Pub Date : 2025-12-30DOI: 10.1016/j.tmater.2025.100081
Giada Rizzo , Luca Brombal , Francesco Brun
Differential phase contrast (DPC) computed tomography (CT), such as Edge Illumination (EI) CT, traditionally incorporates phase integration into the tomographic reconstruction process using Hilbert filtering followed by backprojection. Although this approach is fast, effective, and parameter-free, it offers limited flexibility for noise handling and precludes the use of algebraic reconstruction methods. In this work, we reformulate the phase integration step as a deconvolution problem and propose a practical pipeline that preserves the quantitativeness of phase contrast data as well as enables the use of algebraic reconstruction techniques. We demonstrate and compare several deconvolution strategies on both simulated and experimental data. Qualitative and quantitative evaluations consistently highlight the advantages of the proposed approach.
{"title":"A flexible deconvolution-based reconstruction pipeline for edge illumination phase-contrast computed tomography","authors":"Giada Rizzo , Luca Brombal , Francesco Brun","doi":"10.1016/j.tmater.2025.100081","DOIUrl":"10.1016/j.tmater.2025.100081","url":null,"abstract":"<div><div>Differential phase contrast (DPC) computed tomography (CT), such as Edge Illumination (EI) CT, traditionally incorporates phase integration into the tomographic reconstruction process using Hilbert filtering followed by backprojection. Although this approach is fast, effective, and parameter-free, it offers limited flexibility for noise handling and precludes the use of algebraic reconstruction methods. In this work, we reformulate the phase integration step as a deconvolution problem and propose a practical pipeline that preserves the quantitativeness of phase contrast data as well as enables the use of algebraic reconstruction techniques. We demonstrate and compare several deconvolution strategies on both simulated and experimental data. Qualitative and quantitative evaluations consistently highlight the advantages of the proposed approach.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"10 ","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.tmater.2025.100080
Brian M. Patterson , Theresa E. Quintana , Lynne Goodwin , Cynthia Welch , Jack Brett , Estevan Sandoval , Michael McCann , Paul L. Barclay , Duan Z. Zhang , Zachary Thompson , Alex Arzoumanidis , Nghia Vo , Michael Drakopoulos
Cellular materials are ubiquitous in our modern society. They may be stochastic gas-blown foams (e.g., polyurethane), foamed starches (e.g., cereals), or, in this case, 3D printed microlattices. Failure in these materials is often driven by surface or sub-surface defects, which may be nucleated at a surface roughness, an interior void, or inclusion interfaces that may not be typically observable. Obfuscating our understanding further, bulk materials are known to exhibit strain-rate-dependent mechanical response, making a subsurface understanding of damage even more critical. For the first time, an in situ uniaxial mechanical loading stage that simultaneously rotates specimens up to 18 Hz was fielded at a synchrotron for 3D tomographic imaging. This capability opens a plethora of materials science opportunities to explore strain rate effects in materials and examining deformation, fracture, and delamination’s (in composites) for a complete 3D picture (movie) of material response. We demonstrate the deformation of 3D printed polymer lattice structures, of three different material types, at 0.25, 1.1, and 2.2 s−1 strain rates. We successfully imaged the 3D deformation of these materials and can directly compare the same printed structure to the three material types at three strain rates, all in 3D. Material point method simulations were applied to one of the materials to better understand the role of voids on the 3D printed structure’s performance.
