Abstract: The projectional nature of radiogram limits its amount of information about the instrumented spine. MRI and CT imaging can be more helpful, using cross-sectional view. However, the presence of metal-related artifacts at both conventional CT and MRI imaging can obscure relevant anatomy and disease. We reviewed the literature about overcoming artifacts from metallic orthopaedic implants at high-field strength MRI imaging and multi-detector CT. The evolution of multichannel CT has made available new techniques that can help minimizing the severe beam-hardening artifacts. The presence of artifacts at CT from metal hardware is related to image reconstruction algorithm (filter), tube current (in mA), X-ray kilovolt peak, pitch, hardware composition, geometry (shape), and location. MRI imaging has been used safely in patients with orthopaedic metallic implants because most of these implants do not have ferromagnetic properties and have been fixed into position. However, on MRI imaging metallic implants may produce geometric distortion, the so-called susceptibility artifact. In conclusion, although 140 kV and high milliamperage second exposures are recommended for imaging patients with hardware, caution should always be exercised, particularly in children, young adults, and patients undergoing multiple examinations. MRI artifacts can be minimized by positioning optimally and correctly the examined anatomy part with metallic implants in the magnet and by choosing fast spin-echo sequences, and in some cases also STIR sequences, with an anterior to posterior frequency-encoding direction and the smallest voxel size.

Abstract: Background Within the intensive care unit (ICU), arterial blood pressure (ABP) is typically recorded at different (and sometimes uneven) sampling frequencies, and from different sensors, and is often corrupted by different artifacts and noise which are often non-Gaussian, nonlinear and nonstationary. Extracting robust parameters from such signals, and providing confidences in the estimates is therefore difficult and requires an adaptive filtering approach which accounts for artifact types.

Abstract: Disclosed are a method and/or system for filtering sensor measurements. In one particular implementation, a sensor signal may be processed concurrently in a plurality of signal-filter paths. A particular signal-filter path may be selected to provide an output signal for obtaining a measurement based, at least in part, on a measurement of noise associated with the sensor signal.

Abstract: In the digital multimedia era, it is increasingly important to ensure the integrity and authenticity of the vast volumes of video data. A novel approach is proposed for detecting video forgery based on ghost shadow artifact in this paper. Ghost shadow artifact is usually introduced when moving objects are removed by video inpainting. In our approach, ghost shadow artifact is accurately detected by inconsistencies of the moving foreground segmented from the video frames and the moving track obtained from the accumulative frame differences, thus video forgery is exposed. Experiments show that our approach achieves promising results in video forgery detection.

Abstract: In this study, we proposed and evaluated the use of Independent Component Analysis (ICA) combining the EEG dipole model to automatically remove eye movement artifacts from the EEG without needing EOG as a reference. We separated the EEG data into independent components using the ICA method, and determined the source localization of these independent components with a single dipole model. The EEG signal was reconstructed by automatically excluding those components localized within a preset eye model. EEGs from 12 patients were analyzed. The experimental results indicate that ICA with the dipole model is very efficient at automatically subtracting the eye movement artifacts, while retaining the EEG slow waves and making their interpretation easier.

Abstract: This chapter contains sections titled: Introduction The problem of missing studies Methods for addressing bias Illustrative example The model Getting a sense of the data Is there evidence of any bias? Is the entire effect an artifact of bias? How much of an impact might the bias have? Summary of the findings for the illustrative example Some important caveats Small-study effects Concluding remarks Summary points

Abstract: OBJECTIVE. This article explores the circumstances under which the color comet-tail artifact occurs and illustrates the clinical value of the artifact.CONCLUSION. Subtle abnormalities on gray-scale sonograms often are better appreciated and understood when the color comet-tail artifact is present. This artifact often is helpful in situations in which gray-scale imaging does not provide adequate information for a conclusive diagnosis. Visualization of the color comet-tail artifact can improve diagnostic confidence in a wide spectrum of clinical conditions encountered in sonographic practice.

Abstract: The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.

Abstract: Electromyography (EMG) is a study of muscles function through analysis of electrical activity produced from muscles. This electrical activity which is displayed in form of signal is the result of neuromuscular activation associated with muscle contraction. The most common techniques of EMG signal recording are by using surface and needle/wire electrode where the latter is usually used for interest in deep muscle. This paper will focus on surface electromyography (SEMG) signal. During SEMG recording, several problems had to be encountered such as noise, motion artifact and signal instability. Thus, various signal processing techniques had been implemented to produce a reliable signal for analysis. There are also broad applications of SEMG signal particularly in biomedical field. The SEMG signal had been analyzed and studied for various interests such as neuromuscular disease, enhancement of muscular function and human-computer interface.

