Pub Date : 2025-04-03DOI: 10.1016/j.tmater.2025.100062
Lara Mazy , Greet Kerckhofs
Biological tissues undergo physiological mechanical loading during their functioning in vivo. To properly respond to these mechanical signals, tissues have a highly complex microstructural organization. However, there is not yet sufficient knowledge about the link between their microstructural organization and their mechanical behaviour. Therefore, there is a need for methods to dynamically assess how the microstructure of biological tissues changes during mechanical loading. 4D-µCT is an imaging technique combining mechanical testing with X-ray microfocus computed tomography (µCT) imaging. It has been extensively used to visualize, at the micro-scale and in full 3D, the deformation of the microstructure of non-biological materials during mechanical loading. Additionally, postprocessing of the 4D-µCT datasets allowed 3D strain field calculations. This review aims to provide an overview of the current state of the art of the use of 4D-µCT specifically for the assessment of the mechanical behavior of biological tissue, and this both for mineralized and unmineralized tissues. We highlighted the advancements as well as the current limitations and challenges to overcome, such as the need for complex loading modes, the effect of X-rays on the mechanical behavior and the need to keep the samples hydrated during testing. We finally conclude with some future perspectives.
生物组织在体内运作时会承受生理机械负荷。为了正确响应这些机械信号，组织具有高度复杂的微结构组织。然而，人们对其微观结构组织与其机械行为之间的联系还缺乏足够的了解。因此，需要采用一些方法来动态评估生物组织的微观结构在机械加载过程中是如何变化的。4D-µCT 是一种将机械测试与 X 射线微焦计算机断层扫描（µCT）成像相结合的成像技术。它已被广泛用于在微观尺度上以全三维方式观察非生物材料在机械加载过程中微观结构的变形。此外，通过对 4D-µCT 数据集进行后处理，还可进行三维应变场计算。本综述旨在概述目前使用 4D-µCT 评估生物组织机械行为的最新技术，包括矿化组织和非矿化组织。我们重点介绍了所取得的进展以及目前需要克服的局限性和挑战，如需要复杂的加载模式、X 射线对力学行为的影响以及在测试过程中保持样本水合状态的必要性。最后，我们对未来进行了展望。
{"title":"A review of in-situ mechanical testing combined with X-ray microfocus computed tomography: Application and current challenges for biological tissues","authors":"Lara Mazy , Greet Kerckhofs","doi":"10.1016/j.tmater.2025.100062","DOIUrl":"10.1016/j.tmater.2025.100062","url":null,"abstract":"<div><div>Biological tissues undergo physiological mechanical loading during their functioning <em>in vivo</em>. To properly respond to these mechanical signals, tissues have a highly complex microstructural organization. However, there is not yet sufficient knowledge about the link between their microstructural organization and their mechanical behaviour. Therefore, there is a need for methods to dynamically assess how the microstructure of biological tissues changes during mechanical loading. 4D-µCT is an imaging technique combining mechanical testing with X-ray microfocus computed tomography (µCT) imaging. It has been extensively used to visualize, at the micro-scale and in full 3D, the deformation of the microstructure of non-biological materials during mechanical loading. Additionally, postprocessing of the 4D-µCT datasets allowed 3D strain field calculations. This review aims to provide an overview of the current state of the art of the use of 4D-µCT specifically for the assessment of the mechanical behavior of biological tissue, and this both for mineralized and unmineralized tissues. We highlighted the advancements as well as the current limitations and challenges to overcome, such as the need for complex loading modes, the effect of X-rays on the mechanical behavior and the need to keep the samples hydrated during testing. We finally conclude with some future perspectives.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"8 ","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820723","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 : 2025-03-12DOI: 10.1016/j.tmater.2025.100060
Pedro Damas Resende , Damien Texier , Julien Genée , Malo Jullien , Henry Proudhon , Julien Réthoré , Didier Bardel , Wolfgang Ludwig
Microplasticity of a polycrystalline Ni-based superalloy was investigated using phase contrast tomography (PCT) and laser scanning confocal microscopy (LSCM). Incremental tensile testing was performed on three miniaturized specimens to investigate strain localization at low plastic deformation at room temperature and 650 ∘C. Microplasticity events, such as slip activity, deformation twinning, and grain boundary sliding, are free to emerge at the specimen surface and generate sub-micrometer topographic features. High resolution digital image correlation was conducted using LSCM to have a description of the in-plane and out-of-plane kinematics of the specimen surface. Despite slip amplitudes substantially smaller than the voxel size, PCT was capable to evidence the out-of-plane component of slip traces at the onset of plasticity. The technique was also used at 650 ∘C, a temperature at which grain boundary sliding occurs, but surface reactivity is severe enough not to allow for topographic measurements using LSCM. Therefore, PCT was found particularly adapted to evidence “surface” microplasticity events hidden by an extra surface oxidation layer.
