2020
DOI: 10.1038/s41598-020-74827-x
|View full text |Cite
|
Sign up to set email alerts
|

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

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 pr… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
6

Relationship

1
5

Authors

Journals

citations
Cited by 7 publications
(6 citation statements)
references
References 41 publications
0
6
0
Order By: Relevance
“…Despite our use of GPUs, there is certainly a speed‐up to be gained from optimizing the code toward high‐performance computing. Moreover, we also suspect that there could be considerable room for reducing the computation load by directing the reconstruction efforts to regions within the sample that de facto exhibit significant changes at the given time step (Heyndrickx et al., 2020). As briefly discussed in the Introduction, machine learning is under rapid development and will prove a highly important tool also for measuring and understanding transport in porous media in the coming years.…”
Section: Discussionmentioning
confidence: 99%
“…Despite our use of GPUs, there is certainly a speed‐up to be gained from optimizing the code toward high‐performance computing. Moreover, we also suspect that there could be considerable room for reducing the computation load by directing the reconstruction efforts to regions within the sample that de facto exhibit significant changes at the given time step (Heyndrickx et al., 2020). As briefly discussed in the Introduction, machine learning is under rapid development and will prove a highly important tool also for measuring and understanding transport in porous media in the coming years.…”
Section: Discussionmentioning
confidence: 99%
“…This way, the particle positions were calculated for each radiograph time step in 7 consecutive CT scans of 360 and 700 radiographs each, matching the experimental acquisition. Each of these radiographs was then calculated by raytracing using the in-house developed CTRex code (Heyndrickx et al, 2020;Schryver et al, 2018). Poisson noise was added on the radiographs to match the noise level in the experiment.…”
Section: Simulated Ct Data Sets For Methods Validationmentioning
confidence: 99%
“…All high-speed/in-situ publications included in figure 13 use FBP for fair comparative purposes, but there were 9 out of the 596 studies from the literature search that used iterative reconstruction methods that typically require a lower numbers of projections [55,[110][111][112][113][114][115][116][117]. For example Myers et al [113] experiments with two-phase fluid flow, used an iterative algorithm that exploits the a priori knowledge of the sample.…”
Section: Number Of Projectionsmentioning
confidence: 99%