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.

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.

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.

基于遗传算法优化Canny算子的织绣文物纹样抽取方法研究

织绣文物中的纹样彰显中国传统文化造型艺术的形式美感,是文创设计的重要素材源泉。针对传统Canny算子进行纹样边缘检测需人为确定阈值的问题,本文提出基于遗传算法自适应优化Canny算子阈值的方法,将边缘点、非边缘点类间梯度幅值方差设置为适应度函数,通过格雷码对阈值染色体编解码、锦标赛选择算法优选交叉变异后的阈值,求解最优适应度对应的高低阈值,用于纹样边缘检测;此外,运用形态学操作得到纹样的完整、精确轮廓;最后,添加Alpha通道抽取纹样前景。实验表明,上述方法在织绣纹样抽取精度和像素准确度(PA)上,优于传统Canny算子、混合蛙跳自适应Canny算子、Otsu法优化Canny算子等已有方法,可有效检测织绣文物图像中纹样真实边缘并完成纹样抽取。

基于振动台试验的鱼线固定梅瓶文物响应规律性研究

为了探索鱼线固定梅瓶文物在实际博物馆的地震响应以及抗震效果,首先选取了典型梅瓶文物,并在三层钢筋混凝土框架结构中开展了24种地震工况的振动台试验,然后建立了上述试验所用梅瓶文物的有限元模型,验证了有限元模型的准确性,最后采用增量动力分析法分析了该文物在两种常见直径鱼线保护措施下的运动响应。结果表明:鱼线固定梅瓶文物在地震作用下会产生滑移、摇摆、倾覆以及鱼线断裂等现象;不同楼层下的文物响应差别较大,尤其在大震作用下,高楼层的鱼线固定梅瓶文物易发生倾覆和鱼线断裂破坏,要重视高楼层文物的震前保护措施;在较强的地震作用下,仅依靠增大鱼线直径有时对控制文物的倾覆情况起不到关键性决定作用,需要进一步采取其它措施对文物进行保护。

运动伪影校正技术改善肾动脉瘤锥形束CT图像质量

目的观察运动伪影校正技术(MFT)对于改善肾动脉瘤锥形束CT(CBCT)图像质量的价值。方法前瞻性选取20例拟接受经导管动脉栓塞治疗的肾动脉瘤患者,于CBCT引导下行肾动脉造影,采用MFT处理运动伪影;对MFT校正前、后图像质量进行主、客观评价,评估其显示肾动脉、载瘤动脉及靶区血管树情况,测算单位密度像素平均值(mean)、标准差(SD)及血管边缘锐化误差率。结果MFT校正后,肾动脉瘤最大密度投影图像显示肾动脉、重建CBCT图像显示载瘤动脉均明显优于校正前(P均<0.05);利用Flightplan for Liver软件可自动提取清晰的血管树;肾动脉瘤CBCT图像SD及血管边缘锐化误差率明显减小而mean明显增加(P均<0.05)。结论MFT可有效改善肾动脉瘤CBCT图像质量。

深度学习重建联合Smart去金属伪影算法在口腔金属植入物患者头颈CT血管成像中的应用

目的:探讨深度学习重建(DLR)联合Smart去金属伪影(MAR)算法在口腔金属植入物患者头颈CT血管成像(CTA)中的应用价值。方法:选择郑州大学第一附属医院2023年2月至6月口腔有不可拆卸金属植入物行头颈CTA的患者70例,采用以下3种方法重建图像:基于混合模型的自适应迭代重建(ASIR-V)50%算法(IR),ASIR-V50%联合Smart MAR算法(IR-S),高水平DLR联合Smart MAR算法(DLR-S)。测量不受伪影影响的颈内动脉C1段和头夹肌感兴趣区CT值的标准差(SD)2和SD4,作为图像噪声指标;计算颈内动脉C1段和舌部的金属伪影指数(AI)1和AI2;对颈内动脉C1段和口腔整体图像质量进行主观评分。结果:与IR组和IR-S组比较,DLR-S组SD2和SD4降低(P<0.05)。与IR组比较,IR-S组和DLR-S组AI1、AI2降低;与IR-S组比较,DLR-S组AI1、AT2降低(P<0.05)。与IR组比较,IR-S组和DLR-S组口腔整体和颈内动脉C1段图像质量主观评分均增高;与IR-S组比较,DLR-S组图像质量主观评分增高(P<0.05),9例患者舌部可见新的伪影。结论:Smart MAR联合DLR可减少口腔植入物造成的金属伪影,提高头颈CTA图像质量。但Smart MAR可能引入新的伪影,需联合未加入Smart MAR的图像进行分析。

