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Motion artifact control in body MR imaging.

The mechanisms involved in the generation of motion artifacts in MR imaging are complex and depend both on the type and direction of motion as well as on the parameters of the imaging sequence chosen. The methods used to control or reduce motion artifacts are multiple and the appropriate method for use with any given clinical situation will depend on the particular hardware and software of the MR imaging unit, the patient's clinical status, and the specific organ or disease state to be imaged. Some general guidelines for clinical use that are applicable in most scenarios can be defined, although preferences for the different techniques vary. Appropriate T1-weighted images of the upper abdomen and liver can be obtained with breath-hold T1-weighted gradient echo. These images should be acquired with inferior-superior spatial presaturation pulses to reduce vascular pulsation artifact and ghosting. The application of GMN will depend on the individual MR imaging system. If sufficient coverage cannot be obtained with gradient-echo imaging, then conventional T1-weighted images with phase-encoding reordering is suggested. The addition of spatial presaturation pulses (inferior-superior) may be valuable. The use of fat suppression will further improve image quality by reducing ghost artifact and improving CNR, although SNR will decrease. T2-weighted imaging of the upper abdomen will depend greatly on the hardware and software of the MR imaging unit. Recent techniques of breath-hold T2-weighted imaging require faster and stronger gradients, and may not be universally available. If available, these techniques provide excellent anatomic detail, although image contrast (e.g., liver to spleen) may decrease. Respiratory-triggered FSE techniques are the preferred method of imaging in most centers, because the imaging time is considerably less than conventional T2-weighted imaging whereas the image quality is improved. Liver lesion detection capability of the various techniques is still under study. The addition of fat suppression appears to improve image quality further with an increase in lesion detection. By understanding the principles underlying motion artifacts, one can choose the appropriate method of artifact control tailored for the individual clinical situation. In addition, the recognition of the variable appearances of motion artifacts will prevent interpretive errors and misdiagnoses. Careful attention to motion artifact reduction techniques can greatly improve patient care.

Artifacts↗

Removing electroencephalographic artifacts by blind source separation.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

Artifacts↗

Statistical control of artifacts in dense array EEG/MEG studies.

With the advent of dense sensor arrays (64-256 channels) in electroencephalography and magnetoencephalography studies, the probability increases that some recording channels are contaminated by artifact. If all channels are required to be artifact free, the number of acceptable trials may be unacceptably low. Precise artifact screening is necessary for accurate spatial mapping, for current density measures, for source analysis, and for accurate temporal analysis based on single-trial methods. Precise screening presents a number of problems given the large datasets. We propose a procedure for statistical correction of artifacts in dense array studies (SCADS), which (1) detects individual channel artifacts using the recording reference, (2) detects global artifacts using the average reference, (3) replaces artifact-contaminated sensors with spherical interpolation statistically weighted on the basis of all sensors, and (4) computes the variance of the signal across trials to document the stability of the averaged waveform. Examples from 128-channel recordings and from numerical simulations illustrate the importance of careful artifact review in the avoidance of analysis errors.

Algorithms↗

Reducing loss of image quality because of the attenuation artifact in uncorrected PET whole-body images.

UNLABELLED: In whole-body PET, it is not unusual to shorten the study time by omitting the transmission scan and to ignore attenuation during reconstruction. If a transmission scan is available, many centers reconstruct the images with, but also without, attenuation correction. Although ignoring attenuation leads to an artifact in the reconstructed images, these images still provide valuable diagnostic information in oncologic applications. Several authors have reported that the attenuation artifact may actually increase the tumor-to-background ratio. In this study, we analyzed the causes of the artifact and proposed a new algorithm to reduce the adverse effects on visual image quality. METHODS: We analyzed the causes of the attenuation artifact mathematically and numerically, and we examined its effect on tumor-to-background ratio and on signal-to-noise ratio. In addition, we showed that the attenuation artifact may lead to loss of image detail in conventional maximum-likelihood expectation maximization (MLEM) reconstruction. A new maximum-likelihood algorithm allowing negative reconstruction values (NEG-ML) was derived to reduce this loss. RESULTS: The attenuation artifact consists of 2 components. The first component is the well-known scaling effect: The apparent activity is reduced because attenuation decreases the fraction of detected photons. The second component is a relatively smooth negative contribution that is added to attenuated regions surrounded by activity. The second component tends to increase the tumor-to-background ratio. However, a simulation experiment shows that this increase in signal may be entirely offset by an increase in noise. The negative contribution can interfere with the nonnegativity constraint of the MLEM algorithm, leading to loss of image detail in regions of high attenuation. The new NEG-ML algorithm avoids the problem by allowing negative pixel values. The algorithm is similar to MLEM in the suppression of the streak artifact but provides more anatomic information. In our department, it is in routine clinical use for reconstruction of PET whole-body images without attenuation correction. CONCLUSION: Ignoring attenuation may increase the tumor-to-background ratio, but this increase does not imply improved tumor detection. The NEG-ML algorithm reduces the adverse effect of the attenuation artifact on visual image quality.

