a Fourier filtering in the transverse direction before processing the data in the axial direction. The dc artifact is also removed. The key factor is that the piezoelectric fiber stretcher can be used to generate discrete phase shifts with a high repetition rate. The proposed experimental setup is a much improved version of the previously reported B-M mode scanning for spectral-domain OCT in that it does not generate additional artifacts. It is a simple and low-cost solution for artifact removal that can easily be applied.     

磁绝缘传输线电压的初步测量

针对快脉冲直线变压器驱动源装置上的真空磁绝缘传输线（MITL)电压测量的需求,开展了微分型电容分压器和电感分压器的设计、标定和实验。通过不同的电压值、负载阻抗的装置实验中探头输出结果的分析和比较,讨论了测量方法的可行性和适用范围。实验结果表明:微分型电容分压器能够应用于完全磁绝缘状态下的MITL电压测量,但容易受阴极电子发射的影响导致探头输出波形发生畸变。电感分压器受分布电容和电感的影响导致输出信号存在寄生振荡,采用波形重建的方法初步获得了合理的测量结果。     

Use of Global Symmetries in Automated Signal Class Recognition by a Bayesian Method

Automated or semiautomated pattern recognition in multidimensional NMR spectroscopy is strongly hampered by the large number of noise and artifact peaks occurring under practical conditions. A general Bayesian method which is able to assign probabilities that observed peaks are members of given signal classes (e.g., the class of true resonance peaks or the class of noise and artifact peaks) was proposed previously. The discriminative power of this approach is dependent on the choice of the properties characterizing the peaks. The automated class recognition is improved by the addition of a nonlocal feature, the similarities of peak shapes in symmetry-related positions. It turns out that this additional property strongly decreases the overlap of the multivariate probability distributions for true signals and noise and hence largely increases the discrimination of true resonance peaks from noise and artifacts.     

Finite pulse width artifact suppression in spin-1 quadrupolar echo spectra by phase cycling

A phase cycling scheme for suppressing spectral artifacts introduced in quadrupolar echo spectroscopy of spin-1 nuclei due to finite pulse width effects is presented. The phase cycling scheme is developed using the formalism of average Hamiltonian theory and fictitious spin-1 operators. A simulation and experiment on deuterated polyethelene is performed highlighting the spectral artifact introduced by finite pulse widths and successful removal with the proposed phase cycling scheme.     

Phase gradient imaging for positive contrast generation to superparamagnetic iron oxide nanoparticle-labeled targets in magnetic resonance imaging

Positive contrast imaging methods produce enhanced signal at large magnetic field gradient in magnetic resonance imaging. Several postprocessing algorithms, such as susceptibility gradient mapping and phase gradient mapping methods, have been applied for positive contrast generation to detect the cells targeted by superparamagnetic iron oxide nanoparticles. In the phase gradient mapping methods, smoothness condition has to be satisfied to keep the phase gradient unwrapped. Moreover, there has been no discussion about the truncation artifact associated with the algorithm of differentiation that is performed in k-space by the multiplication with frequency value. In this work, phase gradient methods are discussed by considering the wrapping problem when the smoothness condition is not satisfied. A region-growing unwrapping algorithm is used in the phase gradient image to solve the problem. In order to reduce the truncation artifact, a cosine function is multiplied in the k-space to eliminate the abrupt change at the boundaries. Simulation, phantom and in vivo experimental results demonstrate that the modified phase gradient mapping methods may produce improved positive contrast effects by reducing truncation or wrapping artifacts.     

Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB)

Objective: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI. Methods: We developed a deep residual network with densely connected multi-resolution blocks (DRN-DCMB) model to reduce the motion artifacts in T1 weighted (T1W) spin echo images acquired on different imaging planes before and after contrast injection. The DRN-DCMB network consisted of multiple multi-resolution blocks connected with dense connections in a feedforward manner. A single residual unit was used to connect the input and output of the entire network with one shortcut connection to predict a residual image (i.e. artifact image). The model was trained with five motion-free T1W image stacks (pre-contrast axial and sagittal, and post-contrast axial, coronal, and sagittal images) with simulated motion artifacts. Results: In other 86 testing image stacks with simulated artifacts, our DRN-DCMB model outperformed other state-of-the-art deep learning models with significantly higher structural similarity index (SSIM) and improvement in signal-to-noise ratio (ISNR). The DRN-DCMB model was also applied to 121 testing image stacks appeared with various degrees of real motion artifacts. The acquired images and processed images by the DRN-DCMB model were randomly mixed, and image quality was blindly evaluated by a neuroradiologist. The DRN-DCMB model significantly improved the overall image quality, reduced the severity of the motion artifacts, and improved the image sharpness, while kept the image contrast. Conclusion: Our DRN-DCMB model provided an effective method for reducing motion artifacts and improving the overall clinical image quality of brain MRI.     

Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI

The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small. The purpose of this study is to develop a learning-based model to combine the multiple phase-cycled bSSFP datasets for better banding artifact suppression. Multilayer perceptron (MLP) is a feedforward artificial neural network consisting of three layers of input, hidden, and output layers. MLP models were trained by input bSSFP datasets acquired from human brain and knee at 3T, which were separately performed for two and four PC angles. Banding-free bSSFP images were generated by maximum-intensity projection (MIP) of 8 or 12 phase-cycled datasets and were used as targets for training the output layer. The trained MLP models were applied to another brain and knee datasets acquired with different scan parameters and also to multiple phase-cycled bSSFP functional MRI datasets acquired on rat brain at 9.4T, in comparison with the conventional MIP method. Simulations were also performed to validate the MLP approach. Both the simulations and human experiments demonstrated that MLP suppressed banding artifacts significantly, superior to MIP in both banding artifact suppression and SNR efficiency. MLP demonstrated superior performance over MIP for the 9.4T fMRI data as well, which was not used for training the models, while visually preserving the fMRI maps very well. Artificial neural network is a promising technique for combining multiple phase-cycled bSSFP datasets for banding artifact suppression.     

硬化伪影的新型表现形式及其校正

X射线硬化是导致工业CT重建图像质量下降的物理原因之一。硬化伪影通常表现为两种形式,即杯状伪影和带状伪影。描述并证实了硬化伪影的一种新型表现形式,这种伪影与真实结构相关,且分布规则,容易造成伪影与真实结构的混淆。采用线性化校正方法对该伪影进行抑制,提高了重建图像质量,改善了通过CT重建图像进行几何测量的精度。     

CSF flow artifact reduction using cardiac cycle ordered phase-encoding method

The flow effects appear as a change of phase as well as signal intensity in NMR imaging. Since the flow of blood and CSF (cerebrospinal fluid) is pulsatile due to the heart pumping, their velocities are not constant during NMR imaging. This type of velocity fluctuation such as the blood or CSF flow induces irregular flow-dependent phase shifts, which have been one of the main causes of flow artifacts in NMR imaging. In order to reduce the flow artifacts, especially the CSF flow artifacts, a new cardiac cycle ordered phase encoding method is proposed and has been studied. This proposed technique utilizes the cardiac cycle as a precursor for the phase encoding gradients similar to the ROPE (respiratory ordered phase encoding) technique which has been used for respiratory motion artifact reduction. The basic concept and its applications are discussed together with the experimental results obtained with human volunteers using the KAIS 2.0 2.0 T whole-body NMR imaging system.     

Restoration of MR-induced artifacts in simultaneously recorded MR/EEG data

During a Magnetic Resonance sequence, simultaneously acquired ElectroEncephaloGraphy (EEG) data are compromised by severe pollution due to artifacts originating from the switching of the magnetic field gradients. In this work, it is shown how these artifacts can be strongly reduced or even removed through application of an adaptive artifact restoration scheme. The method has proved to be fully automatic and to retain high frequency EEG information, which is indispensable for many EEG applications.     

Synthesis methods of multiple phase-cycled SSFP images to reduce the band artifact and noise more reliably

The band artifact in steady-state free precession can be reduced by synthesizing the multiple images obtained through different phase increments of successive radiofrequency pulses. Even though the complex summation method was reported to be effective in reducing the band artifact, it has the pitfalls of intensity abnormality and sensitivity to the phase abnormality. Two new methods have been developed for more reliable reduction of the band artifact than the complex summation method. One method is to sum the complex images partially and to take the maximum intensity of the partially summed images. The other method is to sum the free induction decay (FID) and primary echo components of the Fourier series that are obtained through Fourier analysis of the complex base images. Both proposed methods were compared with other magnitude (maximum intensity projection, spectrally decomposed synthesis, sum-of-squares, nonlinear averaging) and complex-based (complex summation, magnitude-weighted complex summation) methods experimentally at 3 T for the phantom and volunteer's head imaging. Both proposed methods were confirmed to maintain the advantage of the complex summation in reducing both the dark and bright band artifacts while reducing the intensity abnormality and sensitivity to the phase abnormality from that of the complex summation method over a wide range of flip angles and relaxation times.     

Deep learning for image reconstruction in thermoacoustic tomography

Microwave-induced thermoacoustic tomography(TAT)is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging.While an image reconstruction algorithm is critical for the TAT,current reconstruction methods often creates significant artifacts and are computationally costly.In this work,we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image.We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy.For the scenarios where realistic training data are scarce or unavailable,we use the finite element method(FEM)to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method.The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness.Compared with other state-of-the-art reconstruction methods,the TAT-Net method can reduce the root mean square error to 0.0143,and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64,respectively.The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.