The multifactor method of EEG separation into the cortical and deep components

A new method of separation of multichannel brain electrical activity into cortical and subcortical components with the help of multifactor analysis is proposed. The method provides a means for isolation and, consequently, more reliable localization of sources of electrical activity not only in deep brain structures (on the basis of the dipole model) but also on the cortical surface. The proposed method does not depend on the rotation and interpretation of factors, and no data losses occur. The mufasel algorithm is based on integration of all selected factors (within a particular EEG or EP time segment) in two groups using a statistical criterion, which defines general and specific factors. It is assumed that general factors loaded with highly correlated derivations predominantly describe the electrical activity of deep brain structures, whereas specific factors loaded by the dynamics of electrical activity in individual derivations, reflect the integrated activity of cortical brain structures.     

Bone-induced streak artifact suppression in sparse-view CT image reconstruction

ABSTRACT: BACKGROUND: In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction. METHODS: We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts. RESULTS: The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method. CONCLUSIONS: The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.     

DSA图像中运动伪影的消除方法

由于病人存在着各种运动(如呼吸、肌肉运动、心脏运动、设备噪声),在成像过程中常会造成图像上出现伪影,干扰医生的正常诊断,为消除这种伪影,本文提出一种基于图像配准思想的全自动消除伪影的方法,该方法能够自动消除DSA图像中的大部分运动伪影,使DSA图像得到较好的增强,并为后面的血管分割和三维重建提供便利,是一种快速有效的方法。     

Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts. Utilizing the uniqueness of vascular wall on A-lines, we detect the A-lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts. We compare these three methods by 216 challenging images with severe blood artifacts selected from clinical 1076 IVOCT images. The metrics of the proposed method are evaluated including Dice index, Jaccard index and accuracy of 94.57%, 90.12%, 98.02%. Compared with automatic lumen segmentation based on the previous morphological feature method and widely used dynamic programming method, the metrics of the proposed method are significantly enhanced, especially in challenging images with severe blood artifacts.     

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.     

Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography

PurposeLimited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed.MethodsSince the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV.ResultsSimulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm.ConclusionThis paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods.     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

Optimization of the DGGE band identification method

Denaturant gradient gel electrophoresis (DGGE) enables insight into the diversity of the studied microbial communities on the basis of separation of PCR amplification products according to their nucleotide sequence composition. However, the success of the method is accompanied by the inherent appearance of various sequence artifacts that bias the impression of community structure by generating additional bands representing no virtual microbes. PCR-DGGE artifacts require optimization of the method when aiming at the phylogenetic identification of the selected DGGE bands. The aim of our study was to develop a procedure which will increase the reliability of the identification. Samples of rumen fluid were used for the optimization since they contain a complex microbial community that supports the generation of artifactual bands. An optimized procedure following band excision and elution of microbial DNA is proposed including nuclease treatment, selection of DNA polymerase with proofreading activity, and cloning prior to sequencing and identification analysis.     

A Retrospective Respiratory Gating System Based on Epipolar Consistency Conditions

Motion artifacts of in vivo imaging, due to rapid respiration rate and respiration displacements of the mice while free-breathing, is a major challenge in micro computed tomography(micro-CT). The respiratory gating is often served for either projective images acquisition or per projection qualification, so as to eliminate the artifacts brought by in vivo motion. In this paper, we propose a novel respiratory gating method, which firstly divides one rotation cycle into a number of segments, and extracts the respiratory signal from the projective image series of current segment by the value of the epipolar consistency conditions (ECC), then in terms of the measured average respiratory period, sets next segment’s start-up time and rotation speed of the gantry for respiratory phase synchronization, and so on so forth. The gating procedure is through the whole projections of three cycles, only one among three projections at each angle is qualified by their phase value and is retained for future use for tomographic image reconstruction. In practical experiment, the ECC based gating method and the conventional hardware gating method are employed on micro CT imaging of C57BL/6 mice respectively. The result shows that, compared with the hardware based one, the proposed method not only achieve much better consistency in the projection images, but also suppresses the streak artifacts more effectively on the different parts like the breast, abdomen and head of in vivo mice.     