基于可拓语义的传统纹样在食品包装设计中的应用

为了满足消费者将传统文化意蕴与食品包装相结合的需求,提出一种可拓语义下的传统纹样特征提取设计研究方法。通过对唐代金银器纹样加以分析筛选,确立目标纹样基元,构建可拓语义模型;对纹样基元进行语义词汇可拓分析,得到最大设计价值区间。利用图解思维对特征语义进行可视化分析,加以形状文法进行推导演变。以某款月饼包装设计为例,采用该方法设计出数款应用方案,并对其进行模糊综合评价,获得了较高满意度。

基于探测器响应机理的碳/碳构件CT图像环状伪影的校正方法

在碳/碳复合材料的计算机断层扫描(CT)中,由于探测器单元的响应非线性及不一致性导致重建图像出现严重的环状伪影,干扰图像中缺陷的检出,影响检测系统对被检构件的质量评价。结合碳/碳复合材料组分单一且密度均匀的特点,提出了一种基于探测器响应机理的CT图像环状伪影的校正方法,利用采集的劣化投影数据做预重建,对重建结果做阈值分割以获得被检测物体的三维模型。结合已知的材料和密度信息,对被检物体进行重投影,得到投影数据的理论值与实测值之间的映射关系,用于探测器响应校正以优化检测图像。与传统低通滤波的环状伪影校正方法相比,该方法考虑了环状伪影的物理成因并充分利用了检测对象的先验信息。研究结果表明,该校正方法在保留图像细节和纹理的同时,能够有效减少环状伪影,提升图像质量,消除伪影对于图形中缺陷识别的干扰,为提升检测系统对碳/碳复合材料构件缺陷的检出能力提供理论依据。

儿童常见周围性眩晕视频头脉冲结果分析

目的分析儿童常见周围性眩晕的视频头脉冲试验(video head impulse test,vHIT)结果并探讨vHIT的诊断价值。方法回顾性选取2020年1月至2022年3月就诊于苏州大学附属第二医院眩晕诊治中心的儿童常见周围性眩晕患儿78例,根据年龄将患儿分为≤10岁组26例和>10岁组52例,分析其vHIT检查结果。结果78例周围性眩晕患儿中儿童良性阵发性眩晕(benign paroxysmal vertigo of childhood,BPVC)28例,前庭性偏头痛(vestibular mi⁃graine,VM)20例,前庭神经炎(vestibular neuritis,VN)15例,突发性聋伴眩晕(sudden deafness with vertigo,SD)8例,梅尼埃病(Meniere's disease,MD)7例。vHIT检查结果异常47例,其中BPVC14例,VM12例,VN14例,SD3例,MD4例。儿童vHIT检查中出现伪迹的概率更高,最常见的伪迹类型为眨眼,高增益值及追踪震荡,≤10岁组vHIT检查出现伪迹多于>10岁组,差异有统计学意义(P<0.001)。结论儿童周围性眩晕最常见的病因主要为BPVC及VM,vHIT在儿童眩晕的诊断中具有很高的特异性,但出现伪迹的概率较高,检查过程中需注意甄别并防范。

影响乳腺X线导管造影图像质量的因素分析及处理对策

目的对影响乳腺X线导管造影图像质量的因素进行调查收集,总结常发生的问题并提出处理对策,为临床提供能满足诊治要求的导管造影图像。方法回顾性收集2016年1月—2019年12月我院行乳腺X线导管造影检查的资料,检查成功的共有2850例,其中一次性成功者2609例,进行二次检查的导管造影的病例241例,对比两次造影检查的过程与图像质量。结果影响导管造影图像质量或造成造影术失败的常见原因有8大类,导管内容物干扰导管的显示最多,有70例;导管内空气造成的伪影有43例;注入对比剂过多有36例;注入对比剂过少有29例;拍片时压力过大有25例;呼吸运动伪影有20例;对比剂误入其他导管有14例;其他原因有4例。针对不同的原因与高年资技师、诊断医师分析并找出应对方法,获得满足临床诊治的图像。结论乳腺X线导管造影的图像质量好坏,影响临床对导管内有无病变的诊断,耽误患者病情的治疗,技师应针对不同的情况采取相适宜的应对方法,可以有效解决造影失败问题,提升乳腺导管造影成功率,提高导管造影图像质量,满足临床诊治所需。

