Abstract: Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.

Abstract: Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions.

Abstract: Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.

Abstract: Purpose In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. Method We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. Results The experimental results confirmed that the proposed algorithm effectively removes low-dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. Conclusions The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

Abstract: Future electroencephalogram (EEG) recordings in body sensor networks are prone to be contaminated by muscle activity due to the mobile, long-term, and pervasive monitoring needs. In this paper, a novel approach for muscle artifact removal in EEG is proposed by combining ensemble empirical mode decomposition (EEMD) with canonical correlation analysis (CCA), termed as EEMD-CCA. This approach can make good use of inter-channel information. We tested the approach on simulated, semi-simulated, and real-life data sets, respectively. The approach outperformed state-of-the-art techniques, including independent component analysis, CCA, and EEMD-ICA. Statistical tests demonstrate the significance ( $p ) in (semi)-simulated studies. The relative root-mean-squared error can be reduced to around 0.3 and the average correlation coefficient can be kept above 0.9 even when the contamination is quite heavy (SNR < 2). Besides, we also tested the approach on few-channel EEG randomly selected from multichannel EEG, and obtained competitive results. The computational cost satisfies the real-time requirement. This indicates that the proposed EEMD-CCA approach is applicable under both multichannel and few-channel settings. It is an effective and efficient signal processing tool for enhancing the signal of interest in both hospital and home healthcare body sensor networks.

Abstract: Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced...

Abstract: Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations. The method was trained and tested on data obtained from St Anne's University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.

Abstract: This work presents an efficient method for motion artifact removal from ambulatory electrocardiogram (ECG) signal for heart rate variability (HRV) in wearable/portable healthcare devices. HRV is the fluctuation in the time interval between the adjacent heartbeats. Motion artifacts affect HRV analysis by creating some outliers. A two-phase method using stationary wavelet transform with level thresholding (SWT-LT) is used to remove motion artifact from the ECG signal. Multi-channel system prototype is used for ambulatory ECG signal recording which is developed using commercial integrated circuit components. Motion artifact affected ECG signals are recorded by emulating daily activity movements. Recorded ECG database (60 signals) and Motion Artifact Contaminated ECG Database (27 signals) are used for validation of the proposed SWT-LT method. Implemented results show that the proposed SWT-LT method removes various in-band motion artifacts efficiently with an average correlation coefficient of 0.9337 and an average normalized mean square error of 0.012 which are better than the other reported methods. The proposed method has shown improvement in features of HRV analysis by removing outliers due to motion artifact from the ECG signal which is verified using MATLAB app HRVTool 1.03 developed by Marcus Vollmer.

Abstract: Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems.

Abstract: The electroencephalogram (EEG) signal is contaminated with various noises or artifacts during recording. For the automated detection of neurological disorders, it is a vital task to filter out these artifacts from the EEG signal. In this paper, we propose two novel approaches for the removal of motion artifact from the single channel EEG signal. These methods are based on the multiresolution total variation (MTV) and multiresolution weighted total variation (MWTV) filtering schemes. The multiresolution analysis using the discrete wavelet transform (DWT) helps to segregate the EEG signal into various subband signals. The total variation (TV) and weighted TV (WTV) are applied to the approximation subband signal. The filtered approximation subband signal is evaluated based on the difference between the noisy approximation subband signal and the output of the TV or WTV filter. The processed EEG signal is obtained using the multiresolution wavelet-based reconstruction. The difference in the signal to noise ratio ( $\Delta $ SNR) and the percentage of reduction in correlation coefficients ( $\eta $ ) is used for evaluating the diagnostic quality of the processed EEG signal. The experimental results demonstrate that the proposed MTV and MWTV approaches have better denoising performance with (average $\Delta $ SNR, and average $\eta $ ) values of (29.12 dB and 68.56%) and (29.29 dB and 67.51%), respectively, as compared to the existing techniques.

Abstract: As the electroencephalography (EEG) biomedical signals are affected under the presence of the muscular motion artifacts. Presence of these artifacts leads to error in visual analysis of EEG signal, thus results in wrong diagnosis of human diseases. The variants of blind source separation (BSS) methods are available. This paper aims to design the efficient BSS based method for effectively eradicating the EEG motion artifacts. This is accomplished by evaluating the six different methods, which are combination of independent component analysis (ICA) and canonical correlation analysis (CCA) along with the discrete wavelet transform and stationary wavelet transform methods. Each of above combination methods are applied on the ensemble empirical mode decomposed, Intrinsic Mode Functions for EEG motion artifact suppression. This research paper tests the performance over pure EEG signal and also on the simulated EEG sinusoids to mimic the effect of motion artifacts. The performance of six BSS artifact removal algorithms are evaluated using efficiency matrices such as del signal to noise ratio, lambda (λ), spectral distortion (Pdis) and root mean square error. The execution time is also calculated to evaluate the computation efficiency of the algorithms. The results suggest that CCA algorithm outperforms over ICA in the case of the high noisy condition of EEG signal.

