Artifact Detection Research Articles

Evaluation of ArmedXpert software tools, MixtureAce and Mixture Interpretation, to analyze MPS-STR data

Massively parallel sequencing (MPS) technologies have revolutionized studies of genomic variations and transformed DNA analysis in multiple fields. Assays based on MPS must be capable of discriminating variations introduced by the method, i.e. artifacts from true polymorphisms. In PCR-MPS methods targeting microsatellite markers, artifacts can arise from PCR mis-incorporation, PCR strand slippage (stutter), and sequencing error. Reliable detection of artifacts in mixed DNA samples is a significant challenge that must be addressed in forensic DNA analysis. The ArmedXpert (NicheVision) software tools, MixtureAce™ and Mixture Interpretation, can analyze MPS data by categorizing sequence reads in alleles, stutter, and non-stutter artifacts and analyzing autosomal STR loci of mixed samples. In this study, we evaluated the ArmedXpert tools for the analysis of STR profiles of single-sourced and mixed samples generated by the ForenSeq™ DNA Signature Prep kit (Verogen). Data from eight experimental runs (240 samples) were analyzed: one benchmark run, two runs testing sensitivity with down to 50 pg DNA input, one run testing artificially degraded samples and DNA derived from bones, blood cards and teeth, as well as four runs with mixed DNA samples of varying ratios, sex, and different number of contributors (two to six). The MixtureAce stutter thresholds were initially set following the recommendations from Verogen, plus a non-stutter artifact threshold was set at 5% of allele read counts. A benchmark run, of 30 samples, plus two controls, containing 2310 total alleles, revealed over 5000 artifacts, above an analytical threshold of 10. A total of 4869 artifacts were correctly classified, while 435 were mis-classified as alleles due to exceedance of initial threshold settings. False positives must be resolved by an analyst, which can be time consuming. Stutter thresholds were adjusted based on the benchmark data and the samples were re-tested, resulting in only 57 false positive allele calls. The revised settings were then used in the analysis of the remaining seven experimental runs. Results show that MixtureAce can accurately classify artifacts and alleles when laboratory-specific threshold settings are used. The Mixture Interpretation tool was applied on two- and three-person mixtures. This tool utilized the analyzed data from MixtureAce to calculate, based on the number of alleles at a locus and their read counts, possible deconvolution outcomes with their respective ratios.

Heart rate variability analyzed by computer science

Computer science is being used in medical applications with very good results. In this work, artificial neural networks are used as intelligent techniques that allow analyzing the heart rate variable to anticipate changes in the autonomic nervous system, which seeks to maintain the homeostasis of the organism and the execution of adaptive responses to environmental changes. This clinical examination has been little explored, and we want to contribute with this work to correct ectopic beats and artifacts detected in an electrocardiographic signal for the precise calculation of heart rate variability. The reverse propagation algorithm is used. The ectopic beat detection and correction program is validated based on the calculation of the sensitivity, specificity, and accuracy of ectopias and artifacts detection. A sample of 40 electrocardiogram signals is taken using for training and testing. With the proposed method, an accuracy of 82.34%, a sensitivity of 89.22% and a specificity of 95.78%.

Training-Free Artifact Detection Method for Radio Tomographic Imaging Based Device-Free Localization

Radio tomographic imaging (RTI) is a promising device-free localization (DFL) method by reconstructing an attenuation image from the variations of received signal strength (RSS) measurements. Targets are considered to be at positions of some pixels with local maximums in the attenuation image. However, the number of local maximums generally exceeds the number of real targets, which will lead to large localization errors. The local maximum which does not have its corresponding real target is defined as an “artifact”. Through our analysis, two causes for artifacts are concluded: 1) multi-path interference: RSS measurements for links that are far from the targets may have variations due to multi-path interference, even though these links are not blocked by the targets. 2) intersecting affected links: the affected links blocked by different targets may intersect within the monitoring area, fabricating artifacts. At a position of artifact, there seems to be a target whose interference effect on links is similar to that of a real target. The quality of the attenuation image will deteriorate because of the above two inevitable influencing factors. To alleviate the effect of artifacts, a training-free artifact detection method is proposed. We formulate the artifact detection problem in the attenuation image as a multi-component optimization problem combing the information of feature links and the density of intersection points. The alternating direction method of multipliers (ADMM) is used to derive a closed-form solution. Experiments are conducted to validate the advantages of the proposed artifact detection method.

