基于组件技术的工业CT控制软件开发

为了解决工业CT控制软件的定制开发问题，本文提出了利用组件技术开发工业CT控制软件。重点分析了工业CT控制系统的硬件结构，控制系统中的主要信息；提出了基于组件技术的工业CT控制系统的体系结构，并对工业CT控制系统中的功能组件进行了划分；根据基金会现场总线的规范定义了功能组件的基本结构，进一步分析了组件的组装；采用COM组件技术完成了组件的设计及工业CT控制软件的开发。实际应用表明，组件技术对解决工业CT控制软件的定制开发问题是有效的。     

基于凸包算法的三维表面重建中边缘轮廓提取

在三维表面重建过程中,边缘轮廓的提取起着关键的作用.为了对头部CT图片中的颅骨边缘进行有效地提取,提出利用平面凸包进行边缘轮廓提取的方法.利用该方法提取出头部CT图像中的颅骨边缘,并将其作为三维表面重建中的轮廓输入,取得了良好的效果.同时,将通过该方法得到的颅骨边缘应用于基于颅骨的三维面貌复原技术研究中,为面貌复原技术的发展提供了新的思路.     

一种新颖三维ICT扫描轨道及重构算法

工业CT图像直接三维重建是近年来工业CT技术研究的前沿领域,针对FDK近似重建算法重建精度不高的问题,在不提高现有系统复杂程度的前提下,讨论了一种新的垂直双圆周扫描轨道在工业CT中的应用,提出了相应的FDK改进算法,获取物体投影的完备数据,提高重建的精度。     

基于凸包算法的三维表面重建中边缘轮廓提取

在三维表面重建过程中，边缘轮廓的提取起着关键的作用。为了对头部CT图片中的颅骨边缘进行有效地提取，提出利用平面凸包进行边缘轮廓提取的方法。利用该方法提取出头部CT图像中的颅骨边缘，并将其作为三维表面重建中的轮廓输人，取得了良好的效果。同时，将通过该方法得到的颅骨边缘应用于基于颅骨的三维面貌复原技术研究中，为面貌复原技术的发展提供了新的思路。     

数字证书透明性CT机制安全威胁研究

公钥基础设施（Public Key Infrastructure,PKI）和SSL/TLS加密协议,是当今互联网进行安全通信的关键要素,但存在被攻击或恶意CA所导致的重大安全隐患.2013年,谷歌提出证书透明性（Certificate Transparency,CT）技术用于对CA签发的HTTPS证书进行公开审计.目前,大多CA都支持CT,在谷歌生态中,浏览器也广泛部署CT技术,但CT也引入了新的运行风险.本文从信任机制、安全威胁两个角度梳理了CT技术,归纳总结出基于CT的Web PKI信任模型和安全威胁模型,并提出安全保障机制及应用部署建议,最后对于CT技术的发展进行了总结和展望.     

图像引导放疗中的锥束CT灌注成像方法

为了实现图像引导放疗与功能成像结合的功能图像引导放疗,研究运用图像引导放疗中的锥束CT影像设备进行CT灌注成像的可能性。为了解决锥束CT不能快速成像,进而不能获取准确的体素时间-密度曲线(IDC)的问题,提出了一种基于投影数据的体素密度建模方法。首先,运用计算机仿真技术模拟出锥束CT投影数据;然后,对各体素密度值进行数学建模;最后,利用计算机最优化值求解技术,编程实现模型具体参数的计算。实验证明,该模型得到的TDC和DCE-CT实测获得的TDC有很高的吻合度。该模型实现图像引导放疗中的锥束CT灌注成像是可行的。     

基于统计特性的小波噪声抑制在低剂量CT中的应用

较高的照射剂量限制了X线断层成像(computed tomography,CT)技术在筛查及体检中的应用,目前临床常采用降低剂量的解决方案,但CT图像质量亦有明显下降。为提高低剂量CT的重建质量,提出了一种基于投影数据统计特性的小波去噪算法。通过分析低剂量投影数据的噪声特性,发现在投影域其噪声均值和方差接近非线性高斯分布,根据非平稳噪声在平稳小波域中的性质,结合贝叶斯估计方法对小波系数进行基于最小均方误差的自适应滤波,实现了图像信噪分离的目的。滤波完成后,采用常规滤波反投影(FBP)法重建CT图像。较传统算法,该方法具有较高的信噪比,实验结果表明,该算法能够有效地抑制噪声,且较好地保留图像细节。     

PET/CT成像呼吸运动B样条校正

PET/CT成像中的人体呼吸运动会造成图像运动模糊,会严重影响图像质量,对医生的诊断造成影响。目前常用的呼吸门控技术能够在一定程度上改善图像质量,但是均存在其局限性。提出了一种基于CT图像提取呼吸运动特征的B样条方法,对呼吸运动图像进行校正。在PET/CT上获取与PET图像周期匹配的CT图像序列,通过B样条配准方式对在呼吸周期内的CT图序列像提取运动信息;对CT图像所匹配的PET图像做基于运动特征信息的校正变换进行运动校正。几何位移形变和像素人体模实验结果表明,提出的方法对PET/CT呼吸运动图像质量改进明显,具有研究价值。     