{"title":"Exploring strain rate effects upon 3D materials using high speed in situ X-ray tomoscopy","authors":"Brian M. Patterson , Theresa E. Quintana , Lynne Goodwin , Cynthia Welch , Jack Brett , Estevan Sandoval , Michael McCann , Paul L. Barclay , Duan Z. Zhang , Zachary Thompson , Alex Arzoumanidis , Nghia Vo , Michael Drakopoulos","doi":"10.1016/j.tmater.2025.100080","DOIUrl":"10.1016/j.tmater.2025.100080","url":null,"abstract":"<div><div>Cellular materials are ubiquitous in our modern society. They may be stochastic gas-blown foams (e.g., polyurethane), foamed starches (e.g., cereals), or, in this case, 3D printed microlattices. Failure in these materials is often driven by surface or sub-surface defects, which may be nucleated at a surface roughness, an interior void, or inclusion interfaces <em>that may not be typically observable</em>. Obfuscating our understanding further, bulk materials are known to exhibit strain-rate-dependent mechanical response, making a subsurface understanding of damage even more critical. For the first time, an in situ uniaxial mechanical loading stage that simultaneously rotates specimens up to 18 Hz was fielded at a synchrotron for 3D tomographic imaging. This capability opens a plethora of materials science opportunities to explore strain rate effects in materials and examining deformation, fracture, and delamination’s (in composites) for a complete 3D picture (movie) of material response. We demonstrate the deformation of 3D printed polymer lattice structures, of three different material types, at 0.25, 1.1, and 2.2 s<sup>−1</sup> strain rates. We successfully imaged the 3D deformation of these materials and can directly compare the same printed structure to the three material types at three strain rates, all in 3D. Material point method simulations were applied to one of the materials to better understand the role of voids on the 3D printed structure’s performance.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100080"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.tmater.2025.100076
Nikola Draganic , Bryce R. Jolley , Andrew Townsend , Chen Yee , Daniel Sparkman , Gabriel Balensiefer , Michael Chapman , Michael D. Uchic
Advancements in Additive Manufacturing (AM) technology are driving the need for improved methods for porosity quantification, especially from nondestructive sensing methods such as x-ray computed tomography (XCT). Maximum gradient (MG) segmentation is one of the most common algorithms used in XCT analysis for defining boundaries between adjacent materials. We utilize a bi-modal characterization approach to examine the accuracy of 2D MG segmentation of machined holes as analogues of internal porosity, by comparing data generated from XCT reconstructions with higher resolution Optical Microscopy (OM) images. We observe a systematic and increasing bias in the measurement of XCT hole diameter with decreasing hole size. To explain a portion of the bias, we develop an analytic 2D Disk-Gaussian Convolution Model that considers the effect of image blurring on the determination of the MG, and show that MG segmentation undersizes convex features relative to the width of the point spread function. Furthermore, we implemented a custom workflow for computing the contrast-to-noise ratio (CNR) for holes, allowing for the characterization in visibility as a function of feature size. Finally, we present statistical tests to determine the impact of XCT beam-hardening on measurements in the interior of the part versus the edge.
{"title":"Quantitative analysis of 2D hole characterization using X-ray computed tomography and maximum gradient segmentation","authors":"Nikola Draganic , Bryce R. Jolley , Andrew Townsend , Chen Yee , Daniel Sparkman , Gabriel Balensiefer , Michael Chapman , Michael D. Uchic","doi":"10.1016/j.tmater.2025.100076","DOIUrl":"10.1016/j.tmater.2025.100076","url":null,"abstract":"<div><div>Advancements in Additive Manufacturing (AM) technology are driving the need for improved methods for porosity quantification, especially from nondestructive sensing methods such as x-ray computed tomography (XCT). Maximum gradient (MG) segmentation is one of the most common algorithms used in XCT analysis for defining boundaries between adjacent materials. We utilize a bi-modal characterization approach to examine the accuracy of 2D MG segmentation of machined holes as analogues of internal porosity, by comparing data generated from XCT reconstructions with higher resolution Optical Microscopy (OM) images. We observe a systematic and increasing bias in the measurement of XCT hole diameter with decreasing hole size. To explain a portion of the bias, we develop an analytic 2D Disk-Gaussian Convolution Model that considers the effect of image blurring on the determination of the MG, and show that MG segmentation undersizes convex features relative to the width of the point spread function. Furthermore, we implemented a custom workflow for computing the contrast-to-noise ratio (CNR) for holes, allowing for the characterization in visibility as a function of feature size. Finally, we present statistical tests to determine the impact of XCT beam-hardening on measurements in the interior of the part versus the edge.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.tmater.2025.100079
Mustapha Eddah, Henning Markötter, Björn Mieller, Martinus Putra Widjaja, Jörg Beckmann, Giovanni Bruno
Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-to-noise ratio (SNR). One particular example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35 % improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible. The paper further discusses methodological requirements, limitations, and potential extensions of the presented multi-energy SXCT merging technique.
