Showing papers on "Artifact (error) published in 2014"

Artifact suppressed dictionary learning for low-dose CT image processing.

Abstract: Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts These artifacts are often hard to suppress without introducing tissue blurring effects In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)" In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

Abstract: The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

A Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications

Abstract: This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 $\mu$ m CMOS process and consumes 32 $\mu$ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.

Respiratory Motion Artifact Affecting Hepatic Arterial Phase Imaging with Gadoxetate Disodium: Examination Recovery with a Multiple Arterial Phase Acquisition

Abstract: Use of single-breath-hold triple arterial phase acquisition in abdominal MR imaging with gadoxetate disodium provides adequate image quality in most arterial phases that might otherwise have been compromised by transient severe motion.

Background field removal using spherical mean value filtering and Tikhonov regularization.

Abstract: PurposeTo introduce a new method for removing background artifacts in field maps and apply it to enhance the accuracy of susceptibility mapping.MethodsA field artifact removal method is introduced that is based on the sophisticated harmonic artifact reduction for phase data (SHARP) method exploiting the harmonic mean value property. The new method uses Tikhonov regularization at the deconvolution stage and is referred to as regularization enabled SHARP (RESHARP). RESHARP was compared with SHARP in a field-forward susceptibility simulation and in human brain experiments, considering effects on both field maps and the resulting susceptibility maps.ResultsFrom the simulation, RESHARP was able to reduce error in the field map by 17.4% as compared with SHARP, resulting in a more accurate single-angle susceptibility map with 6.5% relative error (compared with 48.5% using SHARP). Using RESHARP in vivo, field and susceptibility maps of the brain displayed fewer artifacts particularly at the brain boundaries, and susceptibility measurements of iron-rich deep gray matter were also more consistent than SHARP across healthy subjects of similar age.ConclusionCompared with SHARP, RESHARP removes background field artifact more effectively, leading to more accurate susceptibility measurements in iron-rich deep gray matter. Magn Reson Med 71:1151-1157, 2014. (c) 2013 Wiley Periodicals, Inc.

Management of respiratory motion in PET/computed tomography: the state of the art

Abstract: Combined PET/computed tomography (CT) is of value in cancer diagnosis, follow-up, and treatment planning. For cancers located in the thorax or abdomen, the patient’s breathing causes artifacts and errors in PET and CT images. Many different approaches for artifact avoidance or correction have been developed; most are based on gated acquisition and synchronization between the respiratory signal and PET acquisition. The respiratory signal is usually produced by an external sensor that tracks a physiological characteristic related to the patient’s breathing. Respiratory gating is a compensation technique in which time or amplitude binning is used to exclude the motion in reconstructed PET images. Although this technique is performed in routine clinical practice, it fails to adequately correct for respiratory motion because each gate can mix several tissue positions. Researchers have suggested either selecting PET events from gated acquisitions or performing several PET acquisitions (corresponding to a breath-hold CT position). However, the PET acquisition time must be increased if adequate counting statistics are to be obtained in the different gates after binning. Hence, other researchers have assessed correction techniques that take account of all the counting statistics (without increasing the acquisition duration) and integrate motion information before, during, or after the reconstruction process. Here, we provide an overview of how motion is managed to overcome respiratory motion in PET/CT images.

An Additive Manufacturing Test Artifact.

Abstract: A test artifact, intended for standardization, is proposed for the purpose of evaluating the performance of additive manufacturing (AM) systems. A thorough analysis of previously proposed AM test artifacts as well as experience with machining test artifacts have inspired the design of the proposed test artifact. This new artifact is designed to provide a characterization of the capabilities and limitations of an AM system, as well as to allow system improvement by linking specific errors measured in the test artifact to specific sources in the AM system. The proposed test artifact has been built in multiple materials using multiple AM technologies. The results of several of the builds are discussed, demonstrating how the measurement results can be used to characterize and improve a specific AM system.