A modification for the calculation of water depth profiles in oil‐treated skin by in vivo confocal Raman microscopy

In this study, an extended calculation method for the determination of the water profiles in oil‐treated skin is proposed, which is based on the calculation of the ratio between the Raman band intensities of water (3350‐3550 cm^?1) and keratin Amide I at 1650 cm^?1. The proposed method is compared with the conventional method based on the ratio of the Raman band intensities of water (3350‐3550 cm^?1) and keratin at 2930 cm^?1. The conventional method creates artifacts in the depth profiles of the water concentration in oil‐treated skin, showing a lower amount of water in the upper and intermediate layers of the stratum corneum, which is due to the superposition of oil‐ and keratin‐related Raman bands at 2930 cm^?1. The proposed extended method shows no artifacts and has the potential to determine the water depth profiles after topical application of formulations on the skin.     

Rectification of SEMG as a tool to demonstrate synchronous motor unit activity during vibration

The use of surface electromyography (SEMG) in vibration studies is problematic since motion artifacts occupy the same frequency band with the SEMG signal containing information on synchronous motor unit activity. We hypothesize that using a harsher, 80–500 Hz band-pass filter and using rectification can help eliminate motion artifacts and provide a way to observe synchronous motor unit activity that is phase locked to vibration using SEMG recordings only. Multi Motor Unit (MMU) action potentials using intramuscular electrodes along with SEMG were recorded from the gastrocnemius medialis (GM) of six healthy male volunteers. Data were collected during whole body vibration, using vibration frequencies of 30 Hz, 35 Hz, 40 Hz or 50 Hz. A computer simulation was used to investigate the efficacy of filtering under different scenarios: with or without artifacts and/or motor unit synchronization. Our findings indicate that motor unit synchronization took place during WBV as verified by MMU recordings. A harsh filtering regimen along with rectification proved successful in demonstrating motor unit synchronization in SEMG recordings. Our findings were further supported by the results from the computer simulation, which indicated that filtering and rectification was efficient in discriminating motion artifacts from motor unit synchronization. We suggest that the proposed signal processing technique may provide a new methodology to evaluate the effects of vibration treatments using only SEMG. This is a major advantage, as this non-intrusive method is able to overcome movement artifacts and also indicate the synchronization of underlying motor units.     

Bayesian classifier for brain-computer interface based on mental representation of movements

This paper proposes Bayesian approach to classification of EEG patterns on the basis of imaginary movements of extremities based on analysis ofcovariance matrices of native EEG recordings. An efficacy of a Brain-Computer Interface (BCI) based on the proposed classifier is evaluated. Bayesian classifier is shown to be competitive with the MCSP (Multiclass Common Spatial Patterns) classifier known from the literature as one of the efficient variant for BCI implementation. The influence of eye movement and blinking artifacts on the BCI performance is investigated. It is shown that the presence of such artifacts does not affect the classification accuracy.     

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

Background

Despite its superb lateral resolution, flat-panel-detector (FPD) based tomosynthesis suffers from low contrast and inter-plane artifacts caused by incomplete cancellation of the projection components stemming from outside the focal plane. The incomplete cancellation of the projection components, mostly due to the limited scan angle in the conventional tomosynthesis scan geometry, often makes the image contrast too low to differentiate the malignant tissues from the background tissues with confidence.

Methods

In this paper, we propose a new method to suppress the inter-plane artifacts in FPD-based tomosynthesis. If 3D whole volume CT images are available before the tomosynthesis scan, the CT image data can be incorporated into the tomosynthesis image reconstruction to suppress the inter-plane artifacts, hence, improving the image contrast. In the proposed technique, the projection components stemming from outside the region-of-interest (ROI) are subtracted from the measured tomosynthesis projection data to suppress the inter-plane artifacts. The projection components stemming from outside the ROI are calculated from the 3D whole volume CT images which usually have lower lateral resolution than the tomosynthesis images. The tomosynthesis images are reconstructed from the subtracted projection data which account for the x-ray attenuation through the ROI. After verifying the proposed method by simulation, we have performed both CT scan and tomosynthesis scan on a phantom and a sacrificed rat using a FPD-based micro-CT.