Abstract: This study compares reduction of strong metal artifacts from large dental implants/bridges using spectral detector CT-derived virtual monoenergetic images (VMI), metal artifact reduction algorithms/reconstructions (MAR), and a combination of both methods (VMIMAR) to conventional CT images (CI). Forty-one spectral detector CT (SDCT) datasets of patients that obtained additional MAR reconstructions due to strongest artifacts from large oral implants were included. CI, VMI, MAR, and VMIMAR ranging from 70 to 200 keV (10 keV increment) were reconstructed. Objective image analyses were performed ROI-based by measurement of attenuation (HU) and standard deviation in most pronounced hypo-/hyperdense artifacts as well as artifact impaired soft tissue (mouth floor/soft palate). Extent of artifact reduction, diagnostic assessment of soft tissue, and appearance of new artifacts were rated visually by two radiologists. The hypo-/hyperattenuating artifacts showed an increase and decrease of HU values in MAR and VMIMAR (CI/MAR/VMIMAR-200keV: − 369.8 ± 239.6/− 37.3 ± 109.6/− 46.2 ± 71.0 HU, p < 0.001 and 274.8 ± 170.2/51.3 ± 150.8/36.6 ± 56.0, p < 0.001, respectively). Higher keV values in hyperdense artifacts allowed for additional artifact reduction; however, this trend was not significant. Artifacts in soft tissue were reduced significantly by MAR and VMIMAR. Visually, high-keV VMI, MAR, and VMIMAR reduced artifacts and improved diagnostic assessment of soft tissue. Overcorrection/new artifacts were reported that mostly did not hamper diagnostic assessment. Overall interrater agreement was excellent (ICC = 0.85). In the presence of strong artifacts due to large oral implants, MAR is a powerful mean for artifact reduction. For hyperdense artifacts, MAR should be supplemented by VMI ranging from 140 to 200 keV. This combination yields optimal artifact reduction and improves the diagnostic image assessment in imaging of the head and neck. • Large oral implants can cause strong artifacts. • MAR is a powerful tool for artifact reduction considering such strong artifacts. • Hyperdense artifact reduction is supplemented by VMI of 140–200 keV from SDCT.

Abstract: Objective Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data. Approach Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity. Main results SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain. Significance This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.

Abstract: In this paper, we analyze the statistics of error signals to assess the perceived quality of images Specifically, we focus on the magnitude spectrum of error images obtained from the difference of reference and distorted images Analyzing spectral statistics over grayscale images partially models interference in spatial harmonic distortion exhibited by the visual system but it overlooks color information, selective and hierarchical nature of visual system To overcome these shortcomings, we introduce an image quality assessment algorithm based on the S pectral U nderstanding of M ulti-scale and M ulti-channel E rror R epresentations, denoted as SUMMER We validate the quality assessment performance over 3 databases with around 30 distortion types These distortion types are grouped into 7 main categories as compression artifact, image noise, color artifact, communication error, blur, global and local distortions In total, we benchmark the performance of 17 algorithms along with the proposed algorithm using 5 performance metrics that measure linearity, monotonicity, accuracy, and consistency In addition to experiments with standard performance metrics, we analyze the distribution of objective and subjective scores with histogram difference metrics and scatter plots Moreover, we analyze the classification performance of quality assessment algorithms along with their statistical significance tests Based on our experiments, SUMMER significantly outperforms majority of the compared methods in all benchmark categories

Abstract: Affective human-robot interaction requires lightweight software and cheap wearable devices that could further this field. However, the estimation of emotions in real-time poses a problem that has not yet been optimized. An optimization is proposed for the emotion estimation methodology including artifact removal, feature extraction, feature smoothing, and brain pattern classification. The challenge of filtering artifacts and extracting features, while reducing processing time and maintaining high accuracy results, is attempted in this work. First, two different approaches for real-time electro-oculographic artifact removal techniques are tested and compared in terms of loss of information and processing time. Second, an emotion estimation methodology is proposed based on a set of stable and meaningful features, a carefully chosen set of electrodes, and the smoothing of the feature space. The methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation on the SEED database, both under subject dependent and subject independent paradigms, to test the methodology on a discrete emotional model with three affective states.

Abstract: Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. In this paper, we introduce the Riemannian potato field (RPF) algorithm as such SQI. It is a generalization and extensionof theRiemannian potato, a previouslypublished real-time artifact detection algorithm, whose performance is degraded as the number of channels increases. The RPF overcomes this limitation by combining the outputs of several smaller potatoes into a unique SQI resulting in a higher sensitivity and specificity, regardless of the number of electrodes. We demonstrate these results on a clinical dataset totalizing more than 2200 h of EEG recorded at home, that is, in a non-controlled environment.

Abstract: The pre-ejection period (PEP) is a valid index of myocardial contractility and beta-adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B-point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble-averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long-duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B-point detection algorithm in different sleep stages and in-bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters.

