Four-Dimensional Tomographic Reconstruction by Time Domain Decomposition

Никитин, В. В.; Carlsson, Marcus; Андерссон, Фредрик

doi:10.1109/tci.2019.2898088

Cited by 22 publications

(14 citation statements)

References 52 publications

(66 reference statements)

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“…This justifies the necessity of using fast continuous scanning of the sample with many 14 non-delayed rotations in order to capture fast water, gas, and gas-hydrate redistribution processes. In (Nikitin et al, 2019) we introduced a method for suppressing motion artifacts in the data acquired with continuous scanning and tested it on real data sets. Combining the developed method and fast acquisition we envision a 10-fold decrease of the scanning time that will help in suppressing motion artifacts observed in some of the images presented here.…”

Section: Discussionmentioning

confidence: 99%

Dynamic in-situ imaging of methane hydrate formation and self-preservation in porous media

Никитин

Дугаров

Duchkov

et al. 2020

Marine and Petroleum Geology

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Section: Discussionmentioning

confidence: 99%

Dynamic in-situ imaging of methane hydrate formation and self-preservation in porous media

Никитин

Дугаров

Duchkov

et al. 2020

Marine and Petroleum Geology

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“…Also, when dealing with spectral or generic multi-channel data, multi-channel reconstruction methods [68][69][70][71][72] can be used to increase the reconstruction accuracy even further. When dealing with objects that may change in time, dynamic reconstruction methods can be considered [73][74][75].…”

Section: Discussionmentioning

confidence: 99%

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Zeegers¹,

Leeuwen²,

Pelt³

et al. 2022

Preprint

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Detection of unwanted ('foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labour requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that have been acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way compared to conventional radiograph annotation. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting. Moreover, for real experimental data we show that the workflow leads to higher foreign object detection accuracies than with standard radiograph annotation.

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“…To enable quantitative comparisons between algorithms for dynamic tomography (Kazantsev et al, 2015;Mohan et al, 2015;Van Nieuwenhove et al, 2017;Nikitin et al, 2019), 4D phantoms are needed. Similar to 3D phantoms, these phantoms should be challenging, representative, flexible, and suitable for data-driven applications.…”

Section: D Extensionsmentioning

confidence: 99%

Foam-like phantoms for comparing tomography algorithms

Pelt

Hendriksen

Batenburg

2022

J Synchrotron Radiat

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Tomographic algorithms are often compared by evaluating them on certain benchmark datasets. For fair comparison, these datasets should ideally (i) be challenging to reconstruct, (ii) be representative of typical tomographic experiments, (iii) be flexible to allow for different acquisition modes, and (iv) include enough samples to allow for comparison of data-driven algorithms. Current approaches often satisfy only some of these requirements, but not all. For example, real-world datasets are typically challenging and representative of a category of experimental examples, but are restricted to the acquisition mode that was used in the experiment and are often limited in the number of samples. Mathematical phantoms are often flexible and can sometimes produce enough samples for data-driven approaches, but can be relatively easy to reconstruct and are often not representative of typical scanned objects. In this paper, we present a family of foam-like mathematical phantoms that aims to satisfy all four requirements simultaneously. The phantoms consist of foam-like structures with more than 100000 features, making them challenging to reconstruct and representative of common tomography samples. Because the phantoms are computer-generated, varying acquisition modes and experimental conditions can be simulated. An effectively unlimited number of random variations of the phantoms can be generated, making them suitable for data-driven approaches. We give a formal mathematical definition of the foam-like phantoms, and explain how they can be generated and used in virtual tomographic experiments in a computationally efficient way. In addition, several 4D extensions of the 3D phantoms are given, enabling comparisons of algorithms for dynamic tomography. Finally, example phantoms and tomographic datasets are given, showing that the phantoms can be effectively used to make fair and informative comparisons between tomography algorithms.

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Four-Dimensional Tomographic Reconstruction by Time Domain Decomposition

Cited by 22 publications

References 52 publications

Dynamic in-situ imaging of methane hydrate formation and self-preservation in porous media

Dynamic in-situ imaging of methane hydrate formation and self-preservation in porous media

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Foam-like phantoms for comparing tomography algorithms

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