Low-dose CT image denoising method based on generative adversarial network

In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.

基于X-rayμCT技术的玉米干燥损伤定量研究

应力裂纹是玉米干燥损伤的主要形式,一些微裂纹无法肉眼观察但影响玉米的机械强度、发芽率以及活力等。为此,利用X-rayμCT技术对不同干燥条件下玉米进行扫描,使用深度学习算法分割与胚乳密度相近的胚区域并结合软件对玉米内部物性特征参数进行提取,提出了一种新的玉米内部损伤定量评价方法。结合定量研究结果提出的玉米干燥损伤模型,可为玉米损伤预测提供新思路。

Mortality outcomes of low-dose computed tomography screening for lung cancer in urban China:a decision analysis and implications for practice

Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.Methods: A decision tree model with three scenarios(low-dose CT screening, chest X-ray screening, and no screening) was developed to compare screening results in a simulated Chinese urban cohort(100,000 smokers aged45-80 years). Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality(primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Results: Among the 100,000 subjects, there were 448,541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively(17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, lowdose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths(relative numbers) with low-dose CT screening in the worst and optimal cases were16(5.4%) and 288(40.2%) over no screening, respectively.Conclusions: In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China.However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed.

Assessing efficacy of standard impregnation techniques on die-cast aluminum alloys using X-ray micro-CT

Utilizing lightweight Al alloys in various industrial applications requires achieving precise pressure tightness and leak requirements.Vacuum pressure impregnation(VPI)with thermosetting polymers is commonly used to address leakage defects in die-cast Al alloys.In this study,the efficacy of the VPI technique in sealing alloy parts was investigated using a combination of nondestructive micro X-ray computed tomography(micro XCT)and a standard leak test.The results demonstrate that the commonly used water leak test is insufficient for determining the sealing performance.Instead,micro XCT shows distinct advantages by enabling more comprehensive analysis.It reveals the presence of a low atomic number impregnates sealant within casting defects,which has low grey contrast and allows for visualizing primary leakage paths in 3D.The effective atomic number of impregnated resin is 6.75 and that of Al alloy is 13.69 by dual-energy X-ray CT.This research findings will contribute to enhancing the standard VPI process parameters and the properties of impregnating sealants to improve quality assurance for impregnation in industrial metals.

Low-Dose CT Image Denoising Based on Improved WGAN-gp

In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.

A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Hformer:highly efficient vision transformer for low-dose CT denoising

In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.

Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

Body Pushers: Low-Dose CT, Always the Best Choice? A Study of the Diagnostic Performance of CT Scout View

Purpose: The purpose of this study has been to evaluate the diagnostic information contained in the CT scout view in the detection of body packing. Materials and methods: Retrospect analysis of 43 CT scans between July 2011 and June 2013 in asymptomatic suspects of body packing (29 men, 14 females, mean age 38 ± 9 years). Results: A total of 11 positive cases of body packing were identified. In 10 (91%) of the cases packets were relatively large and spares in number (3 or less);in 7 (64%) a single packet has been identified. 6 (55%) of the packets were located rectally, 4 (36%) vaginally and in 1 (9%) case multiple small packets of approximately 1 cm in size were found to have been ingested orally. Maximum and minimum diameters were 5.9 ± 3 cm and 2.9 ± 1.4 cm, respectively. The mean weight of packets was 7.5 ± 4.2 g (range 2 - 54 g). In 73% (n = 8) heroin had been detected;other drugs such as cocaine (n = 1) and cannabis (n = 1) were encountered once, respectively. One packet was identified retrospectively and its content could therefore not be identified. The average effective dose was 3.8 ± 2.1 mSv for CT, of that 0.12 ± 0.01 mSv was required for the CT scout view. Conclusion: If CT scout view were treated as a diagnostic image, some CT scans may be omitted, thereby maintaining streamlined operations and achieving further dose reduction jointly in the workup of body packing.

Low-Dose High-Pitch Cardiac CT to Evaluate Abnormal Pacemaker Lead Position

Displacement of a cardiac pacemaker lead or myocardial perforation by a lead is an infrequent complication of pacemaker implantation. While standard cardiac CT is useful for diagnosing such complications when routine examinations can not, the potential risks associated with exposure to the high doses of radiation used in CT are of concern. Here, we report three cases in which pacemaker lead displacement or myocardial perforation was definitively diagnosed by low-dose cardiac CT used in the high-pitch spiral mode, when other more routine examinations failed to suggest their occurrence. The mean dose of radiation used for definitively diagnosing the three patients was relatively low (2.19 mSv).