Automated biosignal quality analysis for electromyography using a one-class support vector machine

Abstract: This paper introduces the importance of biosignal quality assessment and presents a pattern classification approach to differentiate clean from contaminated electromyography (EMG) signals. Alternatively to traditional bottom-up approaches, which examine specific contaminants only, we present a top-down approach using a one-class support vector machine (SVM) trained on clean EMG and tested on artificially contaminated EMG. Both simulated and real EMG are used. Results are evaluated for each contaminant: 1) power line interference; 2) motion artifact; 3) ECG interference; 4) quantization noise; 5) analog-to-digital converter clipping; and 6) amplifier saturation, as a function of the level of signal contamination. Results show that different ranges of contamination can be detected in the EMG depending on the type of contaminant. At high levels of contamination, the SVM classifies all EMG signals as contaminated, whereas at low levels of contamination, it classifies the majority of EMG signals as contaminant free. A transition point for each contaminant is identified, where the classification accuracy drops and variance in classification increases. In some cases, contamination can be detected with the SVM when it is not visually discernible. This method is shown to be successful in detecting problems due to single contaminants but is generic to all forms of contamination in EMG.

Recovering pulse rate during motion artifact with a multi-imager array for non-contact imaging photoplethysmography

Abstract: Photoplethysmography relies on characteristic changes in the optical absorption of tissue due to pulsatile (arterial) blood flow in peripheral vasculature. Sensors for observing the photoplethysmographic effect have traditionally required contact with the skin surface. Recent advances in non-contact imaging photoplethysmography have demonstrated that measures of cardiopulmonary system state, such as pulse rate, pulse rate variability, and respiration rate, can be obtained from a participant by imaging their face under relatively motionless conditions. A critical limitation in this method that must be resolved is the inability to recover these measures under conditions of head motion artifact. To investigate the adequacy of channel space dimensionality for the use of blind source separation in this context, nine synchronized, visible spectrum imagers positioned in a semicircular array centered on the imaged participant were used for data acquisition in a controlled lighting environment. Three-lead electrocardiogram and finger-tip reflectance photoplethysmogram were also recorded as ground truth signals. Controlled head motion artifact trial conditions were compared to trials in which the participant remained stationary, with and without the aid of a chinrest. Bootstrapped means of one-minute, non-overlapping trial segments show that, for situations involving little to no head motion, a single imager is sufficient for recovering pulse rate with an average absolute error of less than two beats per minute. However, error in the recovered pulse rate measurement for the single imager can be as high as twenty-two beats per minute when head motion artifact is severe. This increase in measurement error during motion artifact was mitigated by increasing the dimensionality of the imager channel space with multiple imagers in the array prior to applying blind source separation. In contrast to single-imager results, the multi-imager channel space resulted in an absolute error in the recovered pulse rate measurement that is comparable with pulse rate measured via fingertip reflectance photoplethysmography. These results demonstrate that non-contact, imaging photoplethysmography can be accurate in the presence of head motion artifact when a multi-imager array is implemented to increase the dimensionality of the decomposed channel space.

Artifacts in Dual-Energy CT Gout Protocol: A Review of 50 Suspected Cases With an Artifact Identification Guide

Abstract: OBJECTIVE. The objective of our study was to discover the types and incidence of artifacts in dual-energy CT (DECT) using datasets of 50 consecutive patients who underwent a four-limb DECT protocol for the evaluation of suspected gout. Identification of artifacts and techniques for artifact reduction are discussed. CONCLUSION. Artifacts commonly occur in DECT performed for gout assessment but are usually readily recognizable. For 90% of the patients in our study who underwent imaging for suspected gout, DECT showed some type of artifact, with nail bed and skin artifacts being the most common.

Identification of Contaminant Type in Surface Electromyography (EMG) Signals

Abstract: The ability to recognize various forms of contaminants in surface electromyography (EMG) signals and to ascertain the overall quality of such signals is important in many EMG-enabled rehabilitation systems. In this paper, new methods for the automatic identification of commonly occurring contaminant types in surface EMG signals are presented. Such methods are advantageous because the contaminant type is typically not known in advance. The presented approach uses support vector machines as the main classification system. Both simulated and real EMG signals are used to assess the performance of the methods. The contaminants considered include: 1) electrocardiogram interference; 2) motion artifact; 3) power line interference; 4) amplifier saturation; and 5) additive white Gaussian noise. Results show that the contaminants can readily be distinguished at lower signal to noise ratios, with a growing degree of confusion at higher signal to noise ratios, where their effects on signal quality are less significant.

Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Abstract: Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.

Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications

Abstract: Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG

Abstract: Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems For practical reasons, a single EEG channel system must be used in these situations Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications The proposed method is shown to significantly outperform all other methods It can successfully remove muscular artifacts without altering the underlying EEG activity It is thus a promising tool for use in ambulatory healthcare systems

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

Abstract: The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity—SE: 84.3%, specificity—SP: 91.5% and accuracy—ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Motion artifact and speckle noise reduction in polarization sensitive optical coherence tomography by retinal tracking

Abstract: We present a novel polarization sensitive optical coherence tomography (PS-OCT) system with an integrated retinal tracker. The tracking operates at up to 60 Hz, correcting PS-OCT scanning positions during the acquisition to avoid artifacts caused by eye motion. To demonstrate the practical performance of the system, we imaged several healthy volunteers and patients with AMD both with B-scan repetitions for frame averaging and with 3D raster scans. Under large retinal motions with up to 1 mm amplitude at 0.5 ~a few Hz frequency range, motion artifact suppression in the PS-OCT images as well as standard deviation noise reduction in the frame averaged retardation images are presented.

A sub-nanometre spindle error motion separation technique

Abstract: This work designs and validates a spindle error motion separation technique having a sub-nanometre measurement uncertainty. This technique overcomes typical measurement error sources arising from sensor, indexing or the repositioning of the artifact. We compare and assess various known reversal and multiprobe techniques by means of a novel error analysis method. From this, we develop an improved implementation of the multiprobe technique, which by-passes accurate indexing of the artifact and sensor(s) during testing, as well as unequal sensor sensitivities, in case multiple sensors are used. This is achieved by measuring the error motion consecutively under three different orientations by rotating the stator of the spindle utilising a high-precision indexing table. These modifications result in a measurement uncertainty that is four times smaller than the conventional multiprobe technique. Furthermore, the suppression of the low-order harmonics is reduced by an optimisation of measurement angles. Finally, several experimental tests are performed to quantify the measurement uncertainty and the influence of the measurement angles on the error separation. Repeatability tests on the radial error motion of an aerostatic rotary table show a measurement uncertainty of 0.455 nm.

Cancellation of motion artifact induced by exercise for PPG-based heart rate sensing.

Abstract: Heart rate (HR) sensing during exercise is essential for medical, healthcare and sport physiological purposes. Photo-Plethysmo-Graphy (PPG) is a simple and non-invasive technique for HR sensing, but it is highly sensitive to motion artifact. This paper proposes a cancellation technique of motion artifact in PPG-based HR sensing for a man during exercise. The canceller is equipped with two sensors; one is a normal PPG sensor where an LED/Photo-Detector (PD) contacts the skin to detect Blood Volume Pulse (BVP) (+motion artifact) and the other is a motion artifact sensor where an LED/PD does not contact the skin to detect only motion artifact. Experimental results show that the proposed technique, which is implemented in adaptive algorithms, can sense HR correctly by cancelling motion artifact induced by exercises such as running and jumping.

Transient Artifact Reduction Algorithm (TARA) Based on Sparse Optimization

Abstract: This paper addresses the suppression of transient artifacts in signals, e.g., biomedical time series. To that end, we distinguish two types of artifact signals. We define “Type 1” artifacts as spikes and sharp, brief waves that adhere to a baseline value of zero. We define “Type 2” artifacts as comprising approximate step discontinuities. We model a Type 1 artifact as being sparse and having a sparse time-derivative, and a Type 2 artifact as having a sparse time-derivative. We model the observed time series as the sum of a low-pass signal (e.g., a background trend), an artifact signal of each type, and a white Gaussian stochastic process. To jointly estimate the components of the signal model, we formulate a sparse optimization problem and develop a rapidly converging, computationally efficient iterative algorithm denoted TARA (“transient artifact reduction algorithm”). The effectiveness of the approach is illustrated using near infrared spectroscopic time-series data.

