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.

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.

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.

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.

基于Artifact快照序列的行为一致性检测方法

Artifact行为的一致性检测,是在流程建模、运行之后亟待解决的关键问题之一.针对现有一致性检测技术忽略数据操作方面检测的问题,提出了一种基于Artifact快照序列的行为一致性检测方法.首先,利用全序Artifact快照序列定义了Artifact的行为模式,该行为模式不仅体现了服务的运行轨迹,也描述出了Artifact数据属性赋值的状态变化;然后,将Artifact行为一致性检测问题转换为语言可判定问题,证明了该问题是一个可判定问题,该过程中,设计一台判定该语言的图灵机作为一致性验证模型,该模型不仅检测了Artifact生命周期中服务路径的一致性,同时也检测了生命周期中Artifact属性赋值的正确性;进一步地,利用服务-快照关联矩阵的等价转换,给出了行为一致性量化指标中确切度的精确计算方法;最后,通过实例分析及实验对所提出的方法进行了验证.

Artifact的有效性问题研究

为及时发现业务活动对数据的非法操作,将违规实例监测问题归结为Artifact有效性检查问题,基于时间Petri网提出Artifact业务要求模型,从属性赋值顺序的角度给出Artifact在生命周期中的状态变化及时间要求。证明了在给出业务要求模型并求出状态类图的条件下,该问题是多项式时间问题。研究结果表明,该方法支持业务流程中Artifact的时间建模,为业务流程中验证Artifact的有效性提供了新的途径。

基于Artifact状态变迁的射孔数据协同流程模型研究

本课题结合射孔作业的实际需要,提出基于Artifact状态变迁的射孔数据协同流程思想。首先介绍Artifact的定义与特性,结合射孔数据处理时存在的问题,设计了基于Artifact状态变迁的射孔深度数据协同处理流程并建立了模型。尤其考虑到射孔现有的应用软件元素,对Artifact推进进程的应用做了改进,建立完全支持射孔作业的工作模式。最后用状态树描述了Artifact实例状态的生命周期及状态变化情况,实现不同部门、不同岗位的协同工作。

ArtiFlow Designer:一种面向Artifact的业务流程设计工具

ArtiFlow是一种以数据为中心的业务流程描述模型,如何管理ArtiFlow是面向Artifact业务流程管理中的一个关键问题.ArtiFlow的设计方法和设计工具是ArtiFlow管理的基础,首先在介绍Artifact和Arti-fact系统的基础上给出了以业务流程中关键Artifact及其生命周期为出发点的ArtiFlow设计方法;其次描述了ArtiFlow模型中各元素的含义,并给出了ArtiFlow模型的形式化定义;最后设计开发出了ArtiFlow的设计工具——ArtiFlow Designer.

AOBPMR:一个面向Artifact业务流程模型管理库

与传统的以控制为中心的业务流程管理相似,为了更好的对面向Artifact业务流程模型进行流程检索、流程挖掘、流程整合以及流程模型分析等操作,有效的管理和使用流程模型是一个具有挑战性的任务。其中,流程的存储模式以及查询语言规范化是要解决的关键问题之一,鉴于Artifact的生命周期特性,本文构建了一个混合存储模式的业务流程模型管理库,详细的阐述了该库的系统架构和功能模块,该库不仅提供基本的流程模型存储和检索等功能,还具有流程挖掘,流程分析等高级功能。

ArtiFlow中artifact生命周期的可满足性问题

以数据为中心是业务过程管理技术发展的一个新趋势.artifact是记录业务过程的数据实体,围绕artifact的业务过程管理正在成为业务过程管理研究的一个热点.生命周期是artifact的一个重要特征,业务过程能否满足对artifact生命周期的定义是业务过程设计中需要验证的一个重要特性.本文从artifact属性赋值顺序的角度,基于Petri网定义artifact的生命周期树.采用ArtiFlow建立业务过程模型,在ArtiFlow模型中根据业务规则提取artifact状态变化树,与artifact生命周期树进行路径比较,验证业务过程模型中artifact生命周期的可满足性.给出相应的算法并分析了算法的复杂度.