Algorithms↗

[Artifacts in echo-Doppler and color-Doppler].

It is well known that artifacts can be observed during US examinations; the same is true also for Doppler and color-Doppler images of blood flow. Recognizing these artifacts is important to avoid image misinterpretations and, when possible, to overcome them by modifying either techniques or unit settings, or both. This work was aimed at presenting the several artifacts which can be observed during Doppler investigations, at classifying them, and trying to understand the physical and/or technical principles underlying their origin. Doppler and color-Doppler artifacts can be divided into four large groups: 1) artifacts regarding evaluation of the presence of flow; 2) artifacts regarding evaluation of the direction of flow; 3) artifacts regarding determination of the velocity of flow; 4) artifacts affecting spatial location, on the screen, of the examined vessel. Each of the above can cause severe diagnostic misinterpretations, if not correctly recognized and interpreted. It must be kept in mind that an accurate analysis of unit settings during scanning, and the meticulous evaluation of the obtained color images are of the utmost importance for the proper use of this valuable but difficult diagnostic technique.

Artifacts↗

Common artifacts encountered in thoracic magnetic resonance imaging: recognition, derivation, and solutions.

The article discusses the many types of thoracic magnetic resonance imaging artifacts, their derivations, and their current solutions. Magnetic resonance imaging artifacts of the thorax can be divided into two major categories. First, there are machine-related artifacts related to the machine's hardware: the intrinsic magnetic field, chemical shift, magnetic susceptibility, radio frequency leaks, metal artifacts, B1-homogeneity, gradient coils, truncation, aliasing, zipper artifacts, artifacts related to the surface coil profiles, pulse profiles, and crosstalk. Second, there are artifacts related to motion: voluntary patient motion, involuntary motion, and physiologic motions. Each one of these artifacts, and their solutions, will be discussed.

Artifacts↗

[Twinkling artifact in kidney stone disease].

BACKGROUND: Ultrasonography artifacts are false representations of the image caused by the interaction between the ultrasound and the tissues. The ability to identify artifacts is an important source of information that can help the clinician in performing a correct diagnosis. The twinkling artifact (called 'Effetto Arlecchino' by the Italians) consists of a series of colored pixels that appear inside, around and often along the shadow cone of the calculi. METHODS: We evaluated the clinical effectiveness of this artifact in the diagnosis of kidney stone disease. In 107 ultrasonography cases of hyperechogenic formations with clinical features of kidney stones, we used the color box to evoke the twinkling artifact. Of the 107 cases, 102 cases (95%) presented this artifact, while five cases did not. Moreover, this artifact was present in all urethral and bladder stones and in 62/67 kidney stone cases. CONCLUSIONS: In our experience, we found that the twinkling artifact is often positive in urinary stone disease, and the ability to identify it adds useful information to the diagnosis of urinary kidney stone disease.

Artifacts↗

Sellar susceptibility artifacts: theory and implications.