{"title":"Multi-energy high dynamic range synchrotron X-ray computed tomography","authors":"Mustapha Eddah, Henning Markötter, Björn Mieller, Martinus Putra Widjaja, Jörg Beckmann, Giovanni Bruno","doi":"10.1016/j.tmater.2025.100079","DOIUrl":"10.1016/j.tmater.2025.100079","url":null,"abstract":"<div><div>Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-to-noise ratio (SNR). One particular example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35 % improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible. The paper further discusses methodological requirements, limitations, and potential extensions of the presented multi-energy SXCT merging technique.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.tmater.2025.100078
M.C. Crocco , F. Cognigni , A. Sanna , R. Filosa , S. Siprova , R.C. Barberi , R.G. Agostino , S. Wabnitz , A. D’Alessandro , S. Lebrun , M. Rossi , V. Formoso , R. Termine , A. Bravin , M. Ferraro
Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, X-ray computed tomography has recently emerged as a versatile non-destructive tool for mapping their refractive index variations in 3D. In this study, we present a multiscale characterization of standard optical fibers. We carry out an intercomparison of three tomography setups: classical computed microtomography, X-ray microscopy, and nanotomography. In each method, our analysis highlights the trade-offs between resolution, field of view, and segmentation efficiency. Additionally, we integrate deep learning segmentation thresholding to improve the image analysis process. Thanks to its large field of view (10 × 10 mm2), microtomography with classical sources is ideal for the analysis of relatively long fiber spans, where a low spatial resolution is acceptable. The other way around, nanotomography has the highest spatial resolution (50–150 nm), but it is limited to very small fiber samples, e.g., fiber tapers and nanofibers, which have diameters of the order of a few microns. Finally, X-ray microscopy provides a good compromise between the sample size (of the order of 1 mm) fitting the device’s field of view and the spatial resolution needed for properly imaging the inner features of the fiber (about ). Specifically, thanks to its practicality in terms of costs and cumbersomeness, we foresee that the latter will provide the most suitable choice for the quality control of fiber drawing in real-time, e.g., using the ”One-Minute Tomographies with Fast Acquisition Scanning Technology” developed by Zeiss. In this regard, the combination of X-ray computed tomography and artificial intelligence-driven enhancements is poised to revolutionize fiber characterization, by enabling precise monitoring and adaptive control in fiber manufacturing (such as fiber size and non-circularity).
{"title":"Multiscale X-ray computed tomography of standard optical fibers","authors":"M.C. Crocco , F. Cognigni , A. Sanna , R. Filosa , S. Siprova , R.C. Barberi , R.G. Agostino , S. Wabnitz , A. D’Alessandro , S. Lebrun , M. Rossi , V. Formoso , R. Termine , A. Bravin , M. Ferraro","doi":"10.1016/j.tmater.2025.100078","DOIUrl":"10.1016/j.tmater.2025.100078","url":null,"abstract":"<div><div>Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, X-ray computed tomography has recently emerged as a versatile non-destructive tool for mapping their refractive index variations in 3D. In this study, we present a multiscale characterization of standard optical fibers. We carry out an intercomparison of three tomography setups: classical computed microtomography, X-ray microscopy, and nanotomography. In each method, our analysis highlights the trade-offs between resolution, field of view, and segmentation efficiency. Additionally, we integrate deep learning segmentation thresholding to improve the image analysis process. Thanks to its large field of view (10 × 10 mm<sup>2</sup>), microtomography with classical sources is ideal for the analysis of relatively long fiber spans, where a low spatial resolution is acceptable. The other way around, nanotomography has the highest spatial resolution (50–150 nm), but it is limited to very small fiber samples, e.g., fiber tapers and nanofibers, which have diameters of the order of a few microns. Finally, X-ray microscopy provides a good compromise between the sample size (of the order of 1 mm) fitting the device’s field of view and the spatial resolution needed for properly imaging the inner features of the fiber (about <span><math><mrow><mn>1</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>). Specifically, thanks to its practicality in terms of costs and cumbersomeness, we foresee that the latter will provide the most suitable choice for the quality control of fiber drawing in real-time, e.g., using the ”One-Minute Tomographies with Fast Acquisition Scanning Technology” developed by Zeiss. In this regard, the combination of X-ray computed tomography and artificial intelligence-driven enhancements is poised to revolutionize fiber characterization, by enabling precise monitoring and adaptive control in fiber manufacturing (such as fiber size and non-circularity).</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.tmater.2025.100077
Stephen E. Catsamas, Glenn R. Myers, Andrew M. Kingston
A method for general 4D-computed tomography is introduced via a first-principles consideration of small dynamics partitioned according to the Lagrangian description. The model partitions the dynamics into either kinematics (motion) or non-kinematics under the assumption that the kinematic dynamics are maximal, i.e., kinematics are preferred in any ambiguity of partitioning the dynamics between kinematics and non-kinematics. Alongside dynamic reconstruction, this results in a highly physically interpretable and consistent dynamics model.