维特根斯坦哲学视角下的商周青铜器纹饰研究

文章基于维特根斯坦的哲学符号学思想,以商周时期的青铜器纹饰为研究对象,通过分析其符号学系统,深入探讨青铜器纹饰的文化内涵和逻辑演变。从符号学视角,结合青铜器实际案例,分析纹饰的形式结构和符号系统。通过对商周时期青铜器的历史背景进行深入阐述,揭示青铜器在当时社会中的重要性。通过对青铜器纹饰的逻辑系统分析,突显其在礼器、容器等不同器类中的使用规则和文化内涵。强调母题与主题之间的关系,阐释青铜器纹饰的符号演变过程,并将其置于商周历史背景下进行深层次的哲学符号学解读。通过本研究,不仅深化了对青铜器纹饰的理解,还为维特根斯坦的符号学理论在考古学研究中的应用提供了有益的范例。

河南灵宝旧石器考古调查报告

2016年以来,河南省文物考古研究院等单位在河南三门峡地区进行旧石器考古调查,在灵宝窄口水库周边发现旧石器地点8处、石制品74件及少量动物化石;石制品中有47件出自地层。石制品主要埋藏在黄土堆积和河流相堆积中,原料以石英岩为主,类型包括石器、石核、石片、断块等。石核包括单台面石核、双台面石核、盘状石核和多台面石核;石片以锤击石片为主;石器主要为刮削器和砍砸器,也有似手斧和凹缺器等。从技术和类型上判断,该地区的石器工业属于中国北方旧石器时代早期普遍存在的石核-石片工业。石制品多出自黄土-古土壤条带L11~S5之间,年代范围为0.99~0.48 MaBP,属旧石器时代早期。本次调查成果表明,该地区具有发现更多、更重要旧石器时代遗址并开展深入考古发掘与研究的潜力。

能谱CT去金属伪影技术对髋关节置换患者置换物伪影及成像质量效果影响研究

目的 探循能谱CT去金属伪影(MAR)技术对髋关节置换患者置换物伪影及成像质量效果影响研究。方法回顾性研究,纳入首都医科大学附属北京友谊医院于2022年1月至2023年10月收治的42例髋关节置换患者为研究对象,均应用CT能谱及MAR技术,基于原始能谱CT扫描数据,权定能级范围70~130keV,并以20keV能级为间隔(依次为70keV、90keV、110keV及130keV),共计4组,所获单能量图像按MAR技术使用有无分为非MAR组、MAR组。观察两组置换物周围肌肉、周围骨组织、膀胱在相同能级下的信噪比(SNR)及伪影指数(AI)用以评估伪影情况,且由2名高年资影像医师对置换物周围肌肉、骨质结构及盆腔器官情况进行主观评分用以评估成像质量效果。结果 随能级升高,两组置换物周围肌肉、周围骨组织、膀胱SNR均有升高,AI均有降低,且在70~110KeV能级条件下,MAR组置换物周围肌肉、周围骨组织SNR均高于非MAR组,AI均低于非MAR组,差异有统计学意义(P<0.05),在70~130KeV能级条件下,MAR组膀胱SNR均高于非MAR组,AI均低于非MAR组,差异有统计学意义(P<0.05);随能级升高,非MAR组周围肌肉、盆腔器官主观评分先升后降,于110KeV抵达最高值,骨质结构主观评分逐步升高,MA R周围肌肉、骨质结构及盆腔器官情况主观评分均逐步升高,于70keV、90keV、110keV及130keV相同能级下,相较于非MAR组,MAR周围肌肉、骨质结构及盆腔器官情况主观评分均更高(P<0.05)。结论 髋关节置换患者应用能谱CT联合MAR技术,置换物伪影得以减少,成像质量效果获有提高,推荐在110KeV能级水平上采用MAR技术,可最大限度消除金属伪影,并充分显示临近组织,更有助于临床诊断。

LightSpeed VCT Console图像升级改造技术的优化方案

通过对LightSpeed VCT Console的升级改造与应用进行研究与分析,旨在探讨该技术的原理、方法和优势,并在实际场景中验证其效果。首先,介绍LightSpeed VCT Console的基本原理,即利用机器学习算法和大规模图像训练数据实现对低质量图像的提升和改造。然后,详细讨论LightSpeed VCT Console升级后在降低噪声、抑制伪影等方面的应用案例,展示该技术在图像质量改善方面的优秀表现。最后,对比LightSpeed VCT Console与其他图像改造技术的特点与差异,并提出未来该技术在图像处理领域的进一步发展方向。