Abstract: Recent work on a new, higher sensitivity preamplifier design for Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS) revealed a number of artifact peaks (spectral features) which do not contain useful chemical information. In order to determine the cause of these artifacts and eliminate them, these severely distorted spectra were compared with similarly distorted signal models. The source of several common signal processing artifacts was thereby determined and correlated to radio-frequency interference (RFI) noise and saturation of the amplifier and/or the digitizer. Under such conditions, the fast Fourier transform (FFT) generates spectral artifact peaks corresponding to harmonics and mixing frequencies of the real signal peaks and RFI frequencies. While this study was done using FTICRMS data, it is important to stress that these artifacts are inherent to the digitization and FFT process and thus are relevant to any FT-based MS instrument, including the orbitrap and FT ion trap. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract: Using ultra-wide band (UWB) microwave pulse for breast cancer detection has been greatly investigated recently since it does not expose the patient to any harmful radiation and the implementation is relatively cheaper than other methods such as MRI or X-ray. An issue in UWB imaging of breast cancer is the strong backscatter from the breast skin which is in orders of magnitude larger than the pulse backscattered from the tumor and should be eliminated before processing the signal for the tumor detection and imaging. At present no existing method can efiectively remove this artifact without introducing corruption to the tumor signature. In this paper, a novel method to eliminate this artifact is proposed which employs a frequency domain model to isolate and remove skin related information from the signal. This method is compared with the existing methods of the skin artifact removal in difierent scenarios. The results show that the new method can overcome the shortcomings of the previous methods and improve the detection of the tumor in the sense of the tumor to clutter response ratio.

Abstract: Consideration of spatially variable noise fields is becoming increasing necessary in magnetic resonance imaging given recent innovations in artifact identification and statistically-driven image processing. Fast imaging methods enable study of difficult anatomical targets and improve image quality but also increase the spatial variability in the noise field. Traditional analysis techniques have either assumed that the noise is constant across the field of view (or region of interest) or have relied on separate magnetic resonance image acquisitions to measure the noise field. These methods are either inappropriate for many modern scanning protocols or are overly time-consuming for already lengthy scanning sessions. We propose a new, general framework for estimating spatially variable noise fields from related, but independent magnetic resonance scans which we call noise field equivalent scans. These heuristic analyses enable robust noise field estimation in the presence of artifacts. Generalization of noise estimators based on uniform regions, difference images, and maximum likelihood are presented and compared with the estimators derived from the proposed framework. Simulations of diffusion tensor imaging and T2-relaxometry demonstrate a ten-fold reduction in mean squared error in noise field estimation, and these improvements are shown to be robust to artifact contamination. In vivo studies show that spatially variable noise fields can be readily estimated with typical data acquired at 1.5T.

Abstract: A system and method of presentation of an extracted artifact based on an indexing technique are disclosed. In an embodiment, the method includes indexing a database of a captured network characteristic data using a processor and a memory to form an indexed capture data. The method includes enhancing a query response time with the indexed capture data. The method further includes searching the indexed capture data to generate a capture query result. The capture query result includes an extracted artifact. The method also includes graphically presenting the capture query result as at least one of an artifact list and an artifact image.

Abstract: A method and apparatus utilizing a piecewise stitching adaptive algorithm (PSAA) to filter signal artifacts, such as those induced by cardiopulmonary resuscitation (CPR) from sensed signals in real-time. PSAA is a method of estimating artifact component present in a first signal that is highly correlated with a second signal. The PSAA may utilize autocorrelation and cross-correlation calculations to determine signal sample windows in the first and second signals. The PSAA may estimate a signal artifact in a primary signal segment based on the determined correlations between the primary signal and an artifact signal. The PSAA may remove the estimated signal artifact from the primary signal. In the absence of an artifact signal, PSAA is able to estimate artifacts in the first signal utilizing filters. The PSAA may be implemented in Automated External Defibrillators, Monitor Defibrillators or other devices capable sensing highly correlated signals such as, for example, ECG and CPR signals.