Carotid Sound Signal Artifact Detection based on Discrete Wavelet Transform Decomposition

Abstract Auscultation methods allow a non-invasive diagnosis of cardiovascular diseases like atherosclerosis based on blood flow sounds of the carotid arteries. Since this process is highly dependent on the clinician’s experience, it is of great interest to develop automated data processing techniques for objective assessment. We have recently proposed a computerassisted auscultation system that we use to acquire carotid blood flow sounds. In this work, we present an approach for detecting artifacts within the blood flow sound caused by swallowing or coughing events. For this purpose, we first decompose the signal using a discrete wavelet transform (DTW). Then, we compute an energy ratio between the DWT scales associated with the signal information with and without artifacts using a sliding window of 1 s length. Evaluation based on Kruskal-Wallis and Wilcoxon rank-sum tests shows a statistically significant difference (p-value<.0001) between the signal with and without artifact. Therefore, the proposed method allows the identification of the studied signal artifacts.

Detection and removal of dust artifacts in retinal images via sparse-based inpainting

Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule

Analysis of the Effectiveness of Various Types of Conservative Therapy for Chronic Bacterial Prostatitis

Analysis of the Effectiveness of Various Types of Conservative Therapy for Chronic Bacterial Prostatitis

Compressed Video Quality Index Based on Saliency-Aware Artifact Detection.

Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.

Artificial intelligence for quality control of oscillometry measures

BackgroundThe forced oscillation technique (FOT) allows non-invasive lung function testing during quiet breathing even without expert guidance. However, it still relies on an operator for excluding breaths with artefacts such as swallowing, glottis closure and coughing. This manual selection is operator-dependent and time-consuming. We evaluated supervised machine learning methods to exclude breaths with artefacts from data analysis automatically. MethodsWe collected 932 FOT measurements (Resmon Pro Full, Restech) from 155 patients (6–87 years) following the European Respiratory Society (ERS) technical standards. Patients were randomly assigned to either a training (70%) or test set. For each breath, we computed 71 features (including anthropometric, pressure stimulus, breathing pattern, and oscillometry data). Univariate filter, multivariate filter and wrapper methods for feature selection combined with several classification models were considered. ResultsTrained operators identified 4333 breaths with- and 10244 without artefacts. Features selection performed by a wrapper method combined with an AdaBoost tree model provided the best performance metrics on the test set: Balanced Accuracy = 85%; Sensitivity = 79%; Specificity = 91%; AUC-ROC = 0.93. Differences in FOT parameters computed after manual or automatic breath selection was less than ∼0.25 cmH2O*s/L for 95% of cases. ConclusionSupervised machine-learning techniques allow reliable artefact detection in FOT diagnostic tests. Automating this process is fundamental for enabling FOT for home monitoring, telemedicine, and point-of-care diagnostic applications and opens new scenarios for respiratory and community medicine.

Blood Oxygen Level-Dependent Lag Patterns Differ Between Rest and Task Conditions, but Are Largely Typical in Autism.

Background/Introduction: Autism spectrum disorder (ASD) is characterized by atypical functional connectivity (FC) within and between distributed brain networks. However, FC findings have often been inconsistent, possibly due to a focus on static FC rather than brain dynamics. Lagged connectivity analyses aim at evaluating temporal latency, and presumably neural propagation, between regions. This approach may, therefore, reveal a more detailed picture of network organization in ASD than traditional FC methods. Methods: The current study evaluated whole-brain lag patterns in adolescents with ASD (n = 28) and their typically developing peers (n = 22). Functional magnetic resonance imaging data were collected during rest and during a lexico-semantic decision task. Optimal lag was calculated for each pair of regions of interest by using cross-covariance, and mean latency projections were calculated for each region. Results: Latency projections did not regionally differ between groups, with the same regions emerging among the "earliest" and "latest." Although many of the longest absolute latencies were preserved across resting-state and task conditions, lag patterns overall were affected by condition, as many regions shifted toward zero-lag during task performance. Lag structure was also strongly associated with literature-derived estimates of arterial transit time. Discussion: Results suggest that lag patterns are broadly typical in ASD but undergo changes during task performance. Moreover, lag patterns appear to reflect a combination of neural and vascular sources, which should be carefully considered when interpreting lagged FC. Impact statement Altered brain dynamics have been proposed in autism spectrum disorder (ASD). Lagged functional connectivity analysis uses cross-correlation between functional magnetic resonance imaging (fMRI) time series to determine regional latency. Few studies have examined blood oxygen level-dependent (BOLD) lag in ASD, and findings have been inconsistent. Using multi-echo fMRI data with improved artifact detection and removal, we find differences in lag structure between task and rest states, but not between adolescents with ASD and typically developing peers. Additional analyses exploring links with arterial transit time, however, highlight the impact of vascular organization on BOLD lag patterns and its potential to confound measures of neural dynamics.