基于X-ray CT的沥青混合料空隙测试精度影响因素分析

为了分析细观尺度下沥青混合料空隙率测试精度的影响因素,采用德国Phoenix v|tome|x S240型工业X-ray CT机,结合正交试验设计与方差分析,研究不同CT参数及级配类型对沥青混合料三维重构体积参数的影响.通过对不同级配类型马歇尔试件扫描并进行三维重构与体积分析,建立影响因素与空隙率测试结果之间的关系,进而确定最优扫描参数.试验结果表明,基于细观尺度对沥青混合料进行三维体积分析是可行的;扫描电压、电流及级配类型对重构结果有不同程度的影响;适当提高电压、电流及选择恰当的滤波片可提高测试结果的准确性;在恰当的设备参数条件下,基于细观尺度与宏观尺度下得到的沥青混合料空隙率具有良好的相关性,有助于提高测试结果的准确性与试验的经济性.

定向钢纤维混凝土中的钢纤维分布X-ray CT分析

为分析定向钢纤维混凝土中钢纤维的分布,通过X-CT扫描原位测试确定了混凝土中所有钢纤维的空间位置坐标,根据钢纤维空间坐标,分析计算钢纤维的角度分布及纤维方向效应系数。结果表明,定向钢纤维混凝土中,与设计的目标方向夹角<30°的钢纤维根数占总数量的比例达85%,定向钢纤维的定向效果较好;定向钢纤维混凝土中纤维方向效应系数增加25%,增强效率显著提高。

基于X-ray CT技术研究混凝土内部损伤的研究进展

使用X射线断层扫描(X-ray CT)方法可以观测混凝土的内部微观结构并据此分析混凝土的内部损伤,这对研究混凝土材料的性能具有重要意义。CT分辨率高低和试件尺寸大小之间的矛盾以及加载设备的局限阻碍了其在混凝土材料研究中的推广应用。随着CT设备的不断优化,CT用于混凝土的研究逐渐由定性分析发展为定量分析,为进一步研究混凝土材料的性能提供了可靠方法。近五年来,CT用于混凝土的研究由孔的分布、裂缝扩展形态及位置、纤维分布、纤维腐蚀状态等定性分析发展为定量分析。使用CT数据进行定量分析主要有五个方面,一是计算各相在不同条件下的体积,以此分析混凝土损伤变化规律;二是分析孔隙参数,如孔的尺寸、孔径分布、不同孔体积占比等,以此研究孔隙与混凝土损伤之间的关系;三是分析裂缝宽度、体积变化,以此分析混凝土断裂变化规律;四是定量评估纤维在混凝土内部的分散状态及方向,以此研究纤维对混凝土性能的影响;五是基于CT图像构建三维模型进行模拟,获得弹性模量及应力应变分布并与试验结果作对比分析。本文基于孔结构和裂缝变化规律对X-ray CT应用于混凝土材料内部损伤的研究进行综述,对近些年来,特别是近五年的研究现状进行总结,分析X-ray CT用于混凝土研究的优劣势,为混凝土的研究者提供参考。

基于X-Ray CT试验的塌陷区回填体孔隙结构研究

为了研究地表塌陷区回填体破坏过程中的孔隙结构演化规律,利用医用X-Ray CT及小型加载装置开展了废石/尾砂混合回填体实时单轴压缩扫描试验,重构了不同应力状态下回填体的多组分结构模型。试验结果表明:随应力值增大,回填体孔隙率增加,应力超过峰值应力80%后,孔隙率陡增,孔隙主要以X型共轭面分布;回填体孔隙率随含石量增加而增大;回填体细观结构演化过程可划分孔洞持续压密、孔洞先压密后扩容、孔洞持续扩容、相邻微裂隙的贯通、不均质区的孔隙扩展6种模式;回填体在峰值应力80%以前,CT数、孔隙率变化幅度小,回填体中并未出现显著的贯穿性裂缝,主要以原生孔隙的压缩与扩容、次生孔隙的萌生与扩展两种状态为主;应力超过峰值应力80%后,CT数急剧降低,孔隙率陡增,回填体中的原生、次生孔隙结构逐渐贯通,形成贯穿性裂缝,回填体破坏。