PURPOSE: To investigate the prevalence and physical basis of a specific form of MR susceptibility artifact that may be seen in the pituitary gland near the junction of sellar floor and sphenoidal septum. MATERIALS AND METHODS: Coronal, T1-weighted MR images of the pituitary glands in 50 subjects without clinical evidence of pituitary or sphenoidal sinus disease were reviewed to determine the prevalence of a focal susceptibility artifact near the sellar floor. A plexiglass phantom was constructed to duplicate this artifact in vitro, the appearance of which was studied by varying the direction and intensity of the readout gradient. RESULTS: In the clinical studies, a focal artifact larger than 1 mm2 was observed in MR studies of seven (14%) of 50 subjects and was sufficiently large to mask or mimic pathology in all cases. The location of this artifact was always within the pituitary gland but closely related to the junction of the sphenoidal septum and sellar floor. The artifact was successfully reproduced in the phantom, and its magnitude was shown to be linearly related to the strength and direction of the readout gradient. An explanation for the focal nature and shape of this artifact is presented based on consideration of the boundary conditions of the Maxwell equations of electromagnetism. CONCLUSION: A focal susceptibility artifact may be seen on MR images of the pituitary gland closely related to the junction between the sellar floor and sphenoidal septum that may mimic or obscure a microadenoma.

Adolescent↗

Computerized pattern recognition of EEG artifact.

Automated artifact classification of quantified EEG (QEEG) epochs from 9 males using linear discriminant analysis showed greater than 85% agreement with judges' opinions. These results were replicated (n = 600 epochs for each sample). Testing the entire sample (n = 5800) illustrated reliable eye artifact (94%) but reduced muscle artifact classification (70%) accuracy. Agreement was lowest in the case of more subtle forms of muscle artifact (i.e., low amplitude muscle), however, less than 4% of these were wrongly classified as non-artifact. Improved data collection techniques retaining high frequency energies are anticipated to improve muscle artifact recognition. Results indicate that low levels of artifact contamination would result when only those epochs classified as non-artifact were accepted for inclusion in further analysis.

Blinking↗

Diagnosis of ascending aortic dissection by transesophageal echocardiography: utility of M-mode in recognizing artifacts.

OBJECTIVES: This study sought to assess the reliability of biplanar transesophageal echocardiography in the diagnosis of ascending aortic dissection and to test the utility of M-mode information in the differential diagnosis of ascending aortic ultrasound artifacts and intimal flap images. BACKGROUND: Transesophageal echocardiography is a useful technique in the diagnosis of aortic dissection. However, ultrasound artifacts in the ascending aorta are an important limitation. METHODS: Transesophageal echocardiography was performed in 132 consecutive patients with clinically suspected aortic dissection. Two-dimensional and M-mode echocardiography and color Doppler were used to diagnose intimal flap and artifact images. Diagnoses were validated either anatomically or with reference techniques. RESULTS: The sensitivity and specificity of transesophageal echocardiography in the diagnosis of ascending aortic dissection were 96.8% and 100%, respectively. Ninety-three artifacts were observed in 56 (55%) of 101 patients without ascending aortic dissection. Two-dimensional echocardiography easily identified 74 artifacts (80%). Color Doppler showed no ascending flow abnormalities in 71% of artifact images. M-mode echocardiography showed three location and mobility artifact patterns related to the posterior wall of the aorta or the right pulmonary artery. In contrast, intimal flap movement showed no relation to the aortic wall movement in 25 cases (83%). Blind analysis of transesophageal echocardiographic study tapes underlined the utility of M-mode in the differential diagnosis. Ranges of sensitivity, specificity and positive predictive value (established by including doubtful results as either positive or negative) improved from 87.1-93.5% to 93.5-96.8%, from 85.1-94.1% to 99-100% and from 65.9-81.8% to 96.8-100%, respectively, with the inclusion of M-mode data. CONCLUSIONS: Biplanar transesophageal echocardiography permits reliable diagnosis of ascending aortic dissection. Ultrasound artifacts are common, but assessment of the location and mobility of intraluminal images by M-mode echocardiography definitely improves diagnostic accuracy.

Adolescent↗

Detection of artifacts in monitored trends in intensive care.

