Algorithm for post-processing of tomography images to calculate the dimension-geometric features of porous structures

Chukalina, Marina; Khafizov, Anvar; Kokhan, Vladislav; Бузмаков, А. В.; Сенин, Р. А.; Уваров, В. И.; Grigoriev, Maxim V.

doi:10.18287/2412-6179-co-781

Cited by 7 publications

(3 citation statements)

References 54 publications

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“…The porous media are generated using the Gaussian blur method. Similar studies have been performed with some modifications related to processing and generating methods [20][21][22] . One of the main analyzed parameters in the current study is the geometric tortuosity based on Eq.…”

Section: Geometric Path Mmentioning

confidence: 88%

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Ávila

Pagalo

Espinoza‐Andaluz

2022

Sci Rep

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Porous materials are of great interest in multiple applications due to their usefulness in energy conversion devices and their ability to modify structural and diffusive properties. Geometric tortuosity plays an important role in characterizing the complexity of a porous medium. The literature on several occasions has related it as a parameter dependent on porosity only. However, due to its direct relationship with the morphology of the medium, a deeper analysis is necessary. For this reason, in the present study, the analysis of the geometric tortuosity is proposed considering the porosity and the pore size distribution. Geometric tortuosity in artificially generated digital porous media is estimated using the A-star algorithm and the Pore Centroid method. By performing changes in the size of the medium and the distribution of the pore size, results are obtained that indicate that the geometric tortuosity does not only depend on the porosity. By maintaining the same porosity, the geometric tortuosity increases if the pore size is reduced. Similarly, these pore size effects are greater if the size of the medium is reduced. The A-star algorithm was found to be more suitable to characterize the majority of paths within the half-pore. On the other hand, to increase the size, the Pore Centroid method is the most appropriate. Finally, three types of correlations were generated relating tortuosity with porosity and pore size. All the correlations were determined with 95% of interval confidence.

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Section: Geometric Path Mmentioning

confidence: 88%

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Ávila

Pagalo

Espinoza‐Andaluz

2022

Sci Rep

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“…The study of hierarchically structured porous morphology is requested in the manufacturing of batteries [37], for example. Since it is no longer enough to visually analyze the reconstructed images of porous objects, methods for the automatic binarization of tomographic images of porous structures, used as a mask in our algorithm, have also been developed [38]. We understand that the trade-off between the rotation speed and the signal-to-noise ratio in the monitoring of dynamic processes is very important [39].…”

Section: Discussionmentioning

confidence: 99%

Iterative Algorithm for 4D Tomography Reconstruction Using a Single Projection per Time Step

et al. 2022

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In this paper, a new method for the mathematical tomographic reconstruction of time-varying objects (4D tomography) is presented. The method is applicable under the conditions that the amount of substance at each spatial point does not decrease, and only one projection image is available per time point during the process of the object changing. The temporal resolution of the proposed method is obtained by using an iterative approach to reconstruction. Prior knowledge about object morphology is exploited to improve the convergence of the algorithm by reducing the number of processed spatial points. Verification of the algorithm operation was carried out using model examples. The presented method was experimentally tested on the tomographic reconstruction of the dynamics of the capillary rise of a liquid. Our experiments demonstrated that monitoring fast processes, when the speed of the process does not allow to measure more than one tomographic projection, is potentially possible.

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“…X-ray computed tomography (CT) is the widely used non-destructive method for reconstructing the internal structure of an object. In medicine, CT is used for a noninvasive examination of the body structure [1]. This makes it possible to diagnose diseases such as cancer and COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Neural network regularization in the problem of few-view computed tomography

et al. 2022

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The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.

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Algorithm for post-processing of tomography images to calculate the dimension-geometric features of porous structures

Cited by 7 publications

References 54 publications

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Iterative Algorithm for 4D Tomography Reconstruction Using a Single Projection per Time Step

Neural network regularization in the problem of few-view computed tomography

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