Hybrid Artifact Removal for Confocal Microwave Breast Imaging

Abstract: Several factors determine the effectiveness of an early-stage artifact removal algorithm for the detection of breast cancer using confocal microwave imaging. These factors include the ability to select the correct time window containing the artifact, the ability to remove the artifact while being robust to normal variances, and ability to effectively preserve the tumor response in the resultant signal. Very few (if any) of the existing artifact removal algorithms incorporate all of these qualities. In this letter, a novel hybrid artifact removal algorithm for microwave breast imaging applications is presented, which combines the best attributes of two existing algorithms to effectively remove the early-stage artifact while preserving the tumor response. This algorithm is compared to existing algorithms using a range of appropriate performance metrics.

Low-complexity sensor displacement tolerant pulse oximetry based heart rate measurement

Abstract: Methods for heart rate measurement based on pulse oximetry are provided that can tolerate some degree of relative displacement of a photoplethysmograph (PPG) heart rate monitor device. In some methods, artifact compensation based on a reference signal is performed on the PPG signal data to remove artifacts in the signal that may be caused, for example, by changes in ambient light and/or motion of a person wearing the monitor device. The reference signal used for artifact compensation may be generated using an LED of a complementary wavelength to that of the LED used to generate the PPG signal, or by driving an LED at a lower current than the current applied to generate the PPG signal.

Metal artifact reduction in patients with dental implants using multispectral three-dimensional data acquisition for hybrid PET/MRI

Abstract: Hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) shows high potential for patients with oropharyngeal cancer. Dental implants can cause substantial artifacts in the oral cavity impairing diagnostic accuracy. Therefore, we evaluated new MRI sequences with multi-acquisition variable-resonance image combination (MAVRIC SL) in comparison to conventional high-bandwidth techniques and in a second step showed the effect of artifact size on MRI-based attenuation correction (AC) with a simulation study. Twenty-five patients with dental implants prospectively underwent a trimodality PET/CT/MRI examination after informed consent was obtained under the approval of the local ethics committee. A conventional 3D gradient-echo sequence (LAVA-Flex) commonly used for MRI-based AC of PET (acquisition time of 14 s), a T1w fast spin-echo sequence with high bandwidth (acquisition time of 3.2 min), as well as MAVRIC SL sequence without and with increased phase acceleration (MAVRIC, acquisition time of 6 min; MAVRIC-fast, acquisition time of 3.5 min) were applied. The absolute and relative reduction of the signal void artifact was calculated for each implant and tested for statistical significance using the Wilcoxon signed-rank test. The effect of artifact size on PET AC was simulated in one case with a large tumor in the oral cavity. The relative difference of the maximum standardized uptake value (SUVmax) in the tumor was calculated for increasing artifact sizes centered over the second molar. The absolute reduction of signal void from LAVA-Flex sequences to the T1-weighted fast spin-echo (FSE) sequences was 416 mm2 (range 4 to 2,010 mm2) to MAVRIC 481 mm2 (range 12 to 2,288 mm2) and to MAVRIC-fast 486 mm2 (range 39 to 2,209 mm2). The relative reduction in signal void was significantly improved for both MAVRIC and MAVRIC-fast compared to T1 FSE (−75%/− 78% vs. − 62%, p < 0.001 for both). The relative error for SUVmax was negligible for artifacts of 0.5-cm diameter (−0.1%), but substantial for artifacts of 5.2-cm diameter (−33%). MAVRIC-fast could become useful for artifact reduction in PET/MR for patients with dental implants. This might improve diagnostic accuracy especially for patients with tumors in the oropharynx and substantially improve accuracy of PET quantification.

Prior-based artifact correction (PBAC) in computed tomography.

Abstract: Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. Methods: The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. Results: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected imagesmore » obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. Conclusions: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.« less