Abstract: Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

Abstract: Sleep Apnea Syndrome is one of the most common and dangerous causes of sleep disorder that the suspected patients are tested (examined) by recording various types of vital signals during sleep using polysomnography (PSG). Since human body rhythms have a chaotic and non-linear behavior, the nonlinear analysis of body parameters provides the researchers with valuable information about body behavior during the disease and its comparison with the normal state for a more accurate examination of the diseases. The purpose of this is to diagnose apnea events using linear and nonlinear analyses and combining the EMG, ECG and EEG signals in patients with Obstructive Sleep Apnea (OSA). The research data are obtained by the Physionet database including 25 subjects (21 males and 4 females). After performing the pre-processing phase to remove the noise related to EMG, ECG, EEG and artifact signals based on the corresponding algorithms, the healthy and apnea sleep ranges are separated from one another. Linear and nonlinear analyses in MATLAB environment are performed on signals and conditions which are evaluated in healthy sleep and during sleep apnea at different stages of sleep in patients with OSA by multilayer perceptron classifier. The best result of the proposed algorithm obtained by combining the signals and the specificity, sensitivity and accuracy values are 96.87 ± 1.78, 97.14 ± 2.24 and 98.09 ± 2.15 respectively. The results show that the proposed algorithm can help doctors and nurses as a diagnostic tool with more accuracy than similar techniques.

Abstract: Purpose This in vitro study assessed the artifact production related to titanium and zirconia implants in cone beam computed tomography (CBCT) and compared the effect of different protocol settings on image quality for both materials. Materials and methods A titanium implant and a zirconia implant were placed in a dry mandible. CBCT scans were obtained separately for each implant using the ProMax 3D (Planmeca) unit; 20 protocols were tested with varying kilovoltage (70 to 90 kVp) and resolution (high and low), and with and without a metal artifact reduction tool. Standard deviation and contrast-noise ratio were calculated in regions of interest adjacent and distant to the implant. Results The zirconia produced more artifacts and its images were more affected by the different protocols than titanium. High kVps and an activated metal artifact reduction tool decreased the standard deviation values related to both implants. Activation of the metal artifact reduction tool also increased contrast-noise ratio values for both implants, whereas increasing kVp improved them only on titanium images. The standard deviation and contrast-noise ratio were not affected by resolution. Conclusion The zirconia implant generated more image artifacts than the titanium implant. Increasing kVp and the metal artifact reduction tool are efficient in decreasing the CBCT artifacts for both implants, whereas resolution does not affect their production.

Abstract: A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact removal (HEAR) algorithm to remove transient electrode pop and drift (PD) artifacts from electroencephalographic (EEG) signals. Transient PD artifacts reflect impedance variations at the electrode scalp interface that are caused by ion concentration changes. HEAR and its online version (oHEAR) are open-source and publicly available. Both outperformed state of the art offline and online transient, high-variance artifact correction algorithms for simulated EEG signals. (o)HEAR attenuated PD artifacts by approx. 25 dB, and at the same time maintained a high SNR during PD artifact-free periods. For real-world EEG data, (o)HEAR reduced the fraction of outlier trials by half and maintained the waveform of a movement related cortical potential during a center-out reaching task. In the case of BCI training, using oHEAR can improve the reliability of the feedback a user receives through reducing a potential negative impact of PD artifacts.

Abstract: Nighttime image captured in low- or non-uniform illumination scene always suffers from the loss of visibility and contains various noise and objectionable artifact. When we enlarge the amplitude of the brightness, the noise and artifact will be amplified as well. Hence, we propose a nighttime image enhancement approach based on image decomposition. We decompose the input image into two components: Structure layer contains main information of the image, and texture layer contains details, noise, and artifacts. We implement an improved retinex image enhancement algorithm to enhance the structure layer. To remain details and suppress noise and artifact in the texture layer, we use mask-weighted least squares method. In the final, we fuse these two components to obtain the result. The experimental results demonstrate that the proposed approach can improve the perceptual quality of nighttime images and suppress noise and artifact without excessive reinforcement.

Abstract: Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that enables different forms of generations and regularizations between the artifact-affected and artifact-free image domains to support unsupervised learning. Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset. When applied to clinical datasets, our method achieves considerable improvements over the supervised models. The source code of this paper is publicly available at this https URL.

Abstract: OBJECTIVE. The purpose of this study was to compare respiratory motion artifact and diagnostic image quality between end-inspiration and end-expiration breath-holding techniques on unenhanced and c...

Abstract: Metal hardware serves as a common artifact source in spine CT imaging in the form of beam-hardening, photon starvation, and streaking. Postprocessing metal artifact reduction techniques have been developed to decrease these artifacts, which has been proved to improve visualization of soft-tissue structures and increase diagnostic confidence. However, metal artifact reduction reconstruction introduces its own novel artifacts that can mimic pathology.