低剂量CT成像的研究现状与展望

低剂量CT扫描能够有效减少患者接受的辐射剂量,但同时会导致成像质量的下降。在降低剂量的同时,获得可用于临床诊断的高质量图像已经成为近年来CT领域研究的重点方向。本文从低剂量CT的成像方式和图像质量改善等方面介绍了国内外低剂量CT技术的发展与应用,包括低剂量CT扫描的实施方式,低剂量CT扫描的数据模型,相关重建算法与图像后处理策略。本文对近年来国内外研究团队在低剂量CT技术的研究上进行了概括,最后对目前该领域的研究工作进行了总结和分析。     

探究工业CT图像的缺陷定位以及三维分割技术

通过对工业CT图像的特点、工业CT图像分割技术概述,以及工业CT图像的缺陷定位和三维分割技术等方面进行简要研究,进而为技术人员的工业CT技术发展贡献微薄之力.     

The development of a cognitive tool for teaching and learning fractions in the mathematics classroom: A design-based study

Two cycles of design-based research of a cognitive tool (CT) for teaching fractions have been completed. Following the success of a quasi-experimental study of the enhanced CT derived from the second cycle of design-based research, this article reports the findings of a pre-test–post-test control group empirical study using the enhanced CT in the classroom. The results indicate that there were no statistically significant differences in learning outcomes between the exploratory learning approach, using the CT, and the traditional direct teaching approach. The CT enabled students to generate a procedural knowledge of adding and subtracting fractions with like and unlike denominators through an exploratory learning process. Teachers asserted that the CT was effective for stimulating reciprocal tutoring among students, and students were enthusiastic about using the CT as an educational tool. Hence, the CT has potential for further development as a tool for promoting collaborative learning in the classroom.     

Classification and recognition of computed tomography images using image reconstruction and information fusion methods

In this paper, we propose a diagnosis and classification method of hydrocephalus computed tomography (CT) images using deep learning and image reconstruction methods. The proposed method constructs pathological features differing from the other healthy tissues. This method tries to improve the accuracy of pathological images identification and diagnosis. Identification of pathological features from CT images is an essential subject for the diagnosis and treatment of diseases. However, it is difficult to accurately distinguish pathological features owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions, etc. Some study results reported that the ResNet network has a better classification and diagnosis performance than other methods, and it has broad application prospectives in the identification of CT images. We use an improved ResNet network as a classification model with our proposed image reconstruction and information fusion methods. First, we evaluate a classification experiment using the hydrocephalus CT image datasets. Through the comparative experiments, we found that gradient features play an important role in the classification of hydrocephalus CT images. The classification effect of CT images with small information entropy is excellent in the evaluation of hydrocephalus CT images. A reconstructed image containing two channels of gradient features and one channel of LBP features is very effective in classification. Second, we apply our proposed method in classification experiments on CT images of colonography polyps for an evaluation. The experimental results have consistency with the hydrocephalus classification evaluation. It shows that the method is universal and suitable for classification of CT images in these two applications for the diagnosis of diseases. The original features of CT images are not ideal characteristics in classification, and the reconstructed image and information fusion methods have a great effect on CT images classification for pathological diagnosis.

     

Analysing computational thinking in collaborative programming: A quantitative ethnography approach

Computational thinking (CT), the ability to devise computational solutions for real‐life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence through collaborative problem solving remains unclear. This study adopted an innovative approach, quantitative ethnography, to analyze the collaborative programming activities of a high‐performing and a low‐performing team. Both the discourse analysis and epistemic network models revealed that across concepts, practices, and identity, the high‐performing team exhibited CT that was systematic, whereas the CT of the low‐performing team was characterized by tinkering or guess‐and‐check approaches. However, the low‐performing group's CT development trajectory ultimately converged towards the high‐performing group's. This study thus improves understanding of how novices learn CT, and it illustrates a useful method for modeling CT based in authentic problem‐solving contexts.     

Charge transport in cancer-related genes and early carcinogenesis

The electronic transmission properties of DNA molecules are believed to play a significant role in many physical phenomena taking place in living organisms (Chakraborty, 2007) [1]. Here we study the charge transport (CT) properties of cancer-related genes, including some of the most important tumor suppressors. We find that the changes in averaged CT around the sites of pathogenic and cancerous mutations are statistically smaller than those on sites where pathogenic mutations have not been observed. The results suggest that CT might be an indicator to discriminate between pathogenic and non-pathogenic mutations at an early stage. Mutations which cause little change in CT may be more likely to occur, or more likely to be missed by damage-repair enzymes which probe CT, and are therefore more likely to persist and cause disease.     