Abstract: Assessing video content transmitted over networked content infrastructures becomes a fundamental requirement for service providers. Previous research has shown that there is no direct correlation between traditional network QoS and user perceived video quality. This paper presents a study investigating the impact of individual packet loss on four types of H.264 main-profile encoded video streams. Four artifact factors to model the degree of artifacts in video frames are defined. Further, the visibility of artifacts considering the video content characteristics, encoding scheme and error concealment is investigated in conjunction with a user study. The individual and joint impacts of artifact factors are explored on the test video sequences. From the results of user tests, the artifact factor-based assessment method shows superiority over PSNR-based and network QoS based quality assessment.

Abstract: To reduce the effects of artifacts in electroencephalography (EEG), we propose the use of Morphological Component Analysis (MCA). Taking advantage of the sparse representation of data in overcomplete dictionaries, MCA decomposes EEG signals into parts that have different morphological characteristics. For denoising purpose, the parts related to artifacts are removed. An overcomplete dictionary is constructed using the discrete cosine transform, Daubechies wavelet basis, and Dirac basis. Movement-related potentials (MRP) and EEG signals contaminated by spikes, eye-blinks, and muscle artifacts caused by eye-brow raising are used to evaluate the performance of the method. The results demonstrate that MCA can be used to decompose the single-channel EEG signals into artifacts and MRP components. The correlation coefficient between the denoised MRP and the original MRP using MCA is significantly higher than that obtained using stationary wavelet transform.

Abstract: The electrocardiogram (ECG) is the most commonly used for diagnosis of heart diseases Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena However, in real situations, ECG signals are corrupted by artifacts So the noise removal is a classical problem in ECG records, that generally produces artifactual data when measuring the ECG parameters The Block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm In this paper, we present a BLMS algorithm for removing artifacts preserving the low frequency components and tiny features of the ECG Finally, we have applied this algorithm on ECG signals from the MIT-BIH data base and compared its performance with the conventional LMS algorithm The results show that the performance of the BLMS algorithm is superior than the LMS algorithm

Abstract: OBJECTIVE. The purpose of this study was to address the controversy whether the quality of volumetric high-resolution CT (HRCT) images is as good as that of axial nonvolumetric HRCT images by assessing the degree of motion artifact on images acquired with the two methods at MDCT of pediatric patients with known or suspected lung disease.MATERIALS AND METHODS. A search of the hospital information system was conducted to identify the cases of pediatric patients with clinically suspected or known interstitial lung disease who underwent 16-MDCT of the chest with both volumetric and axial HRCT acquisitions (both 1.25-mm slice thickness) from March 2005 to July 2008. Two pediatric radiologists reviewed the images for the presence of motion artifacts at three anatomic levels (upper, middle, and lower lung zones). Motion artifacts were given numerical grades representing no artifact to severe artifact, and the paired Student's t test was used to compare the scores for the two acquisition methods. A total motion s...

Abstract: Electroencephalograms (EEGs) recorded simultaneously with functional magnetic resonance imaging (fMRI) are corrupted by large repetitive artifacts generated by the switched MR gradients. Several methods have been proposed to remove these distortions by subtraction of averaged artifact templates from the ongoing EEG. Here, we present a modification of this approach which accounts for head movements to improve the extracted template. Using the fMRI analysis package statistical parametric mapping (SPM; FIL London) the head displacement is determined at each half fMRI-volume. The basic idea is to apply a moving average algorithm for template extraction but to include only epochs that were obtained at the same head position as the artefact to be removed. This approach was derived from phantom EEG measurements demonstrating substantial variations of the artefact waveform in response to movements of the phantom in the MRI magnet. To further reduce the residual noise, we applied a resampling algorithm which aligns the EEG samples in a strict adaptive manner to the fMRI timing. Finally, we propose a new algorithm to suppress residual artifacts such as those occasionally observed in case of brief strong movements, which are not reflected by the movement indicator because of the limited temporal resolution of the fMRI sequence. On the basis of EEG recordings of six subjects these measures combined reduce the residual artefact activity quantified in terms of the spectral power at the gradient repetition rate and its harmonics by roughly 20 to 50% (depending on the amount of movement) predominantly in frequencies beyond 30 Hz.

Abstract: Methods and apparatuses for correcting image artifacts in a high dynamic range image formed by combining a plurality of exposures taken at different integration periods. A determination is made as to whether there is motion between the exposures. If motion is detected, pixel signal values chosen are replaced with pixel signal values from another exposure.