Application of Computer Vision Technology in Industrial Automation

With the continuous development of artificial intelligence and Internet of Things technology, the manufacturing industry is transforming and upgrading from automation to intelligence. Countries continue to increase support and investment in intelligent manufacturing from the policy and financial aspects. This paper mainly studies the application of computer vision technology in the field of industrial automation. In this paper, the sparse representation algorithm is applied to the category detection of artifacts with arbitrary orientation. By establishing the Gabor feature dictionary of the workpiece and solving the sparse reconstruction solution according to the feature vector of the detected workpiece image, the category label of the workpiece is obtained. Aiming at the reconstruction of 3D point cloud of workpiece, this paper uses the method of feature point matching to realize the reconstruction of 3D point cloud. Finally, the workpiece detection method studied in this paper is implemented on the system and the performance of the algorithm is verified.

DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image.

Super-resolution fluorescence microscopy, with a spatial resolution beyond the diffraction limit of light, has become an indispensable tool to observe subcellular structures at a nanoscale level. To verify that the super-resolution images reflect the underlying structures of samples, the development of robust and reliable artifact detection methods has received widespread attention. However, the existing artifact detection methods are prone to report false alert artifacts because it relies on absolute intensity mismatch between the wide-field image and resolution rescaled super-resolution image. To solve this problem, we proposed DETECTOR, a structural information-guided artifact detection method for super-resolution images. It detects artifacts by computing the structural dissimilarity between the wide-field image and the resolution rescaled super-resolution image. To focus on structural similarity, we introduce a weight mask to weaken the influence of strong autofluorescence background and proposed a structural similarity index for super-resolution images, named MASK-SSIM. Simulations and experimental results demonstrated that compared with the state-of-the-art methods, DETECTOR has advantages in detecting structural artifacts in super-resolution images. It is especially suitable for wide-field images with strong autofluorescence background and super-resolution images of single molecule localization microscopy (SMLM). DETECTOR has extreme sensitivity to the weak signal region. Moreover, DETECTOR can guide data collection and parameter tuning during image reconstruction.

PH-0655 Filling in the allegedly unknown: Detection and inpainting of interpolation artifacts in 4DCT images

PH-0655 Filling in the allegedly unknown: Detection and inpainting of interpolation artifacts in 4DCT images

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

AbstractEffective detection and removal of the artifacts from dermoscopic images require the analysis of structures within lesions for accurate cancer detection. But the dermoscopic images acquired may be affected by image acquisition noise, variation in texture, color, and irrelevant structure such as hairs, air bubbles, and contour blurring around the lesion. Hence preprocessing is very essential for accurate diagnosis. The proposed algorithm includes the preprocessing, to remove acquisition noise using curvelet transform, illumination correction using contrast limited adaptive histogram equalization (CLAHE) algorithm for image enhancement, Frangi vesselness algorithm is utilized for detection and removal of hairs and vessels like structures present in enhanced image, fast marching inpainting method is applied for repairing the information of lesion from removed hair pixels. The performance of the proposed algorithm is analyzed based on commonly used parameters such as diagnostic accuracy (DA), sensitivity (SE), specificity (SP), and precision (P). The performance is evaluated on PH2 and International Symposium on Biomedical Imaging (ISBI) datasets than compared with existing methods. The proposed algorithm segments the skin lesion with the highest diagnostic accuracy of 81.49% and sensitivity of 93.88% for the PH2 dataset and 75.39% and 79.34% respectively for the ISBI dataset. Results are better than existing methods. The proposed algorithm with improved preprocessing for artifact detection and removal is highly accurate and able to retrieve the hair occluded information.

