Interlaboratory Study of Digital Volume Correlation Error Due to X-Ray Computed Tomography Equipment and Scan Parameters: an Update from the DVC Challenge

Croom, Brendan P.; Burden, Diana Elizabeth; Jin, H.; Vonk, N. H.; Hoefnagels, Jpm Johan; Smaniotto, Benjamin; Hild, François; Quintana, Enrico C.; Sun, Qilong; Nie, Xu; Li, X.

doi:10.1007/s11340-020-00653-x

Cited by 15 publications

(4 citation statements)

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“…More projections mean more data for digital volume reconstruction and a longer acquisition time. Longer scanning times may result in image distortion because of X‐ray source drift or thermal expansion of the sample (Croom et al., 2021; Wang et al., 2017), and also escalated imaging costs. Moreover, some experimental procedures may require a quick scan, such as in the case of time‐lapse observations for core flooding experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, some experimental procedures may require a quick scan, such as in the case of time‐lapse observations for core flooding experiments. In this case, one may opt for a fewer number of projections which may lead to lower‐quality images that introduce errors for volume reconstruction (Croom et al., 2021; Xuanhao et al., 2022). Thus, choosing the appropriate number of projections holds practical importance for digital volume reconstruction, especially for complex porous media samples.…”

Section: Introductionmentioning

confidence: 99%

“…Binning is the process of merging charges from the neighboring voxels in the Charge Coupled Device (CCD) detector during readout. It is implemented to improve the signal‐to‐noise ratio (Croom et al., 2021). For example, going from bin size 1 to two increases the X‐ray intensity captured by each voxel by the square of the binning increase.…”

Section: Introductionmentioning

confidence: 99%

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Effect of X‐Ray Computed Tomography Imaging Parameters on Quantification of Petrophysical Properties

Salek,

Beckingham

2023

Earth and Space Science

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Three‐dimensional (3D) X‐ray computed tomography (X‐ray CT) imaging has emerged as a nondestructive means of microstructural characterization. However, obtaining and processing high‐quality and high‐resolution images is time‐consuming and often requires high‐performance computing, particularly with a high number of projections. This work evaluates the effect of 3D X‐ray CT imaging parameters on pore connectivity and surface area quantification in sandstone samples of varied composition. Samples from Bentheimer and Torrey Buff formations are imaged via 3D X‐ray CT imaging at resolutions ranging from 1.25 to 15 μm, bin sizes 1, 2, and 4, and number of projections from 400 to 4,500. Collected images are processed and analyzed using ImageJ and MATLAB to discern petrophysical properties and the results are compared with each other and Mercury Intrusion Capillary Pressure (MICP) results. Overall, little variation in bulk porosity with changing scanning parameters is observed. However, for low resolution and projection numbers, connected porosity is lower compared to bulk porosity due to a failure to capture microfeatures. Overall, mineral surface area is observed to decrease with increasing bin size, voxel size, and projection numbers, except an observed increase with projection numbers for Torrey Buff. The Torrey Buff samples contains comparatively more clays and even the highest resolution (1.25 μm) fails to separate the micrograins, which is reflected in the pore size distribution. Identifying these variations are helpful as discrepancies in imaged pore connectivity and surface area can largely impact assessments of fluid flow and transport in reactive transport simulations informed by this data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of X‐Ray Computed Tomography Imaging Parameters on Quantification of Petrophysical Properties

Salek,

Beckingham

2023

Earth and Space Science

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“…Example quantities of interest for foams and polymers include feature sizes (voids, particles, ligaments) [ 3 , 4 ], relative density and porosity [ 4 - 7 ], or anisotropy and auxaticity [ 8 , 2 ]. For polymer foam applications, in-situ load frames enable microstructural imaging, stress, and strain measurements, which enable a volumetric assessment of the onset of instabilities, measure materials properties such as the tangent Poisson’s ratio, and apply techniques such as digital volume correlation (DVC) [ 9 , 10 ], typically over the course of several scans [ 11 , 12 , 8 , 13 - 15 ]. For complex or heterogeneous material applications, such as foams used for impact protection [ 16 - 18 ], micro-structural responses to strain are important to modeling the material performance [ 19 , 20 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Unintended consequences: Assessing thermo-mechanical changes in vinyl nitrile foam due to micro-computed X-ray tomographic imaging

Landauer,

Tsinas,

Kafka

et al. 2023

Materials & Design

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Sensitivity Study Using Synthetic 3D Image Datasets to Investigate the Effect of Noise Artefacts on Digital Volume Correlation

Paraskevoulakos,

Ghosh,

Andriollo

et al. 2024

Exp Mech

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Background The potential effect of image noise artefacts on Digital Volume Correlation (DVC) analysis has not been thoroughly studied and, more particularly quantified, even though DVC is an emerging technique widely used in life and material science over the last decade. Objective This paper presents the results of a sensitivity study to shed light on the effect of various noise artefacts on the full-field kinematic fields generated by DVC, both in zero and rigid body motion. Methods Various noise artefacts were studied, including the Gaussian, Salt & Pepper, Speckle noise and embedded Ring Artefacts. A noise-free synthetic microstructure was generated using Discrete Element Modelling (DEM), representing an idealistic case, and acting as the reference dataset for the DVC analysis. Noise artefacts of various intensities (including selected extreme cases) were added to the reference image datasets using MATLAB (R2022) to form the outline of the parametric study. DVC analyses were subsequently conducted employing AVIZO (Thermo Fisher). A subset-based local approach was adopted. A three-dimensional version of the Structural Similarity Index Measure (SSIM) was used to define the similarity between the compared image datasets on each occasion. Sub-pixel rigid body motion was applied on the DEM-generated microstructure and subsequently “poisoned” with noise artefacts to evaluate mean bias and random error of the DVC analysis. Results When the local approach is implemented, the sensitivity study on zero motion data revealed the insignificant effect of the Gaussian, Salt & Pepper, and Speckle noise on the DVC-computed kinematic field. Therefore, the presence of such noise artefacts can be neglected when DVC is executed. On the contrary, Ring Artefacts can pose a considerable challenge and therefore, DVC results need to be evaluated cautiously. A linear relationship between SSIM and the correlation index is observed for the same noise artefacts. Gaussian noise has a pronounced effect on the mean bias error for sub-pixel rigid body motion. Conclusions Generating synthetic image datasets using DEM enabled the investigation of a variety of noise artefacts that potentially affect a DVC analysis. Given that, any microstructure – resembling the material studied – can be simulated and used for a DVC sensitivity analysis, supporting the user in appropriately evaluating the computed kinematic field. Even though the study is conducted for a two-phase material, the method elaborated in this paper also applies to heterogeneous multi-phase materials also. The conclusions drawn are valid within the environment of the AVIZO DVC extension module. Alternative DVC algorithms, utilising different approaches for the cross-correlation and the sub-pixel interpolation methods, need to be investigated.

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Interlaboratory Study of Digital Volume Correlation Error Due to X-Ray Computed Tomography Equipment and Scan Parameters: an Update from the DVC Challenge

Cited by 15 publications

References 46 publications

Effect of X‐Ray Computed Tomography Imaging Parameters on Quantification of Petrophysical Properties

Effect of X‐Ray Computed Tomography Imaging Parameters on Quantification of Petrophysical Properties

Unintended consequences: Assessing thermo-mechanical changes in vinyl nitrile foam due to micro-computed X-ray tomographic imaging

Sensitivity Study Using Synthetic 3D Image Datasets to Investigate the Effect of Noise Artefacts on Digital Volume Correlation

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