Complete Material Characterisation Through A Single Polychromatic X-ray Scan
通过单次多色 X 射线扫描完成材料表征
基本信息
- 批准号:EP/X018377/1
- 负责人:
- 金额:$ 25.77万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2023
- 资助国家:英国
- 起止时间:2023 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
X-ray computed tomography (CT) is a powerful technique for imaging three-dimensional objects revealing the interior of a sample. X-ray CT is routinely used in medical imaging and industrial inspection, for example. Currently, contrast in X-ray CT images is largely based upon how strongly X-rays are attenuated by the sample. For example, bones in the human body strongly attenuate X-rays incident upon them, which is why bones, which are highly attenuating, appear with relatively high contrast in X-ray CT images. There is much interest in extending X-ray CT to be able to identify particular materials in a sample. For example, personalised diagnosis of complex diseases increasingly involves the use of imaging agents. Our method would provide the ability to pinpoint multiple agents simultaneously in a single scan, allowing for more effective diagnostic tests to be performed in a simple and speedy mannerAt present, material identification using X-ray CT can only be performed using two separate CT scans which therefore takes twice the amount of time of a single CT, thus delivering double the radiation does to the sample. Furthermore, this approach only works for monochromatic X-ray source, such as are available at synchrotrons, which are expensive to operate and have very limited capacity. Even if synchrotron access is possible, in many applications, such as medical imaging, performing two scans is not feasible due to the dose they can safely receive. More recently, methods which make use of expensive detectors can achieve this using a single scan, however severely limiting the sample size.We propose a method of performing material identification which uses a single phase-sensitive CT scan and is based on the edge illumination x-ray phase imaging technique which employs standard, therefore cost effective, x-ray imaging equipment. Our technique works by recognizing that three-dimensional images of electron density and effective atomic number, reconstructed from a single edge-illumination CT data set will only have sharp interfaces between two materials when both of these materials have been correctly identified. Thus, the unknown material can be determined by varying a reconstruction parameter and inspecting an interface between the known and unknown materials. The parameter which leads to this interface becoming sharp essentially reveals the unknown material. This phenomenon can be used sequentially to identify all materials in a sample, resulting in complete material identification using a single CT scan.
x射线计算机断层扫描(CT)是一种强大的技术,用于成像三维物体,揭示样品的内部。例如,x射线CT通常用于医学成像和工业检查。目前,x射线CT图像的对比度很大程度上取决于样品对x射线的衰减程度。例如,人体的骨骼会强烈衰减照射在其上的x射线,这就是为什么具有高衰减性的骨骼在x射线CT图像中会出现相对较高的对比度。人们对扩展x射线CT以识别样品中的特定物质很感兴趣。例如,复杂疾病的个性化诊断越来越多地涉及到显像剂的使用。我们的方法将提供在一次扫描中同时确定多个病原体的能力,允许以简单和快速的方式进行更有效的诊断测试。目前,使用x射线CT进行材料鉴定只能使用两次单独的CT扫描进行,因此需要两倍于单次CT的时间,从而向样品提供两倍的辐射。此外,这种方法只适用于单色x射线源,如同步加速器,这是昂贵的操作和非常有限的容量。即使同步加速器进入是可能的,在许多应用中,如医学成像,由于它们可以安全接收的剂量,进行两次扫描是不可行的。最近,使用昂贵的检测器的方法可以通过一次扫描实现这一目标,但严重限制了样本量。我们提出了一种使用单相敏感CT扫描进行材料识别的方法,该方法基于边缘照明x射线相位成像技术,该技术采用标准的,因此具有成本效益的x射线成像设备。我们的技术通过认识到,当两种材料都被正确识别时,从单边缘照明CT数据集重建的电子密度和有效原子序数的三维图像将只在两种材料之间具有尖锐的界面。因此,可以通过改变重构参数和检查已知和未知材料之间的界面来确定未知材料。导致界面变尖锐的参数本质上揭示了未知物质。这种现象可以依次用于识别样品中的所有材料,从而使用一次CT扫描完成材料识别。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Peter Munro其他文献
114 On line portal imaging
- DOI:
10.1016/0360-3016(95)97620-g - 发表时间:
1995-01-01 - 期刊:
- 影响因子:
- 作者:
Peter Munro - 通讯作者:
Peter Munro
Mo1169 X-RAY PHASE CONTRAST IMAGING FOR STAGING ESOPHAGEAL TUMORS: PRELIMINARY RESULTS FROM THE VIOLIN STUDY
- DOI:
10.1016/s0016-5085(20)32710-4 - 发表时间:
2020-05-01 - 期刊:
- 影响因子:
- 作者:
Paul Wolfson;Jinxing Jiang;Alex Ho;Hazel McBain;Harry Delaney;Mark Hawthorne;Ash Wilson;Lorenzo massimi;Charlotte Maughan-Jones;khaled dawas;borzoueh Mohammadi;Majid Hashemi;Rami Sweis;Yassar A. Qureshi;Matthew Banks;David G. Graham;Rehan Haidry;Sarmed S. Sami;Marco Endrizzi;Peter Munro - 通讯作者:
Peter Munro
Peter Munro的其他文献
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{{ truncateString('Peter Munro', 18)}}的其他基金
Solving Maxwell's equations using deep learning
使用深度学习求解麦克斯韦方程组
- 批准号:
EP/V048465/1 - 财政年份:2021
- 资助金额:
$ 25.77万 - 项目类别:
Research Grant
X-ray elastography: a novel approach to breast imaging
X 射线弹性成像:一种新的乳腺成像方法
- 批准号:
EP/P005209/1 - 财政年份:2017
- 资助金额:
$ 25.77万 - 项目类别:
Research Grant
Computed microscopy: quantitative, deep-tissue imaging
计算机显微镜:定量、深层组织成像
- 批准号:
BB/P027008/1 - 财政年份:2017
- 资助金额:
$ 25.77万 - 项目类别:
Research Grant
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