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通过加权反投影提高快速、时间分辨微型计算机断层扫描中的图像质量。

Improving image quality in fast, time-resolved micro-CT by weighted back projection.

作者信息

Heyndrickx Marjolein, Bultreys Tom, Goethals Wannes, Van Hoorebeke Luc, Boone Matthieu N

机构信息

UGCT/Radiation Physics, Dept. Physics and Astronomy, Ghent University, Proeftuinstraat 86 - Building N12, 9000, Ghent, Belgium.

UGCT / PProgRess, Dept. Geology, Ghent University, Krijgslaan 281 - Building S8, 9000, Ghent, Belgium.

出版信息

Sci Rep. 2020 Oct 22;10(1):18029. doi: 10.1038/s41598-020-74827-x.

DOI:10.1038/s41598-020-74827-x
PMID:33093571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7581769/
Abstract

Time-resolved micro-CT is an increasingly powerful technique for studying dynamic processes in materials and structures. However, it is still difficult to study very fast processes with this technique, since fast scanning is typically associated with high image noise levels. We present weighted back projection, a technique applicable in iterative reconstruction methods using two types of prior knowledge: (1) a virtual starting volume resembling the sample, for example obtained from a scan before the dynamic process was initiated, and (2) knowledge on which regions in the sample are more likely to undergo the dynamic process. Therefore, processes on which this technique is applicable are preferably occurring within a static grid. Weighted back projection has the ability to handle small errors in the prior knowledge, while similar 4D micro-CT techniques require the prior knowledge to be exactly correct. It incorporates the prior knowledge within the reconstruction by using a weight volume, representing for each voxel its probability of undergoing the dynamic process. Qualitative analysis on a sparse subset of projection data from a real micro-CT experiment indicates that this method requires significantly fewer projection angles to converge to a correct volume. This can lead to an improved temporal resolution.

摘要

时间分辨显微CT是一种用于研究材料和结构中动态过程的日益强大的技术。然而,用这种技术研究非常快速的过程仍然很困难,因为快速扫描通常伴随着高图像噪声水平。我们提出了加权反投影法,这是一种适用于迭代重建方法的技术,它使用两种类型的先验知识:(1)一个类似于样品的虚拟起始体积,例如从动态过程开始之前的扫描中获得;(2)关于样品中哪些区域更有可能经历动态过程的知识。因此,该技术适用的过程最好发生在一个静态网格内。加权反投影法有能力处理先验知识中的小误差,而类似的4D显微CT技术要求先验知识完全正确。它通过使用一个权重体积将先验知识纳入重建中,该权重体积为每个体素表示其经历动态过程的概率。对来自实际显微CT实验的投影数据的稀疏子集进行的定性分析表明,该方法收敛到正确体积所需的投影角度明显更少。这可以提高时间分辨率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/5e07c6771c98/41598_2020_74827_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/d9c0da9f0572/41598_2020_74827_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/771577f6adf3/41598_2020_74827_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/96f384fb8d3b/41598_2020_74827_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/0e346ff04e7a/41598_2020_74827_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/d61b8da722aa/41598_2020_74827_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/a21272f507d1/41598_2020_74827_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/c267ac9d9e61/41598_2020_74827_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/5e07c6771c98/41598_2020_74827_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/d9c0da9f0572/41598_2020_74827_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/771577f6adf3/41598_2020_74827_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/96f384fb8d3b/41598_2020_74827_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/0e346ff04e7a/41598_2020_74827_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/d61b8da722aa/41598_2020_74827_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/a21272f507d1/41598_2020_74827_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/c267ac9d9e61/41598_2020_74827_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0379/7581769/5e07c6771c98/41598_2020_74827_Fig8_HTML.jpg

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