面向Artifact的业务流程行为相似性度量方法

面向Artifact的业务流程是以数据为中心的业务流程的代表.与传统以过程为中心的业务流程相似,为了更好的对流程模型进行流程检索、流程挖掘等操作,计算流程间的相似性或距离是一个关键的问题.给出一种面向Artifact的业务流程行为相似性度量方法.首先,通过测量流程模型之间关键Artifact的相似性来评估流程处理的核心业务数据的相似度.其次,根据关键Artifact生命周期特性,测量任务执行路径中任务依赖关系的相似性.最后,测量生命周期中关键Artifact属性赋值序列的相似性.理论和实例分析表明,该方法是一个有效的相似性度量方法.

以Artifact为中心的业务流程扩展二部图模型及其相似性度量方法

为了保证计算结果的准确性和有效性,提出一种以Artifact为中心的业务流程扩展二部图模型及其相似性度量方法。首先提出一个用于描述以Artifact为中心的业务流程的扩展二部图模型,将流程模型匹配转换为图匹配问题;其次提出一种利用矩阵转换操作来度量图编辑距离的方法;在扩展二部图模型的基础上结合属性划分技术,分别提出服务节点和仓库节点行为相似度计算方法;设计实现了以Artifact为中心的业务流程扩展二部图的图匹配算法。理论分析和实验结果表明,该算法在查询时间和执行效率上优于现有算法,具有较高的查准率和检索质量。

一种以Artifact为中心的多业务流程协同监控方法

多业务流程协同监控是通过监控合作伙伴的行为,保证可以灵活、动态地选择最优合作伙伴,确保企业利益最大化的一种有效方法。已有的方法在监控过程中忽略了业务流程数据的重要性,一定程度上降低了监控信息质量和可利用性。因此,本文提出一种以Artifact为中心的多业务流程协同监控方法。首先,给出了以Artifact为中心的业务流程协同模型及Artifact实例协同快照定义。其次,采用快照日志挖掘获得候选以Artifact为中心的业务流程协同模型,然后,根据蚁群优化算法在候选流程模型中获取最优流程服务协同路径。最后,通过实例分析验证了方法的可行性。

面向Artifact的业务过程模型

提出一种面向Artifact的业务过程形式化模型。利用谓词/变迁Petri网建模的优势,根据Artifact的特点,用半结构化数据描述Artifact,以表示Artifact结构的文档类型定义作为静态谓词,通过一种操作XML的一阶逻辑语言描述变迁。实验表明,该模型能有效分析Artifact的可达性、持久性和唯一性等问题。

Ar/T-Net:一种面向Artifact的业务过程概念模型

以数据为中心是业务过程管理发展的新趋势,面向Artifact的业务过程管理是一种具有代表性的以数据为中心的业务过程管理技术。概念模型设计是面向Artifact的业务过程设计的关键步骤,但目前还没有成型的建模和分析工具。利用谓词/变迁Petri-Net建模的优势,根据Artifact的特点,用嵌套关系模式定义Artifact的结构;以描述Artifact的嵌套关系模式作为谓词,提出了一种适合于概念模型设计的谓词/变迁Petri-Net,即Ar/T-Net。以可达性分析为例,讨论了Ar/T-Net的功能。

Sinogram fusion-based metal artifact correction method

To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain proportion.The final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT image.Experiments on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.

ArtiMate:一种以Artifact为中心的多粒度协作流程建模方法

业务流程是组合服务的主要表现形式之一.跨组织多方协作流程往往包含多重粒度,难以基于任何单一粒度建模.Proclets方法将多粒度单体流程分解为一组交互协作的单粒度流程,以实例的基数、多重性来约束其间的交互关系.然而,在此类方法中如何体现流程业务目标与业务进展,支持复杂且多变的协作关系,准确刻画模型的执行语义等问题尚缺乏有效的建模技术.提出一种以Artifact为中心的多粒度协作流程建模方法 Arti Mate:继承proclets的粒度分解思路但以Artifact为中心建模各单粒度流程artilet,利于刻画业务目标,追踪业务进展;解耦交互机制和交互策略,以丰富、插件式交互策略polilet连接较为稳定的artilet,复用实质性业务流程并适应协作需求变化;以着色Petri网描述的Arti Mate的执行语义,有利于建模方法的实现和仿真确认.论文以国家海洋局东海分局会签类应用为案例,检验了Arti Mate方法的可行性和有效性.