In intensive care, decision-making is often based on trend analysis of physiological parameters. Artifact detection is a pre-requisite for interpretation of trends both for clinical and research purposes. In this study, we developed and tested three methods of artifact detection in physiological data (systolic, mean and diastolic artery and pulmonary artery pressures, central venous pressure, and peripheral temperature) using pre-filtered physiological signals (2-min median filtering) from 41 patients after cardiac surgery. These methods were: (1) the Rosner statistic; (2) slope detection with rules; and (3) comparison with a running median (median detection). After tuning the methods using data from 20 randomly chosen patients, the methods were tested using the data from the remaining patients. The results were compared with those obtained by manual identification of artifacts by three senior intensive care unit physicians. Out of an average of 22,480 data points for each variable, the three observers labelled 0.98% (220 data points) as artifacts. The inter-observer agreement was good. The average (range) sensitivity for artifact detection in all variables in the test database was 66% (33-92%) for the Rosner statistic, 64% (24-98%) for slope detection and 72% (41-98%) for median detection. All methods had a high specificity (> or = 94%). Slope detection had the highest mean positive prediction rate (53%; 21-85%). When the performance was measured by the cost function, slope detection and running median performed equally well and were superior to Rosner statistics for systemic arterial and central venous pressure and peripheral temperature. None of the methods produced acceptable results for pulmonary artery pressures. We conclude that median filtering of physiological variables is effective in removing artifacts. In post-operative cardiac surgery patients, the remaining artifacts are difficult to detect among physiological and pathophysiological changes. This makes large databases for tuning artifact algorithms mandatory. Despite these limitations, the performance of running median and slope detection were good in selected physiological variables.

Algorithms↗

An approach to artifact identification: application to heart period data.

A rational strategy for the automated detection of artifacts in heart period data is outlined and evaluated. The specific implementation of this approach for heart period data is based on the distribution characteristics of successive heart period differences. Because beat-to-beat differences generated by artifacts are large, relative to normal heart period variability, extreme differences between successive heart periods serve to identify potential artifacts. Critical to this approach are: 1) the derivation of the artifact criterion from the distribution of beat differences of the individual subject, and 2) the use of percentile-based distribution indexes, which are less sensitive to corruption by the presence of artifactual values than are least-squares estimates. The artifact algorithms were able to effectively identify artifactual beats embedded in heart period records, flagging each of the 1494 simulated and actual artifacts in data sets derived from both humans and chimpanzees. At the same time, the artifact algorithms yielded a false alarm rate of less than 0.3%. Although the present implementation was restricted to heart period data, the outlined approach to artifact detection may also be applicable to other biological signals.

Algorithms↗

Artifacts associated with implementation of the Grangeat formula.

To compensate for image artifacts introduced in approximate cone-beam reconstruction, exact cone-beam reconstruction algorithms are being developed for medical x-ray CT. Although the exact cone-beam approach is theoretically error-free, it is subject to image artifacts due to the discrete nature of numerical implementation. We report a study on image artifacts associated with the Grangeat algorithm as applied to a circular scanning locus. Three types of artifacts are found, which are thorn, wrinkle, and V-shaped artifacts. The thorn pattern is created by inappropriate extrapolation into the shadow zone in the radon domain. If the shadow zone is filled in with continuous data, the thorn artifacts along the boundary of the shadow zone can be removed. The wrinkle appearance arises if interpolated first derivatives of the radon data are not smooth between adjacent detector planes. In particular, the nearest-neighbor interpolation method should not be used. If the number of projections is not small, the bilinear interpolation method is effective to suppress the wrinkle artifacts. The V-shaped artifacts on the meridian plane come from the line integrations through the transition zones where derivative data change abruptly. Two remedies are to increase the sampling rate and suppress data noise.

Algorithms↗

Magnetic resonance imaging artifacts: mechanism and clinical significance.

Many types of artifacts may occur in magnetic resonance imaging. These artifacts may be related to extrinsic factors such as patient motion or metallic artifacts; they may be due specifically to the MR system such as power gradient drop off and chemical shift artifacts; they may occur as a consequence of general image processing techniques, as in the case of truncation artifacts and aliasing. Change in patient position, pulse sequence, or other imaging variables may improve some artifacts. Although reduction of some artifacts may require a service engineer, the radiologist has the responsibility to recognize MR imaging problems. The radiologist's knowledge of MR imaging artifacts is important to the continued maintenance of high image quality and is essential if one is to avoid confusing artifactual appearances with pathology.