肺癌骨转移^(99m)Tc-MDP骨显像的临床价值及与CT、X-rays检查对比观察

目的 评价99mTc MDP骨显像诊断肺癌骨转移的临床价值。方法 12 8例肺癌骨转移患者常规行99mTc MDP骨显像检查。 91例患者同时行骨显像、CT检查 :74例病变部位相符者同时行骨显像、X rays检查。分别对比观察99mTc MDP骨显像与CT、X rays诊断肺癌骨转移的阳性检出率 ,并分析相互之间产生差异的原因和机制。结果 全部病例中 ,99mTc MDP骨显像阳性检出率为 91.4% (117/12 8) ,假阳性率为 12 .0 % (16/13 3 ) ,假阴性率 8.5 9% (11/12 8) ;对比观察99mTc MDP骨显像与CT ,阳性检出率分别为 85 .7% (78/91)、2 9.7% (2 2 /74) ,(P <0 .0 1,已剔除部位不相符因素 ,但未剔除X线检查部位包括不全因素 )。结论 99mTc MDP骨显像诊断肺癌骨转移具有较高的临床价值 ,辅以CT、X rays检查可提高阳性诊断的准确性。

X-ray CT在纤维增强聚合物复合材料中的应用研究进展

X射线计算机断层扫描(X-ray computed tomography,X-ray CT)作为一种具有高分辨率、非破坏性、三维可视化等特征的材料表征测量技术,显示出独特的优势。X-ray CT不仅能得到试样的高分辨率外部特征,还能够获取其详细的内部结构信息,在各个领域已经受到越来越多的关注。通过简单比较几种无损检测技术的适用性和局限性,引出了X-ray CT的独特性,阐述了X-ray CT的原理以及实验室断层扫描和同步辐射计算机断层扫描(synchrotron radiation computed tomography,SRCT)的特点;从探索纤维增强聚合物(fiber reinforced polymer,FRP)复合材料的内部结构、评估制造过程、获取损伤失效演变以及X-ray CT与其他表征手段相结合等几个方面归纳了X-ray CT在FRP中的应用,并提出了X-ray CT在FRP研究中的挑战。

基于X-ray CT图像的集料形状表征方法

从细观角度出发,以集料X-Ray CT扫描图像为数据源,运用计算机图形图像技术建立集料颗粒的三维实体模型,在此基础上实现集料的体积、球度和棱角性的虚拟测量,并对集料体积的测量结果进行试验验证。结果表明,集料体积虚拟测量结果的最大误差为2%,平均误差为1.2%,验证所提出方法的可行性和有效性。

基于X-ray CT的沥青混合料计算机辅助设计技术的研究进展

利用X-ray CT技术,获取沥青混合料内部结构组成,分析体积参数,进而完成沥青混合料的计算机辅助设计的研究已成为沥青路面技术领域的关注热点.重点介绍了在利用X-ray CT进行沥青混合料体积组成分析与差异性物质辨识、混合料内部结构组成的三维图像重构、沥青混合料性能的虚拟试验等方面研究时存在的问题和可能的解决方法.同时,提出了进一步研究所需要解决的一些关键技术,给出了一个技术路线图以供日后的研究参考.

基于X-ray CT和离散元法级配离析对沥青混合料骨架结构力学性能的影响

为从细观角度探究分析级配离析对沥青混合料骨架结构力学性能的影响,基于X-ray CT断层图像扫描技术和离散元法建立三维混合料骨架模型,对不同级配离析情况下的混合料骨架内部受力情况、抵抗荷载性能、应力传递性能等方面进行了对比和研究。结果表明:提出了利用X-ray CT断层图像扫描技术构建更接近于真实形态的集料颗粒模型的方法;骨架结构中较粗集料承受了主要的外力荷载作用;以典型的SMA-16级配为例,4.75~9.5 mm档集料是构成该级配集料骨架的关键粒径;骨架结构中细集料的增多有助于提高骨架的抗压强度,发生粗集料离析时,抗压强度将随着离析程度的加剧而显著地降低;不同的级配离析状态对混合料骨架结构的竖向与横向应力传递性能产生不同影响,其中细集料离析对应力传递性能的影响更大。

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.