Parametrisation of the model is achieved via deformation vector fields for the kinematics and sparse arrays named ‘patches’ for the non-kinematics. A method to estimate the dynamics model from a time series of volumes is proposed and employed to analyse a series of phantoms and experimental datasets. Results reveal that the dynamics model can faithfully capture both kinematic and non-kinematic dynamics in both simulated and experimental systems while requiring vastly fewer parameters than a time-series approach.
Tomography using this dynamics model is developed via algebraic reconstruction techniques with modified projection operators and projection data. This is tested both with dynamics known a priori and dynamics estimated from projection data via reconstructed volumes. The results show improvements to tomogram quality, both using best-case static reconstruction and deformation-vector-field-only reconstruction, particularly in reducing motion blur and capturing dynamical features.
{"title":"Physically interpretable dynamic tomography via a first-principles model","authors":"Stephen E. Catsamas, Glenn R. Myers, Andrew M. Kingston","doi":"10.1016/j.tmater.2025.100077","DOIUrl":"10.1016/j.tmater.2025.100077","url":null,"abstract":"<div><div>A method for general 4D-computed tomography is introduced via a first-principles consideration of small dynamics partitioned according to the Lagrangian description. The model partitions the dynamics into either kinematics (motion) or non-kinematics under the assumption that the kinematic dynamics are maximal, i.e., kinematics are preferred in any ambiguity of partitioning the dynamics between kinematics and non-kinematics. Alongside dynamic reconstruction, this results in a highly physically interpretable and consistent dynamics model.</div><div>Parametrisation of the model is achieved via deformation vector fields for the kinematics and sparse arrays named ‘patches’ for the non-kinematics. A method to estimate the dynamics model from a time series of volumes is proposed and employed to analyse a series of phantoms and experimental datasets. Results reveal that the dynamics model can faithfully capture both kinematic and non-kinematic dynamics in both simulated and experimental systems while requiring vastly fewer parameters than a time-series approach.</div><div>Tomography using this dynamics model is developed via algebraic reconstruction techniques with modified projection operators and projection data. This is tested both with dynamics known <em>a priori</em> and dynamics estimated from projection data via reconstructed volumes. The results show improvements to tomogram quality, both using best-case static reconstruction and deformation-vector-field-only reconstruction, particularly in reducing motion blur and capturing dynamical features.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100077"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-29DOI: 10.1016/j.tmater.2025.100075
Austin Yunker , Peter Kenesei , Hemant Sharma , Jun-Sang Park , Antonino Miceli , Rajkumar Kettimuthu
Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.
{"title":"Boosting Noise2Inverse via Enhanced Model Selection for Denoising Computed Tomography Data","authors":"Austin Yunker , Peter Kenesei , Hemant Sharma , Jun-Sang Park , Antonino Miceli , Rajkumar Kettimuthu","doi":"10.1016/j.tmater.2025.100075","DOIUrl":"10.1016/j.tmater.2025.100075","url":null,"abstract":"<div><div>Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-26DOI: 10.1016/j.tmater.2025.100072
Bogong Wang , Andrew M. Kingston , Philipp D. Lösel , Warren Creemers
3D imaging of granular packings and geological particle samples by computed tomography offers the means for non-destructive analysis. However, obtaining such tomograms with the corresponding segmentation labels, i.e. a unique label per particle, remains a significant challenge. This study introduces a novel physics-based simulation workflow that generates synthetic tomograms with corresponding ground truth segmentations. The synthetic dataset generation tool produces realistic particle pack tomograms in large quantities, supporting data augmentation and serving as a benchmark for geological tomographic segmentation testing. The code in this study is publicly available at: github.com/bogongwang/particle-pack-generation.