{"title":"Slip localization and grain boundary sliding analysis at sub-voxel resolution using phase contrast tomography","authors":"Pedro Damas Resende , Damien Texier , Julien Genée , Malo Jullien , Henry Proudhon , Julien Réthoré , Didier Bardel , Wolfgang Ludwig","doi":"10.1016/j.tmater.2025.100060","DOIUrl":"10.1016/j.tmater.2025.100060","url":null,"abstract":"<div><div>Microplasticity of a polycrystalline Ni-based superalloy was investigated using phase contrast tomography (PCT) and laser scanning confocal microscopy (LSCM). Incremental tensile testing was performed on three miniaturized specimens to investigate strain localization at low plastic deformation at room temperature and 650 <sup>∘</sup>C. Microplasticity events, such as slip activity, deformation twinning, and grain boundary sliding, are free to emerge at the specimen surface and generate sub-micrometer topographic features. High resolution digital image correlation was conducted using LSCM to have a description of the in-plane and out-of-plane kinematics of the specimen surface. Despite slip amplitudes substantially smaller than the voxel size, PCT was capable to evidence the out-of-plane component of slip traces at the onset of plasticity. The technique was also used at 650 <sup>∘</sup>C, a temperature at which grain boundary sliding occurs, but surface reactivity is severe enough not to allow for topographic measurements using LSCM. Therefore, PCT was found particularly adapted to evidence “surface” microplasticity events hidden by an extra surface oxidation layer.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"8 ","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-03DOI: 10.1016/j.tmater.2025.100057
Iwan T. Mitchell , Jean Michel Létang , Llion Marc Evans , Franck P. Vidal
Scan planning for X-ray CT systems can be difficult due to the large number of elements affecting scan quality. The use of X-ray simulation can answer feasibility questions, however existing methods are focused on experts who are familiar with XCT and programming knowledge. WebCT is a user-centric application for performing virtual XCT scans with the validated X-ray simulator gVirtualXray. Focused on accessibility, the interface allows changing all scanning parameters; from tube characteristics to detector energy response, while allowing full-scale simulation and reconstruction in minutes. WebCT is available as a free, open-source application, giving full control over a virtual lab-CT or synchrotron system. Configurations can be saved, shared, or even imported from many popular XCT dataset formats. We demonstrate in this paper the use of WebCT as a scan planning tool, using a simple CAD mockup to select filtration based on transmission before scanning.