Diagnostic Errors↗

Artifacts and pitfalls of high-resolution CT scans.

Artifacts on CT images have been observed since the introduction of CT scanners. Some artifacts have been corrected with the improvement of technology and better understanding of the image formation and reconstruction algorithms. Some artifacts, however, are still observable in state-of-the-art high-resolution scans. Many investigations on CT artifacts have been reported. Some artifacts are obvious and some are similar to patterns commonly associated with pathological conditions. The present report summarizes some of the causes of artifacts and presents some artifacts that mimic pathology on clinical scans of the head and spine. It is the intention of this report to bring these artifacts and potential pitfalls to the attention of the radiologists so that misinterpretation can be avoided.

Brain Diseases↗

Pulsatile flow artifacts in fast magnetization-prepared sequences.

Fast magnetization-prepared magnetic resonance imaging sequences allow clinical acquisitions in about 1 second, with the preparation phase providing the desired contrast. Pulsatile flow artifacts, although reduced by rapid acquisition, can degrade image quality. The authors explore the causes of aortic pulsatile flow artifacts in inversion-recovery-prepared acquisitions of the abdomen, taking into consideration various parameters. The flow signal within an 8-mm-thick section was simulated and subsequently Fourier transformed to determine the location and extent of flow artifacts. Results of simulations were validated with abdominal images of human subjects. Recording all encodings within one cardiac cycle reduced pulsatile flow artifacts in nonsegmented acquisitions with sequential phase-encoding order, regardless of the location of magnetization preparation within the cardiac cycle. In segmented acquisitions, however, the sequential order always increased flow artifacts. To reduce the artifacts in short TI acquisitions, the magnetization should be prepared during diastole. In clinical acquisitions, flow artifacts were further reduced by modifying the phase-encoding scheme.

Aorta, Abdominal↗

Nonsusceptibility artifacts due to metallic objects in MR imaging.

The authors investigated eddy current artifacts due to metallic objects within the magnetic resonance imaging field. The problem was simplified by using a circular copper loop as a model for the more complex eddy current pathways present in a metallic implant. With this simple geometry, the authors show that radio-frequency (RF)-induced eddy currents in the metal produce a significant local artifact. However, no appreciable artifacts due to the switching magnetic field gradients were observed. A detailed quantitative analysis of the mechanism of RF-induced eddy current artifact due to the the copper loop was performed. The artifact was demonstrated experimentally to result from perturbations of the transmit and receive sensitivities of the RF coil. Theoretical calculations of these perturbations showed excellent agreement with experimental results. With an understanding of the artifact mechanism, methods for correcting the RF-induced eddy current artifact were applied.

Artifacts↗

Magnetic resonance imaging artifacts caused by aneurysm clips and shunt valves: dependence on field strength (1.5 and 3 T) and imaging parameters.

PURPOSE: To evaluate artifact sizes at 3 T compared to at 1.5 T, and to evaluate the influence of scanning parameters with respect to artifact size on a 3-T magnetic resonance imaging (MRI) system. MATERIALS AND METHODS: Two aneurysm clips and five shunt valves were imaged in a water phantom at 1.5 and 3 T. At 3 T the influence of bandwidth (spin echo (SE) images) and echo time (gradient echo (GRE) images) on artifact size (area and extension in two orthogonal directions) was investigated. RESULTS: Artifact sizes increased substantially (typically 5-10 mm) at 3 T, compared to at 1.5 T, for implants entirely made of metallic materials, whereas the increase was the size less prominent (0-5 mm) for implants only partly containing metal. Artifact areas could be altered by changing the bandwidth or the echo time to about the same extent as it was affected by the increased field strength. CONCLUSION: Artifact sizes increase at 3 T, compared to at 1.5 T, depending on the type and composition of the implant, but can be substantially reduced by altering the imaging parameters. Optimization of imaging protocols to minimize artifacts is therefore important at higher field strengths.

Artifacts↗