Exploring age and gender differences of computational thinkers in primary school: A developmental perspective

Over the past decade, the call to foster computational thinking (CT) in every child has received considerable attention. However, there is little understanding of whether children are developmentally ready to think computationally and what specific CT concepts and skills can be developed at various ages. This study explored the developmental and gender differences in CT skills of 197 Grade 4–6 students (aged 9–13) before being exposed to instruction and investigated the age–gender interaction effects on their CT acquisition in an intervention combining both programming and non-programming (unplugged) activities. Results show that students' CT skills followed a developmental progression before instruction. Gender difference across ages was insignificant in conditionals, logical operators, pattern recognition and generalization skills. Additionally, students of different ages developed CT differently during the intervention, and their CT acquisition was unaffected by gender. Implications for practice and research in CT education were discussed.     

Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences

This work investigates the development of students’ computational thinking (CT) skills in the context of educational robotics (ER) learning activity. The study employs an appropriate CT model for operationalising and exploring students’ CT skills development in two different age groups (15 and 18 years old) and across gender. 164 students of different education levels (Junior high: 89; High vocational: 75) engaged in ER learning activities (2 hours per week, 11 weeks totally) and their CT skills were evaluated at different phases during the activity, using different modality (written and oral) assessment tools. The results suggest that: (a) students reach eventually the same level of CT skills development independent of their age and gender, (b) CT skills in most cases need time to fully develop (students’ scores improve significantly towards the end of the activity), (c) age and gender relevant differences appear when analysing students’ score in the various specific dimensions of the CT skills model, (d) the modality of the skill assessment instrument may have an impact on students’ performance, (e) girls appear in many situations to need more training time to reach the same skill level compared to boys.     

基于边缘保持的CT图像插值放大算法

由于医学硬件设备和辐射剂量的限制,为了获得高分辨力、高质量的CT图像以协助医生进行诊断和治疗,通常需要从软件方面将CT图像放大或局部CT图像放大,考虑到CT图像边缘在CT图像处理中的重要性,提出一种基于边缘保持的CT图像插值算法,该算法包括两个步骤,第一步对原图像进行边缘提取以及沿边缘方向进行边缘插值放大;第二步进行图像平滑区域的双线性插值放大。该算法能很好地保护图像的边缘细节,文中给出了放大实例,实验结果证明了算法的有效性。     

螺旋锥束CT重建的近似逆算法

三维螺旋锥束CT以扫描速度快、成像分辨率高等诸多优点成为现代CT技术的一个重要发展方向。Katsevich精确FBP算法的提出,使得三维锥束CT研究获得了突破性进展。由于该算法的复杂性,应用中受到了限制。研究了Katsevich算法在检测板上沿滤波线展开的形式,其滤波运算由Hilbert核函数构成,利用近似逆的思想提出了融合的CT重建算法。该算法将Katsevich公式改写成近似逆的形式,得到了重建核的具体形式。     

On embedded FIR filter models for identifying continuous-time and discrete-time transfer functions: the RPM approach

For identifying a continuous-time (CT) transfer function model, data filtering is a solution which provides the necessary unmeasurable input--output derivative approximations. In discrete-time (DT) system identification, the well-known ARX model can be used successfully if the estimate is performed with suitable prefiltered data. This article describes the reinitialised partial moment (RPM) model which embeds implicitly a finite impulse response filter in both CT and DT domains. With knowledge of the important role of data prefiltering in standard methods, this RPM model embedded filter gives particular properties to this original tool. Although both the CT RPM model and the DT RPM model present an embedded filter, the formulation and the implementation in the CT and the DT domains are different. Therefore, the aim of this article is to present a tutorial on the RPM models and to give an overview of all the applications.     

Computer-assisted categorization of brain computerized tomography pixels into cerebrospinal fluid, white matter, and gray matter

A computer-assisted method was employed to estimate the amounts of cerebrospinal fluid (CSF), white matter, and gray matter in individual computerized tomography (CT) scans of brains. By means of an image processing procedure (DMORPH), the means +/- SD CT numbers of "pure" CSF, white matter, and gray matter were determined in each scan and stored. A CATSEG program used these means to define ranges for CT numbers for each of the three tissues on each scan, and to assign each pixel in a scan to one of the three categories. Summing over seven serial scans provided volumetric estimates of CSF, white matter, and gray matter in a brain segment. For 10 subjects aged 21 to 43 years, CSF volume equaled 1.4 to 4.7% of the total segment volume, white matter equaled 37.5 to 48.2%, and gray matter equaled 50.2 to 58.9%. Image processing hardware and software which allow standardized sampling from CT images for the evaluation of surface areas and CT numbers are described. These procedures, as applied to CT scans of the human brain, can be used to estimate the volumes of CSF, white matter, and gray matter in a selected intracranial segment.