Dietary Shifts and Risks of Artifact Ingestion for Argentine Shortfin Squid Illex argentinus in the Southwest Atlantic

Cephalopods play an important role in both ecology and fisheries. Variations in the dietary compositions of squids with large populations further promote interactions between different trophic levels in marine ecosystems. Moreover, due to marine pollution, squids are also at risks of artifact ingestion, and there is limited understanding about the influence of artifacts on the feeding behavior of squids. We examined 296 stomachs of the Argentine shortfin squid Illex argentinus collected through commercial catches across the Southwest Atlantic from February to April of 2018 and 2019 to establish the monthly dietary compositions and risks of artifact ingestion. The results supported typical observations that the Argentine shortfin squids switched from a diet dominated by crustaceans at small sizes to consuming larger prey, predominantly fishes and/or cephalopods, at large sizes during their growth and southward migration. Significantly higher consumption of fishes was observed in recent years compared with that observed in other studies since 1992. The ingested artifacts examined were composed of plastic and non-plastic materials. Artifacts were observed in 19.9% of the total number of stomachs, with 20.5% of the empty stomachs containing artifact remains. The results indicate that although the main dietary compositions of the Argentine shortfin squid maintain the taxonomic groups of prey compositions, the dietary structure, i.e., composition percentage, is varying. The detection of artifacts suggests that environmental monitoring is needed in this region in order to manage and conserve the squid and safeguard aquatic food safety.

Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study.

BackgroundPrevious research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed.ObjectiveThis research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted from the EDA and HRV signals and self-reported outcomes.MethodsParticipants (N=11; female: n=10) were asked to wear an Empatica E4 wearable sensor for continuous unobtrusive physiological signal collection for up to 14 days. During the same time frame, participants responded to a daily diary study using ecological momentary assessment of self-reported stress, emotions, alcohol-related cravings, pain, and discomfort via a web-based survey, which was conducted 4 times daily. Participants also participated in structured interviews throughout the study to assess daily alcohol use and to validate self-reported and physiological stress markers. In the analysis, we first used existing artifact detection methods and physiological signal processing approaches to assess the quality of the physiological data. Next, we examined the descriptive statistics for self-reported outcomes. Finally, we investigated the associations between the features of physiological signals and self-reported outcomes.ResultsWe determined that 87.86% (1,032,265/1,174,898) of the EDA signals were clean. A comparison of the frequency of skin conductance responses per minute with previous research confirmed that the physiological signals collected in the ambulatory setting were successful. The results also indicated that the statistical features of the EDA and HRV measures were significantly correlated with the self-reported outcomes, including the number of stressful events marked on the sensor device, positive and negative emotions, and experienced pain and discomfort.ConclusionsThe results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were consistent with previous literature in terms of the quality of the data and that features of these physiological signals were significantly associated with several self-reported outcomes among a sample of adults diagnosed with alcohol use disorder. These results suggest that ambulatory assessment of stress is feasible and can be used to develop tailored mobile health interventions to enhance sustained recovery from alcohol use disorder.

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the most common sources of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG), and chest electrodes, as in electrocardiography (ECG), however this complicates imaging setup, decreases patient comfort, and can induce further artifacts from movement. This work proposes an EOG- and ECG-free approach to identify eye-blinks, saccades, and cardiac activity signals for automated artifact suppression.The contribution of this work is three-fold. First, using a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA), a highly accurate artifact classifier is constructed as an amalgam of deep 1-D and 2-D Convolutional Neural Networks (CNNs) to automate the identification and removal of ubiquitous whole brain artifacts including eye-blink, saccade, and cardiac artifacts. The specific architecture of this network is optimized through an unbiased, computer-based hyperparameter random search. Second, visualization methods are applied to the learned abstraction to reveal what features the model uses and to bolster user confidence in the model's training and potential for generalization. Finally, the model is trained and tested on both resting-state and task MEG data from 217 subjects, and achieves a new state-of-the-art in artifact detection accuracy of 98.95% including 96.74% sensitivity and 99.34% specificity on the held out test-set. This work automates MEG processing for both clinical and research use, adapts to the acquired acquisition time, and can obviate the need for EOG or ECG electrodes for artifact detection.