{"title":"Synthetic particle pack generation for augmentation and testing in geological tomographic segmentation","authors":"Bogong Wang , Andrew M. Kingston , Philipp D. Lösel , Warren Creemers","doi":"10.1016/j.tmater.2025.100072","DOIUrl":"10.1016/j.tmater.2025.100072","url":null,"abstract":"<div><div>3D imaging of granular packings and geological particle samples by computed tomography offers the means for non-destructive analysis. However, obtaining such tomograms with the corresponding segmentation labels, i.e. a unique label per particle, remains a significant challenge. This study introduces a novel physics-based simulation workflow that generates synthetic tomograms with corresponding ground truth segmentations. The synthetic dataset generation tool produces realistic particle pack tomograms in large quantities, supporting data augmentation and serving as a benchmark for geological tomographic segmentation testing. The code in this study is publicly available at: <span><span>github.com/bogongwang/particle-pack-generation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-24DOI: 10.1016/j.tmater.2025.100074
David Haberthür , Oleksiy-Zakhar Khoma , Tim Hoessly , Eugenio Zoni , Marianna Kruithof-de Julio , Stewart D. Ryan , Myriam Grunewald , Benjamin Bellón , Rebecca Sandgren , Stephan Handschuh , Benjamin E. Pippenger , Dieter Bosshardt , Valentin Djonov , Ruslan Hlushchuk
Angiogenesis is essential for skeletal development, bone healing, and regeneration. Improved non-destructive, three-dimensional (3D) imaging of the vasculature within bone tissue benefits many research areas, especially implantology and tissue engineering. X-ray microcomputed tomography (microCT) is a well-suited non-destructive 3D imaging technique for bone morphology. For microCT-based detection of vessels, it is paramount to use contrast enhancement. Limited differences in radiopacity between perfusion agents and mineralized bone make their distinct segmentation problematic and have been a major drawback of this approach. A decalcification step resolves this issue but inhibits the simultaneous assessment of bone microstructure and vascular morphology. The problem of contrasting becomes further complicated in samples with metal implants. This study describes contrast-enhanced microCT-based visualization of vasculature within bone tissue in small and large animal models, also in the vicinity of the metal implants. We present simultaneous microvascular and bone imaging in murine tibia, a murine bone metastatic model, the pulp chamber, gingiva, and periodontal ligaments. In a large animal model (minipig), we performed visualization and segmentation of different tissue types and vessels in the hemimandible containing metal implants. We further demonstrate the potential of dual-energy imaging in distinguishing bone tissue from the applied contrast agents. This work introduces a non-destructive approach for 3D imaging of vasculature within soft and hard tissues near metal implants in a large animal model.
