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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Cardiac CT blooming artifacts:clinical significance,root causes and potential solutions

This review paper aims to summarize cardiac CT blooming artifacts,how they present clinically and what their root causes and potential solutions are.A literature survey was performed covering any publications with a specific interest in calcium blooming and stent blooming in cardiac CT.The claims from literature are compared and interpreted,aiming at narrowing down the root causes and most promising solutions for blooming artifacts.More than 30 journal publications were identified with specific relevance to blooming artifacts.The main reported causes of blooming artifacts are the partial volume effect,motion artifacts and beam hardening.The proposed solutions are classified as high-resolution CT hardware,high-resolution CT reconstruction,subtraction techniques and post-processing techniques,with a special emphasis on deep learning(DL)techniques.The partial volume effect is the leading cause of blooming artifacts.The partial volume effect can be minimized by increasing the CT spatial resolution through higherresolution CT hardware or advanced high-resolution CT reconstruction.In addition,DL techniques have shown great promise to correct for blooming artifacts.A combination of these techniques could avoid repeat scans for subtraction techniques.

JPEG-Compatibility Steganalysis that Distinguishes Embedding Changes from Recompression Artifacts

JPEG-Compatibility steganalysis detects the presence of secret message embedded in the JPEG decompressed images and estimates the embedding rate. We propose a JPEG-Compatibility steganalysis algorithm that estimates the embedding rate based on the difference between the stego image and its recompression based predicted co-vet image. In particular, compression artifacts and embedding changes are distinguished based on the amplitude of pixel value changes. This is done independent of the embedding positions, thus is effective for both content non-adaptive and content adaptive steganography. In addition, we also improve the recompression prediction scheme to more accurately estimate the JPEG quantization table. Experimental results show that the proposed algorithm is significantly more effective in detecting spatial ±1 steganography across a wide range of quality factors and embedding rates, when compared to the previous works.

Ring artifacts correction based on the projection-field in neutron CT

Ring artifacts will happen mostly when the detector has inconsistent response among the detector channels,and the characteristic produced rings centered in the iso-center in the reconstructed slices inevitably affect the recognition and analysis of the corresponding sample structures in neutron computed tomography(CT).In this work,a ring correction method based on the projection-field(RCP)is proposed,it is a pre-processing method and provides the corrected projection data directly,which is also conducive to efficient data storage and other algorithmic researches.Simulation and physical experiments are performed for verifying the effect of the method,and one of the correction methods based on the image-field is used for comparison.The results demonstrate that the RCP can correct the ring artifacts well without reducing the image resolution or over-correction.

Analysis of photostimulable phosphor image plate artifacts and their prevalence

BACKGROUND Digital radiography has recently been used in dentistry as a substitute for conventional film radiography worldwide.Digital imaging has many advantages and provides new possibilities for recording and interpreting radiographic data.This system uses different types of digital receptors.AIM To detect the frequency,type,and reasons behind the appearance of intraoral image artifacts acquired by photostimulable phosphor plates(PSP).METHODS This retrospective descriptive study was conducted in the oral and maxillofacial radiology unit of the dental clinics of the College of Dentistry,Princess Nourah University(PNU).All intraoral digital radiographs were acquired using(Gendex Expert DC.,United States)an intraoral X-ray machine with 7-mA,65-kVP using a PSP system(Soredex DIGORA Optime imaging plate)and laser scanners(Soredex DIGORA Optime),which can house all sizes of reusable intraoral PSP sensor plates with image acquisition software(MIPACS Dental Enterprise viewer 3.2.2).A total of 50000 intraoral radiographs were retrieved from the clinical database from April 2018 to April 2020 to evaluate the reason,type,and solutions to these image artifacts.RESULTS Overall,50000 intraoral digital radiographs were acquired in a two-year-period;that is,from April 2018 to April 2020.Of these,3550(7.1%)retakes were performed due to the presence of image artifacts.Periapical radiographs were the most common image type of intraoral retakes(80.8%).Imaging artifacts were divided into three categories:operator,plate and scanning errors.Out of 3550 retakes,5%,1.37%,and 0.73%were related to operator,plate,and scanning errors,respectively.The cone cut was the most common operator error(988 images),Bite marks were the most common plate error(276 images),and delayed scanning artifacts were the most common scanning errors(145 images).The calculated kappa value for interobserver reliability was 0.99,indicating almost perfect interobserver agreement.CONCLUSION Our study discussed intraoral image artifacts that were characteristic of PSP,where the most common artifacts were bitemarks,image size reduction,scratches,and delayed scanning.

A POCS-Based Algorithm for Blocking Artifacts Reduction

An algorithm for blocking artifacts reduction in DCT domain for block-based image coding was developed. The algorithm is based on the projection onto convex set （POCS） theory. Due to the fact that the DCT characteristics of shifted blocks are different caused by the blocking artifacts, a novel smoothness constraint set and the corresponding projection operator were proposed to reduce the blocking artifacts by discarding the undesired high frequency coefficients in the shifted DCT blocks. The experimental resuhs show that the propo,sed algorithm outperforms the conventional algorithms in terms of objective quality, subiective quality, and convergence property.

Score based assessment of implant-related post fusion MRI artifacts focused on different interbody disc spacers: An <i>in vitro</i>study

Interbody disc spacers for anterior spine fusion are made of different materials, such as titanium alloys or carbon fiber reinforced polymers (CFRP). Implant-related susceptibility artifacts can decrease the quality of MRI scans. This cadaveric study aimed to demonstrate the extent that implant-related MRI artifacting affects the post fusion differentiation of the spinal canal (SC) and intervertebral disc space (IDS). In 6 cadaveric porcine spines, we evaluated the post-im- plantation MRI scans of a titanium and CFRP spacer that differed in shape and surface qualities. A spacer made of human cortical bone was used as a control. A defined evaluation unit was divided into regions of interest (ROI) to characterize the SC and IDS. Considering 15 different MRI sequences read independently by an interobserver-validated team of specialists artifact-affected image quality of the median MRI slice was rated on a modified score of 0-1-2-3. A maximum score of 15 points for the SC and 9 points for the IDS (100%) was possible. Turbo spin echo sequences produced the best scores for both spacers and the control. Only the control achieved a score of 100%. For the IDS the titanium and CFRP spacer maximally scored 0% and 74%, for the SC 80% and 99%, respectively. By using favored T1 TSE sequences the CFRP-spacer represented clear advantages in post fusion spinal imaging. Independent of artifact dimensions the used scoring system allowed us to create an implant-related ranking of MRI scan quality in reference to the bone control.

New Blocking Artifacts Reduction Method Based on Wavelet Transform

It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The algorithm exploits blocking-artifact features shown in wavelet domain. The energy of blocking artifacts is concentrated into some lines to form annoying visual effects after wavelet transform. The aim of reducing blocking artifacts is to capture excessive energy on the block boundary effectively and reduce it below the visual scope. Adaptive operators for different subbands are computed based on the wavelet coefficients. The operators are made adaptive to different images and characteristics of blocking artifacts. Experimental results show that the proposed method can significantly improve the visual quality and also increase the peak signal-noise-ratio(PSNR) in the output image.