Online detection and removal of eye blink artifacts from electroencephalogram

The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, sensitivity, specificity, and error rate for eye blink artifact removal with FastEMD-CCA2 is 97.9%, 97.65%, 99.22%, and 2.1%, respectively, validated on a Hitachi dataset with 60 EEG signals, consisting of more than 5600 eye blink artifacts. FastCCA achieved an average of 99.47%, 99.44%, 99.74%, and 0.53% artifact removal accuracy, sensitivity, specificity, and error rate, respectively, validated on the Hitachi dataset too. FastEMD-CCA2 and FastCCA algorithms are developed and implemented in the C++ programming language, mainly to investigate the processing speed that these algorithms could achieve in a different medium. Analysis has shown that FastEMD-CCA2 and FastCCA took about 10.7 and 12.7 ms, respectively, on average to clean a 1-s length of EEG segment. As a result, they’re a viable option for applications that require online removal of eye blink objects from EEG signals.

Probability mapping based artifact detection and removal from single-channel EEG signals for brain–computer interface applications

BackgroundDifferent types of artifacts in the electroencephalogram (EEG) signals can considerably reduce the performance of the later-stage EEG analysis algorithms for making decisions, such as those for brain–computer interfacing (BCI) classification. In this paper, we address the problem of artifact detection and removal from single-channel EEG signals. New methodWe propose a novel approach that maps the probability of an EEG epoch to be artifactual based on four different statistical measures: entropy (a measure of uncertainty), kurtosis (a measure of peakedness), skewness (a measure of asymmetry), and periodic waveform index (a measure of periodicity). Then, a stationary wavelet transform based artifact removal is proposed that employs a particular probability threshold provided by the user. ResultsWe have executed our experiments with both synthetic and real EEG data. It is observed that the proposed method exhibits a superior performance for suppressing the artifact contaminated from EEG with minimum distortion. Moreover, evaluation of the algorithm using EEG dataset for BCI experiments reveals that artifact removal can considerably improve the BCI output in both event-related potential and motor-imagery based BCI applications. Comparison with existing methodsThe proposed algorithm has been applied to both real and synthesized data testing and compared with other state-of-the-art automated artifact removal methods. Its superior performance is verified in terms of various performance metrics including computational complexity for justifying its use in BCI-like real-time applications. ConclusionOur work is expected to be useful for future research EEG signal processing and eventually to develop more accurate real-time EEG-based BCI applications.

Gout of feet and ankles in different disease durations: diagnostic value of single-source DECT and evaluation of urate deposition with a novel semi-quantitative DECT scoring system

ObjectivesTo investigate the diagnostic performance of single-source dual-energy computed tomography (DECT) based on gemstone spectral imaging technology (including Discovery CT750HD and Revolution CT) in patients with suspected feet/ankles gouty arthritis, and evaluate the urate deposition with a novel semi-quantitative DECT scoring system.MethodsA total of 196 patients were consecutively included. Feet and ankles were evaluated in all patients by single-source DECT scan. The 2015 EULAR/ACR criteria were used as the reference for the diagnosis of gout. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of DECT for the diagnosis of gout in the early (≤1 year), middle (1–3 years), and late (> 3 years) disease durations were calculated. Besides, a novel semi-quantitative DECT scoring system was assessed for the measurement of urate deposition, and the correlation between the scores and the clinical and serological data were also evaluated. Moreover, the influences of artifacts on the diagnostic performance of DECT were also determined.ResultsThe sensitivity, specificity, and AUC of DECT in 196 patients were 38.10, 96.43%, and 0.673 in the early-stage group; 62.96, 100.00%, and 0.815 in the middle-stage group; and 77.55, 87.50%, and 0.825 in the late-stage group, respectively. The overall diagnostic accuracies in the AUC of DECT (Discovery CT750HD and Revolution CT) in the middle and late stages of gout were higher than that in the early stage of gout. Besides, the monosodium urate crystals were deposited on the first metatarsophalangeal joints and ankles/midfeet. Age, the presence of tophus, bone erosion, and disease duration considerably affected the total urate score. No statistical difference in the positive detection of nail artifact, skin artifact, vascular calcification, and noise artifact was found between the case and control groups.ConclusionDECT (Discovery CT750HD and Revolution CT) showed promising diagnostic accuracy for the detection of urate crystal deposition in gout but had limited diagnostic sensitivity for short-stage gout. Longer disease duration, the presence of tophus, and bone erosion were associated with the urate crystal score system. The artifacts do not remarkably affect the diagnostic performance of DECT in gout.