OBJECTIVE: To propose a framework that combines sinogram interpolation with unsupervised image-to-image translation (UNIT) network to correct metal artifacts in CT images. METHODS: The initially corrected CT image and the prior image without artifacts, which were considered as different elements in two different domains, were input into the image transformation network to obtain the corrected image. Verification experiments were carried out to assess the effectiveness of the proposed method using the simulation data, and PSNR and SSIM were calculated for quantitative evaluation of the performance of the method. RESULTS: The experiment using the simulation data showed that the proposed method achieved better results for improving image quality as compared with other methods, and the corrected images preserved more details and structures. Compared with ADN algorithm, the proposed algorithm improved the PSNR and SSIM by 2.4449 and 0.0023 when the metal was small, by 5.9942 and 8.8388 for images with large metals, and by 8.8388 and 0.0130 when both small and large metals were present, respectively. CONCLUSION The proposed method for metal artifact correction can effectively remove metal artifacts, improve image quality, and preserve more details and structures on CT images.
Artifacts ; Algorithms ; Computer Simulation ; Tomography, X-Ray Computed

Anti-motion artifact is one of the most important properties of ambulatory ECG monitoring equipment. At present, there is a lack of standardized means to test the performance of anti-motion artifact. ECG simulator and special conductive leather are used to build the simulator, it is used to simulate human skin, to generate ECG signal input for the ECG monitoring equipment attached to it. The mechanical arm and fixed support are used to build a motion simulation system to fix the conductive leather. The mechanical arm is programmed to simulate various motion states of the human body, so that the ECG monitoring equipment can produce corresponding motion artifacts. The collected ECG signals are read wirelessly, observed, analyzed and compared, and the anti-motion artifact performance of ECG monitoring equipment is evaluated. The test results show that by artificially creating the small difference between the two groups of ambulatory ECG monitoring equipment, the system can accurately test the interference signals introduced under the conditions of controlled movement such as tension and torsion, and compare the advantages and disadvantages. The research shows that the test system can provide convenient and accurate verification means for the research of optimizing anti-motion interference.
Humans ; Artifacts ; Signal Processing, Computer-Assisted ; Electrocardiography, Ambulatory/methods* ; Electrocardiography ; Motion

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.
Humans ; Electrooculography/methods* ; Artifacts ; Brain-Computer Interfaces ; Algorithms ; Electroencephalography/methods* ; Signal Processing, Computer-Assisted

OBJECTIVE: To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. METHODS: A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. RESULTS: For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). CONCLUSION The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.
Algorithms ; Artifacts ; Image Processing, Computer-Assisted/methods* ; Metals ; Phantoms, Imaging ; Tomography, X-Ray Computed/methods*

OBJECTIVE: To build a helical CT projection data restoration model at random low-dose levels. METHODS: We used a noise estimation module to achieve noise estimation and obtained a low-dose projection noise variance map, which was used to guide projection data recovery by the projection data restoration module. A filtering back-projection algorithm (FBP) was finally used to reconstruct the images. The 3D wavelet group residual dense network (3DWGRDN) was adopted to build the network architecture of the noise estimation and projection data restoration module using asymmetric loss and total variational regularization. For validation of the model, 1/10 and 1/15 of normal dose helical CT images were restored using the proposed model and 3 other restoration models (IRLNet, REDCNN and MWResNet), and the results were visually and quantitatively compared. RESULTS: Quantitative comparisons of the restored images showed that the proposed helical CT projection data restoration model increased the structural similarity index by 5.79% to 17.46% compared with the other restoration algorithms (P < 0.05). The image quality scores of the proposed method rated by clinical radiologists ranged from 7.19% to 17.38%, significantly higher than the other restoration algorithms (P < 0.05). CONCLUSION The proposed method can effectively suppress noises and reduce artifacts in the projection data at different low-dose levels while preserving the integrity of the edges and fine details of the reconstructed CT images.
Algorithms ; Artifacts ; Tomography, Spiral Computed ; Tomography, X-Ray Computed/methods*

The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
Algorithms ; Artifacts ; Computer Simulation ; Electroencephalography ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Photoacoustic imaging (PAI) is a rapidly developing hybrid biomedical imaging technology, which is capable of providing structural and functional information of biological tissues. Due to inevitable motion of the imaging object, such as respiration, heartbeat or eye rotation, motion artifacts are observed in the reconstructed images, which reduce the imaging resolution and increase the difficulty of obtaining high-quality images. This paper summarizes current methods for correcting and compensating motion artifacts in photoacoustic microscopy (PAM) and photoacoustic tomography (PAT), discusses their advantages and limits and forecasts possible future work.
Artifacts ; Microscopy ; Motion ; Photoacoustic Techniques ; Tomography, X-Ray Computed

OBJECTIVE: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE).MATERIALS AND METHODS: One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images.RESULTS: LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images.CONCLUSION: DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.
Artifacts ; Humans ; Noise ; Tomography, X-Ray Computed

OBJECTIVE: To explore the application value of MAR algorithm in metal artifact removal of CT simulator. METHODS: CT phantom with titanium plate was scanned using conventional algorithms and MAR algorithms, respectively. Artifact index(AI), contrast-to-noise ratio(CNR) and AI values at different slices were used to analyze the artifact images. RESULTS: In artifact index, MAR algorithm (10.28±2.60) is significantly lower than conventional algorithm (20.65±5.04); In contrast-to-noise ratio index, MAR algorithm (7.81±1.12) is better than conventional algorithm (5.61±1.36). The above indicators were statistically significant in both algorithms (P<0.01). In the slices affected by metal artifacts, the artifact index decreased by 21.72%~88.40% after the MAR algorithm. CONCLUSIONS MAR algorithm can significantly reduce the metal artifacts and improve the clinical value of CT data.
Algorithms ; Artifacts ; Metals ; Phantoms, Imaging ; Titanium ; Tomography, X-Ray Computed

artifacts. When performed in the post-filtration process, PPV was increased by approximately 20% in FFPE. Buffy coat showed 100% of sensitivity, specificity and accuracy in BRCA1/2 NGS test.CONCLUSIONS: On the comparison of the analytical performance according to different sample types, the buffy coat was not affected by sequencing artifacts and VAF shifted variants. Therefore, the blood test should be given priority in detecting germline BRCA1/2 mutation, and tumor materials could be suitable to detect somatic mutations in OC patients without identifying germline BRCA1/2 mutation.]]>
Artifacts ; Blood Buffy Coat ; Gene Frequency ; Hematologic Tests ; High-Throughput Nucleotide Sequencing ; Humans ; Mass Screening ; Ovarian Neoplasms ; Sensitivity and Specificity ; Tissue Preservation