Results

We have measured contrast-to-noise ratio (CNR) from the tomosynthesis images which is an indicator of the residual inter-plane artifacts on the focal-plane image. In both cases of the simulation and experimental imaging studies of the contrast evaluating phantom, CNRs have been significantly improved by the proposed method. In the rat imaging also, we have observed better visual contrast from the tomosynthesis images reconstructed by the proposed method.

Conclusions

The proposed tomosynthesis technique can improve image contrast with aids of 3D whole volume CT images. Even though local tomosynthesis needs extra 3D CT scanning, it may find clinical applications in special situations in which extra 3D CT scan is already available or allowed.     

Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings

The aim of this paper is to develop a method to extract relevant activities from surface electromyography (SEMG) recordings under difficult experimental conditions with a poor signal to noise ratio. High amplitude artifacts, the QRS complex, low frequency noise and white noise significantly alter EMG characteristics. The CEM algorithm proved to be useful for segmentation of SEMG signals into high amplitude artifacts (HAA), phasic activity (PA) and background postural activity (BA) classes. This segmentation was performed on signal energy, with classes belonging to a χ² distribution. Ninety-five percent of HAA events and 96.25% of BA events were detected, and the remaining noise was then identified using AR modeling, a classification based upon the position of the coordinates of the pole of highest module. This method eliminated 91.5% of noise and misclassified only 3.3% of EMG events when applied to SEMG recorded on passengers subjected to lateral accelerations.     

Metal artifact reduction on cervical CT images by deep residual learning

Background

Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Methods

A novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.

Results

The proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512?×?512 image, the average removal artifact time is less than 1 s.

Conclusions

The RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

     

Ring Artifacts Reduction in CBCT: Pixels Detection and Patch Based Correction

The ring artifacts introduced by the defective pixels with non-linear responses in the high-resolution detector, have a great impact on subsequent processing and quantitative analysis of the reconstructed images. In this paper, a multistep method is proposed to suppress the ring artifacts of micro CT images, which firstly locates the positions of the defective pixels in the sinogram, and then corrects the corresponding value in the projections. Since the defective pixels always appear as vertical stripes in the sinogram, a horizontal curve is derived by summing the pixel values along vertical direction, thus the abrupt segments related to the defective stripes are enhanced notably, and a proportion coefficient based on the second derivative of the curve is taken as the indicator for the position and the severity of the defective pixels. Then, the detected defective pixels in the sinogram are transferred and relocated in the projections, an improved 3D block matching filtering (BM3D) algorithm is applied to restore the defective pixels in corresponding projection images. In the end, the tomographic images are reconstructed from the corrected projections. In the experiment, a small piece of the motherwort’s rhizome and a part of a mouse’s lung are imaged by micro-CT, and the result shows that, compared with the other four state-of-art methods, the proposed method has a great reduction on the ring artifacts of the reconstructed images, and makes less impact in spatial resolution and contrast in the same time.     

Significance and assessment of autonomic indices in cardiovascular reactions.

The purpose of this review was to integrate recent evidence supporting the reliability of noninvasive measures of parasympathetic and sympathetic activity. Literature concerning spectral analysis of heart period (HP) variability is reviewed with special emphasis on works revealing neural mediation of high-frequency and mid-frequency components of HP power spectrum and suggesting their use as a tool to assess autonomic balance. Problems of derivations of autonomic indices based on impedance cardiography and HP variance analysis are discussed. Advantages of parametric time series (autoregressive-AR) models are described with the objective of providing an informed basis for choosing among methodological alternatives. Two original approaches developed in our laboratory are outlined, namely the algorithms for systolic time interval assessment based on impedance cardiogram as well as the AR method developed for heart period power spectral density estimation.     

