Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

Common Artifacts and Remedies in Histological Preparations

The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which are used in scientific research or for the purpose of diagnosing various diseases of the body. Despite this, some cellular structures within the tissue may suffer from some alterations that result from the appearance of defects during any stage of preparing these microscopic sections, which alter or interfere with the precise cellular structures and morphology that constitute the tissue and thus give a different image for tissue features and cause confusion in the work histopathologist in the diagnosis. There are several reasons that can cause a misdiagnosis of the sample that occurs during the surgical separation process or after separation during the stages of microscopic preparation techniques from fixation stage, tissue processing, embedding or microtomy, staining until mounting procedures. The constant need to identify these defects and their causes in addition to try to reduce them is one of the biggest challenges evident in pathology laboratories. Therefore, this study aims to review the most common defects that occur in any stage of tissue processing, with an explanation of their causes and appropriate ways to avoid them.

AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.

Fetal MRI Artifacts: Semi-Supervised Generative Adversarial Neural Network for Motion Artifacts Reducing in Fetal Magnetic Resonance Images

This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.

Artifacts in two-dimensional shear wave elastography of liver

BACKGROUND Artifacts are common when using two-dimensional shear wave elastography(2-D SWE)to measure liver stiffness(LS),but they are poorly recognized.AIM To investigate the presence and influence of artifacts in 2-D SWE of liver.METHODS We included 158 patients with chronic liver disease,who underwent 2-D SWE examination by a novice and an expert.A cross line at the center of the elastogram was drawn and was divided it into four locations:top-left,top-right,bottom-left,and bottom-right.The occurrence frequency of artifacts in different locations was compared.The influence of artifacts on the LS measurements was evaluated by comparing the elastogram with the most artifacts(EMA)and the elastogram with the least artifacts(ELA).RESULTS The percentage of elastograms with artifacts in the novice(51.7%)was significantly higher than that of the expert(19.6%)(P<0.001).It was found that both operators had the highest frequency of artifacts at bottom-left,followed by top-left and bottom-right,and top-right had the lowest frequency.The LS values(LSVs)and standard deviation values of EMAs were significantly higher than those of ELAs for both operators.An intraclass correlation coefficient value of 0.96 was found in the LSVs of EMAs of the two operators,and it increased to 0.98 when the LSVs of the ELAs were used.Both operators had lower stability index values for EMAs than ELAs,but the difference was only statistically significant for the novice.CONCLUSION Artifacts are common when using 2-D SWE to measure LS,especially for the novice.Artifacts may lead to the overestimation of LS and reduce the repeatability and reliability of LS measurements.

Investigations of moiréartifacts induced by flux fluctuations in x-ray dark-field imaging

X-ray dark-field imaging using a grating interferometer has shown potential benefits for a variety of applications in recent years.X-ray dark-field image is commonly retrieved by using discrete Fourier transform from the acquired phasestepping data.The retrieval process assumes a constant phase step size and a constant flux for each stepped grating position.However,stepping errors and flux fluctuations inevitably occur due to external vibrations and/or thermal drift during data acquisition.Previous studies have shown that those influences introduce errors in the acquired phase-stepping data,which cause obvious moiréartifacts in the retrieved refraction image.This work investigates moiréartifacts in x-ray dark-field imaging as a result of flux fluctuations.For the retrieved mean intensity,amplitude,visibility and dark-field images,the dependence of moiréartifacts on flux fluctuation factors is theoretically derived respectively by using a first-order Taylor series expansion.Results of synchrotron radiation experiments verify the validity of the derived analytical formulas.The spatial frequency characteristics of moiréartifacts are analyzed and compared to those induced by phase-stepping errors.It illustrates that moiréartifacts can be estimated by a weighted mean of flux fluctuation factors,with the weighting factors dependent on the moiréphase and different greatly for each retrieved image.Furthermore,moiréartifacts can even be affected by object’s features not displayed in the particular contrast.These results can be used to interpret images correctly,identify sources of moiréartifacts,and develop dedicated algorithms to remove moiréartifacts in the retrieved multi-contrast images.

Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins

This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for visualization.To implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic algorithm.Transient thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical insulation.An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP.Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT.Moreover,manufacturability was verified based on a thermal-solid coupled finite element analysis.The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.

Analysis of an Axial-flux Slotted Limited-angle Torque Motor with Quasi-Halbach Array for Torque Performance Improvement

Better torque performance and higher reliability have long been the focus of research for slotted limited-angle torque motors(LATMs),which are primarily used to position first-stage valves in electrohydraulic servosystems.This paper presents a high reliability axial-flux slotted LATM with quasi-Halbach array for torque performance improvement including constant torque range(CTR)and output torque.Firstly,the structure with two sets of windings and the operation principle of the proposed slotted LATM is analyzed.Secondly,a brief design procedure is presented,the structure selections of open slot and double-stator single-rotor(DSSR)interior rotor with surface mounted quasi-Halbach permanent magnet(PM)array are illustrated,and the geometric parameters are optimized to obtain the optimal design of the proposed slotted LATM.Then,3-D finite-element method(FEM)is employed to compare the proposed slotted LATM with the conventional surface mounted PM slotted LATM in terms of cogging torque,no-load back EMF,and output torque,and the results show that the proposed LATM with quasi-Halbach array has a 10%improvement in output torque and a 25%improvement in CTR.Meanwhile,the flux linkages and torque performance of the two sets of windings under various conditions verify good magnetic isolation.Finally,prototypes of two different rotor types are manufactured and a series of experiments are performed to validate the analysis.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography(OCT)imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues.However,it still faces the following challenges:including data processing speed,image quality,and improvements in three-dimensional(3D)visualization effects.OCT technology,especially functional imaging techniques like optical coherence tomography angiography(OCTA),requires a long acquisition time and a large data size.Despite the substantial increase in the acquisition speed of swept source optical coherence tomography(SS-OCT),it still poses significant challenges for data processing.Additionally,during in situ acquisition,image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis.Firstly,a customized frequency domainfilter with anti-banding suppression parameters was designed to suppress artifact noises.Then,this study proposed a graphics processing unit(GPU)-based real-time data processing pipeline for SS-OCT,achieving a measured line-process rate of 800 kHz for 3D fast and high-quality data visualization.Furthermore,a GPU-based realtime data processing for CC-OCTA was integrated to acquire dynamic information.Moreover,a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials,with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network.OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation.Therefore,GPU-based OCT enables real-time processing and visualization with artifact suppression,making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Method for predicting motion artifacts of plasma display panels

A simulation method is proposed to predict the motion artifacts of plasma display panels （PDPs）. The method simulates the behavior of the human vision system when perceiving moving objects. The simulation is based on the measured temporal light properties of the display for each gray level and each phosphor. Both the effect of subfield arrangement and phosphor decay are involved. A novel algorithm is proposed to improve the calculation speed. The simulation model manages to predict the appearance of the motion image perceived by a human with a still image. The results are validated by a set of perceptual evaluation experiments. This rapid and accurate prediction of motion artifacts enables objective characterization of the PDP performance in this aspect.

An adaptive fuzzy filter for coding artifacts removal in video and image

This paper proposes a new adaptive post-filtering algorithm to remove coding artifacts in block-based video coder. The proposed method concentrates on blocking and ringing artifacts removal. For de-blocking, the blocking strength is identified to determine the filtering range, and the maximum quantization parameter of the image is used to adapt the 1D fuzzy filter. For de-ringing, besides the edge detection, a complementary ringing detection method is proposed to locate the neglected ringing blocks, and the gradient threshold is adopted to adjust the parameter of 2D fuzzy filter. Experiments are performed on the MPEG-4 sequences. Compared with other methods, the proposed one achieves better detail preservation and artifacts removal performance with lower computational cost.

Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning

BACKGROUND Endoscopy artifacts are widespread in real capsule endoscopy(CE)images but not in high-quality standard datasets.AIM To improve the segmentation performance of polyps from CE images with artifacts based on ensemble learning.METHODS We collected 277 polyp images with CE artifacts from 5760 h of videos from 480 patients at Guangzhou First People’s Hospital from January 2016 to December 2019.Two public high-quality standard external datasets were retrieved and used for the comparison experiments.For each dataset,we randomly segmented the data into training,validation,and testing sets for model training,selection,and testing.We compared the performance of the base models and the ensemble model in segmenting polyps from images with artifacts.RESULTS The performance of the semantic segmentation model was affected by artifacts in the sample images,which also affected the results of polyp detection by CE using a single model.The evaluation based on real datasets with artifacts and standard datasets showed that the ensemble model of all state-of-the-art models performed better than the best corresponding base learner on the real dataset with artifacts.Compared with the corresponding optimal base learners,the intersection over union(IoU)and dice of the ensemble learning model increased to different degrees,ranging from 0.08%to 7.01%and 0.61%to 4.93%,respectively.Moreover,in the standard datasets without artifacts,most of the ensemble models were slightly better than the base learner,as demonstrated by the IoU and dice increases ranging from-0.28%to 1.20%and-0.61%to 0.76%,respectively.CONCLUSION Ensemble learning can improve the segmentation accuracy of polyps from CE images with artifacts.Our results demonstrated an improvement in the detection rate of polyps with interference from artifacts.

Reduction of artifacts in dental cone beam CT images to improve the three dimensional image reconstruction

Cone-beam CT (CBCT) scanners are based on volumetric tomography, using a 2D extended digital array providing an area detector [1,2]. Compared to traditional CT, CBCT has many advantages, such as less X-ray beam limitation, and rapid scan time, etc. However, in CBCT images the x-ray beam has lower mean kilovolt (peak) energy, so the metal artifact is more pronounced on. The position of the shadowed region in other views can be tracked by projecting the 3D coordinates of the object. Automatic image segmentation was used to replace the pixels inside the metal object with the boundary pixels. The modified projection data, using synthetically Radon Transformation, were then used to reconstruct a new back projected CBCT image. In this paper, we present a method, based on the morphological, area and pixel operators, which we applied on the Radon transformed image, to reduce the metal artifacts in CBCT, then we built the Radon back project images using the radon invers transformation. The artifacts effects on the 3d-reconstruction is that, the soft tissues appears as bones or teeth. For the preprocessing of the CBCT images, two methods are used to recognize the noisy black areas that the first depends on thresholding and closing algorithm, and the second depends on tracing boundaries after using thresholding algorithm too. The intensity of these areas is the lowest in the image than other tissues, so we profit this property to detect the edges of these areas. These two methods are applied on phantom and patient image data. It deals with reconstructed CBCT dicom images and can effectively reduce such metal artifacts. Due to the data of the constructed images are corrupted by these metal artifacts, qualitative and quantitative analysis of CBCT images is very essential.

Investigation of artifacts by mapping SAR in thermoacoustic imaging

Microwave-induced thermoacoustic imaging(MI-TAI)remains one of the focus of attention among biomedical imaging modalities over the last decade.However,the transmission and dis-tribution of microwave inside bio-tissues are complicated,thus result in severe artifacts.In this study,to reveal the underlying mechanisms of artifacts,we deeply investigate the distribution of specific absorption rate(SAR)inside tissue-mimicking phantoms with varied morphological features using both mathematical simulations and corresponding experiments.Our simulated results,which are confirmed by the associated experimental results,show that the SAR distri-bution highly depends on the geometries of the imaging targets and the polarizing features of the microwave.In addition,we propose the potential mechanisms including Mie-scattering,Fabry-Perot-feature,small curvature effect to interpret the diffraction effect in different scenarios,which may provide basic guidance to predict and distinguish the artifacts for TAI in both fundamental and clinical studies.

