Tomographic Imaging

断层成像

• 批准号: EP/M022498/1
• 负责人: Philip Withers
• 金额: $ 30.41万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM022498%2F1
• 关键词: Tomographic; Imaging

Computed Tomography (CT) is a powerful non-destructive evaluation (NDE) technique for producing 2-D cross-sections and 3-D images of an object from a series of 2D projections (shadowgraphs). Critical to the process is the computer algorithms that are able to pre-process and then reconstruct the 3D images from many (often over thousands) shadowgraphs. There are three critical steps to obtaining useful information from tomography i) the correction and calibration stage starting from initial image capture, ii) the reconstruction of a 3D image from these 2D projections and iii) the analysis and quantification of features in the 3D object. While the techniques are rapidly growing in popularity, most users have fairly limited options when trying to recover the 3D image and are able to write only fairly primitive computer programs to extract key features from the images. The aim of this CCP is to establish a framework within which different reconstruction algorithms, artefact reduction codes, and image analysis procedures are made available to all.

计算机断层扫描 (CT) 是一种强大的无损评估 (NDE) 技术，用于从一系列 2D 投影（阴影图）生成物体的 2D 横截面和 3D 图像。该过程的关键是计算机算法能够对许多（通常超过数千张）阴影图进行预处理，然后重建 3D 图像。从断层扫描中获取有用信息需要三个关键步骤：i) 从初始图像捕获开始的校正和校准阶段，ii) 从这些 2D 投影重建 3D 图像，以及 iii) 3D 对象特征的分析和量化。虽然这些技术正在迅速普及，但大多数用户在尝试恢复 3D 图像时的选择相当有限，并且只能编写相当原始的计算机程序来从图像中提取关键特征。该 CCP 的目标是建立一个框架，在该框架内向所有人提供不同的重建算法、伪影减少代码和图像分析程序。

项目成果

Single droplets to particles - size, shape, shell thickness and porosity analyses using X-ray computed tomography

• DOI: 10.1016/j.ces.2021.116879
• 发表时间: 2021-06
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: H. Abdullahi;Petros Neoptolemou;C. Burcham;T. Vetter

High-grip and hard-wearing graphene reinforced polyurethane coatings

• DOI: 10.1016/j.compositesb.2021.108727
• 发表时间: 2021-02
• 期刊: Composites Part B-engineering
• 影响因子: 13.1
• 作者: Monica Alberto;Maria Iliut;Mohan Kumar Pitchan;J. Behnsen;A. Vijayaraghavan

Computed Tomography: Algorithms, Insight, and Just Enough Theory

计算机断层扫描：算法、洞察力和足够的理论

• 发表时间: 2021
• 作者: Andersen Martin S.

Loss of PKCa increases arterial medial calcification in a uremic mouse model of chronic kidney disease

PKCa 缺失会增加慢性肾病尿毒症小鼠模型中的动脉内侧钙化

• DOI: 10.1101/2020.05.20.097642
• 发表时间: 2020
• 作者: Borland S

Reliability of Algorithms Interpreting Topological and Geometric Properties of Porous Media for Pore Network Modelling

• DOI: 10.1007/s11242-019-01244-8
• 发表时间: 2019-05-01
• 期刊: TRANSPORT IN POROUS MEDIA
• 影响因子: 2.7
• 作者: Baychev, Todor G.;Jivkov, Andrey P.;Withers, Philip J.
• 通讯作者: Withers, Philip J.