Flavoprotein Autofluorescence Imaging of Visual System Activity in Zebra Finches and Mice

Large-scale brain activity patterns can be visualized by optical imaging of intrinsic signals (OIS) based on activity-dependent changes in the blood oxygenation level. Another method, flavoprotein autofluorescence imaging (AFI), exploits the mitochondrial flavoprotein autofluorescence, which is enhanced during neuronal activity. In birds, topographic mapping of visual space has been shown in the visual wulst, the avian homologue of the mammalian visual cortex by using OIS. We here applied the AFI method to visualize topographic maps in the visual wulst because with OIS, which depends on blood flow changes, blood vessel artifacts often obscure brain activity maps. We then compared both techniques quantitatively in zebra finches and in C57Bl/6J mice using the same setup and stimulation conditions. In addition to experiments with craniotomized animals, we also examined mice with intact skull (in zebra finches, intact skull imaging is not feasible probably due to the skull construction). In craniotomized animals, retinotopic maps were obtained by both methods in both species. Using AFI, artifacts caused by blood vessels were generally reduced, the magnitude of neuronal activity significantly higher and the retinotopic map quality better than that obtained by OIS in both zebra finches and mice. In contrast, our measurements in non-craniotomized mice did not reveal any quantitative differences between the two methods. Our results thus suggest that AFI is the method of choice for investigations of visual processing in zebra finches. In mice, however, if researchers decide to use the advantages of imaging through the intact skull, they will not be able to exploit the higher signals obtainable by the AFI-method.     

3D Algebraic Iterative Reconstruction for Cone-Beam X-Ray Differential Phase-Contrast Computed Tomography

Due to the potential of compact imaging systems with magnified spatial resolution and contrast, cone-beam x-ray differential phase-contrast computed tomography (DPC-CT) has attracted significant interest. The current proposed FDK reconstruction algorithm with the Hilbert imaginary filter will induce severe cone-beam artifacts when the cone-beam angle becomes large. In this paper, we propose an algebraic iterative reconstruction (AIR) method for cone-beam DPC-CT and report its experiment results. This approach considers the reconstruction process as the optimization of a discrete representation of the object function to satisfy a system of equations that describes the cone-beam DPC-CT imaging modality. Unlike the conventional iterative algorithms for absorption-based CT, it involves the derivative operation to the forward projections of the reconstructed intermediate image to take into account the differential nature of the DPC projections. This method is based on the algebraic reconstruction technique, reconstructs the image ray by ray, and is expected to provide better derivative estimates in iterations. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a mini-focus x-ray tube source. It is shown that the proposed method can reduce the cone-beam artifacts and performs better than FDK under large cone-beam angles. This algorithm is of interest for future cone-beam DPC-CT applications.     

Epilepsy in kcnj10 Morphant Zebrafish Assessed with a Novel Method for Long-Term EEG Recordings

We aimed to develop and validate a reliable method for stable long-term recordings of EEG activity in zebrafish, which is less prone to artifacts than current invasive techniques. EEG activity was recorded with a blunt electrolyte-filled glass pipette placed on the zebrafish head mimicking surface EEG technology in man. In addition, paralysis of agarose-embedded fish using D-tubocurarine excluded movement artifacts associated with epileptic activity. This non-invasive recording technique allowed recordings for up to one hour and produced less artifacts than impaling the zebrafish optic tectum with a patch pipette. Paralyzed fish survived, and normal heartbeat could be monitored for over 1h. Our technique allowed the demonstration of specific epileptic activity in kcnj10a morphant fish (a model for EAST syndrome) closely resembling epileptic activity induced by pentylenetetrazol. This new method documented that seizures in the zebrafish EAST model were ameliorated by pentobarbitone, but not diazepam, validating its usefulness. In conclusion, non-invasive recordings in paralyzed EAST syndrome zebrafish proved stable, reliable and robust, showing qualitatively similar frequency spectra to those obtained from pentylenetetrazol-treated fish. This technique may prove particularly useful in zebrafish epilepsy models that show infrequent or conditional seizure activity.