{"title":"MicroCT-based vascular imaging in bone and peri-implant tissues","authors":"David Haberthür , Oleksiy-Zakhar Khoma , Tim Hoessly , Eugenio Zoni , Marianna Kruithof-de Julio , Stewart D. Ryan , Myriam Grunewald , Benjamin Bellón , Rebecca Sandgren , Stephan Handschuh , Benjamin E. Pippenger , Dieter Bosshardt , Valentin Djonov , Ruslan Hlushchuk","doi":"10.1016/j.tmater.2025.100074","DOIUrl":"10.1016/j.tmater.2025.100074","url":null,"abstract":"<div><div>Angiogenesis is essential for skeletal development, bone healing, and regeneration. Improved non-destructive, three-dimensional (3D) imaging of the vasculature within bone tissue benefits many research areas, especially implantology and tissue engineering. X-ray microcomputed tomography (microCT) is a well-suited non-destructive 3D imaging technique for bone morphology. For microCT-based detection of vessels, it is paramount to use contrast enhancement. Limited differences in radiopacity between perfusion agents and mineralized bone make their distinct segmentation problematic and have been a major drawback of this approach. A decalcification step resolves this issue but inhibits the simultaneous assessment of bone microstructure and vascular morphology. The problem of contrasting becomes further complicated in samples with metal implants. This study describes contrast-enhanced microCT-based visualization of vasculature within bone tissue in small and large animal models, also in the vicinity of the metal implants. We present simultaneous microvascular and bone imaging in murine tibia, a murine bone metastatic model, the pulp chamber, gingiva, and periodontal ligaments. In a large animal model (minipig), we performed visualization and segmentation of different tissue types and vessels in the hemimandible containing metal implants. We further demonstrate the potential of dual-energy imaging in distinguishing bone tissue from the applied contrast agents. This work introduces a non-destructive approach for 3D imaging of vasculature within soft and hard tissues near metal implants in a large animal model.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-23DOI: 10.1016/j.tmater.2025.100073
Joe Stickland , Laurenz Schröer , Florian Buyse , Alexandra Guedes , Håvard Haugen , Ragnvald Mathiesen , Dag W. Breiby , Veerle Cnudde , Basab Chattopadhyay
Geomaterials form the basis of our planet. With structural features spanning from the nanometre- to the continental-scale, geomaterials possess a complex but fascinating hierarchical structure that allows us to investigate their formation’s associated physical, chemical, and biological processes. Geomaterials provide us insights into the formation and evolution of the Earth as well as the origin of life as preserved in fossilised remains of microorganisms. Microscopy is perhaps the most powerful tool that helps us to appreciate and understand geomaterials. With rapid advances in experimental science during the last several decades, we can now image internal structures and follow internal dynamic processes in real-time in three dimensions (3D). A wide range of current 3D imaging methodologies have emerged that help us understand and observe geomaterials’ relevant structural features. Attenuation-based 3D X-ray tomography is the most used micro-scale technique, which can be paired with complementary techniques to highlight more features and details within geomaterials. This review documents the relevant complementary microscopy modalities: phase contrast and diffraction contrast X-ray tomography, neutron tomography and electron tomography, and other methods like atom probe tomography and chemical- and structural-specific Raman imaging. This review article aims to provide an overview of a wide range of microscopy methodologies (for researchers) and the insight that can be garnered from their use with geomaterials.
{"title":"Advanced microscopy probes for geomaterials – Current state of the art and future perspectives","authors":"Joe Stickland , Laurenz Schröer , Florian Buyse , Alexandra Guedes , Håvard Haugen , Ragnvald Mathiesen , Dag W. Breiby , Veerle Cnudde , Basab Chattopadhyay","doi":"10.1016/j.tmater.2025.100073","DOIUrl":"10.1016/j.tmater.2025.100073","url":null,"abstract":"<div><div>Geomaterials form the basis of our planet. With structural features spanning from the nanometre- to the continental-scale, geomaterials possess a complex but fascinating hierarchical structure that allows us to investigate their formation’s associated physical, chemical, and biological processes. Geomaterials provide us insights into the formation and evolution of the Earth as well as the origin of life as preserved in fossilised remains of microorganisms. Microscopy is perhaps the most powerful tool that helps us to appreciate and understand geomaterials. With rapid advances in experimental science during the last several decades, we can now image internal structures and follow internal dynamic processes in real-time in three dimensions (3D). A wide range of current 3D imaging methodologies have emerged that help us understand and observe geomaterials’ relevant structural features. Attenuation-based 3D X-ray tomography is the most used micro-scale technique, which can be paired with complementary techniques to highlight more features and details within geomaterials. This review documents the relevant complementary microscopy modalities: phase contrast and diffraction contrast X-ray tomography, neutron tomography and electron tomography, and other methods like atom probe tomography and chemical- and structural-specific Raman imaging. This review article aims to provide an overview of a wide range of microscopy methodologies (for researchers) and the insight that can be garnered from their use with geomaterials.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"9 ","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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