Determining the Authenticity of Artifacts by Oxygen Isotope Analysis

A technique used to determine the authenticity of artifacts that compares the oxygen isotopic composition of speleothems to the carbonate included within the patina of unprovenanced artifacts is of questionable value. The unprovenanced Jehoash Inscription Tablet and James Ossuary are of potentially immense historical and cultural importance. Nevertheless, they both were rejected by workers based on the oxygen isotope technique which provided the major foundational evidence of forgery in the longest running archaeological trial in Israel. Nevertheless, both these artifacts were determined not to be forged. The initial incongruence between the oxygen isotopes of the speleothems of the Soreq cave (Israel) purported to represent the unique composition of Jerusalem rainfall, and the patina on the artifacts, can be readily explained by the accretion of materials and geo-biochemical processes expected in normal patina formation in the Jerusalem region. The patina formation involves sporadic events in disequilibrium kinetic processes that are opposed to the equilibrium formation of speleothems in a sealed cave. Moreover, 23 of 56 patina samples (41%) on well-documented ancient artifacts from Israel yielded oxygen isotope values greater or lower than the expected speleothem values of -4 δ18O ‰ [PDB] to -6 δ18O ‰ [PDB]. Thus, the speleothem-patina correlation is invalid and the applied oxygen isotopes technique for determining the authenticity of patinas on artifacts is not a useful tool in the authentication of artifacts.

A review of methods for mitigating ionospheric artifacts in differential SAR interferometry

Interferometric synthetic aperture radar(InSAR)has been widely used to measure ground displacements related to geophysical and anthropic activities over the past three decades.Satellite SAR systems use microwave signals that interact with the ionosphere when they travel through it during the imaging processes.In this context,ionospheric variations can significantly contaminate SAR imagery,which in turn affects spaceborne InSAR measurements.This bias also leads to a decrease in the coherence and accuracy of InSAR measurements,especially for the low-frequency SAR systems.In this paper,we give an overview of the latest methods for mitigating the ionospheric contributions in InSAR,including Faraday rotation method,azimuth shift method,and range split-spectrum method,and only focus on the single pair of InSAR interferograms.The current challenges and future perspectives are outlined at the end of this paper.

Ring artifacts removal in X-ray-induced acoustic computed tomography

X-ray-induced acoustic computed tomography(XACT)is a hybrid imaging modality for detecting X-ray absorption distribution via ultrasound emission.It facilitates imaging from a single projection X-ray illumination,thus reducing the radiation exposure and improving imaging speed.Nonuniform detector response caused by the interference between multichannel data acquisition for ring array transducers and amplifier systems yields ring artifacts in the reconstructed XACT images,which compromises the image quality.We propose model-based algorithms for ring artifacts corrected XACT imaging and demonstrate their effcacy on numerical and experimental measurements.The corrected reconstructions indicate significantly reduced ring artifacts as compared to their conventional counterparts.

Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications.In general,image compression can introduce undesired coding artifacts,such as blocking artifacts and ringing effects.In this paper,we proposed a Multi-Scale Feature Attention Network(MSFAN)with two essential parts,which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images.Multiscale feature extraction layers have four Feature Extraction(FE)blocks.Each FE block consists of five convolution layers and one CA block for weighted skip connection.In order to optimize the proposed network architectures,a variety of verification tests were conducted using validation dataset.We used Computer Vision Center-Clinic Database(CVC-ClinicDB)consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration.The proposedMSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNNand DCSC,respectively.

Sing Your City: Local Stories of Global Artifacts

The article discusses five local stories that, without being connected to each other, tell about the socio-cultural circumstances accompanying creation of works of art that have received significant (but not the highest) recognition on a global scale. The discussion concerns three songs about cities: Torna a Surriento, Moscow Nights, and Li Beirut;a famous Soviet painting Low Marks Again;and the design features of Audi cars for the US market over the past decade. The approach to each certain artifact, be it song, painting, or the design of rear turn signals, is developed on the basis of disciplinary affiliation, whereas a discipline is more sociology, anthropology, or history of an appropriate art, than its theory. Another research method places a local context into the test tube of globalization. Experience shows that these two methods are applicable to different arts, places, and periods enriching the scientist with more detailed information about the era studied.