{"title":"WebCT – OpenSource web-based GUI for real-time X-ray simulation","authors":"Iwan T. Mitchell , Jean Michel Létang , Llion Marc Evans , Franck P. Vidal","doi":"10.1016/j.tmater.2025.100057","DOIUrl":"10.1016/j.tmater.2025.100057","url":null,"abstract":"<div><div>Scan planning for X-ray CT systems can be difficult due to the large number of elements affecting scan quality. The use of X-ray simulation can answer feasibility questions, however existing methods are focused on experts who are familiar with XCT and programming knowledge. WebCT is a user-centric application for performing virtual XCT scans with the validated X-ray simulator gVirtualXray. Focused on accessibility, the interface allows changing all scanning parameters; from tube characteristics to detector energy response, while allowing full-scale simulation and reconstruction in minutes. WebCT is available as a free, open-source application, giving full control over a virtual lab-CT or synchrotron system. Configurations can be saved, shared, or even imported from many popular XCT dataset formats. We demonstrate in this paper the use of WebCT as a scan planning tool, using a simple CAD mockup to select filtration based on transmission before scanning.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"8 ","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.tmater.2025.100058
Maximilian Linde , Wolfram Wiest , Anna Trauth , Markus G.R. Sause
The advent of robot-based computed tomography systems accelerated the development of trajectory optimization methodologies, with the objective of achieving superior image quality compared to standard trajectories while maintaining the same or even fewer number of required projections. The application of standard trajectories is not only inefficient due to the lack of integration of available prior knowledge about the object under investigation but also suboptimal because of limited accessibility issues during scans of large components, which are common in robot-based computed tomography. In this work, we introduce an object-specific trajectory optimization technique for few-view applications, based on a 3D Radon space analysis using a RANSAC algorithm. In contrast to existing methods, this approach allows for object geometry specific projection views, which are no longer constrained by discretized initial view sets on predefined acquisition geometries. In addition to eliminating the effects of discretized initial sets, this technique offers a distinct advantage in scenarios of limited accessibility by enabling the avoidance of collision elements, unlike trajectory optimizations on predefined acquisition geometries and standard trajectories. Our results show that the presented technology outperforms standard trajectories of evenly distributed projection views on predefined geometries in both ideal accessibility and limited accessibility scenarios. According to the employed geometry-based image quality metrics, our approach allows for reductions of more than 50 % in the number of projection views while maintaining equivalent image quality.
{"title":"Trajectory optimization for few-view robot-based CT: Transitioning from static to object-specific acquisition geometries","authors":"Maximilian Linde , Wolfram Wiest , Anna Trauth , Markus G.R. Sause","doi":"10.1016/j.tmater.2025.100058","DOIUrl":"10.1016/j.tmater.2025.100058","url":null,"abstract":"<div><div>The advent of robot-based computed tomography systems accelerated the development of trajectory optimization methodologies, with the objective of achieving superior image quality compared to standard trajectories while maintaining the same or even fewer number of required projections. The application of standard trajectories is not only inefficient due to the lack of integration of available prior knowledge about the object under investigation but also suboptimal because of limited accessibility issues during scans of large components, which are common in robot-based computed tomography. In this work, we introduce an object-specific trajectory optimization technique for few-view applications, based on a 3D Radon space analysis using a <em>RANSAC</em> algorithm. In contrast to existing methods, this approach allows for object geometry specific projection views, which are no longer constrained by discretized initial view sets on predefined acquisition geometries. In addition to eliminating the effects of discretized initial sets, this technique offers a distinct advantage in scenarios of limited accessibility by enabling the avoidance of collision elements, unlike trajectory optimizations on predefined acquisition geometries and standard trajectories. Our results show that the presented technology outperforms standard trajectories of evenly distributed projection views on predefined geometries in both ideal accessibility and limited accessibility scenarios. According to the employed geometry-based image quality metrics, our approach allows for reductions of more than 50 % in the number of projection views while maintaining equivalent image quality.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1016/j.tmater.2025.100059
Florian Buyse , Matthieu N. Boone , Frederic Van Assche , Stéphane Faucher , Peter Moonen , Stijn Dewaele , Veerle Cnudde
Differentiating minerals using high-resolution X-ray tomography (µCT) relies on distinct differences in the attenuation coefficient µ. The µ value depends on an interplay between the material density ρ and the effective atomic number Zeff of a mineral phase. Difficulties in identifying mineral phases arise when this interplay gives similar µ values and thus limited contrast within µCT images. Untangling these two dependencies is essential to improve the three-dimensional chemical identification of critical minerals. Lab-based methods and techniques often incorporate different measures, but only show a limited application potential on multiphase geological samples. Using high-Z spectral laboratory-based µCT we studied the potential of directly identifying chemical elements within the practical margins of high-Z spectral detectors. This paper compares the results from three mineral deposits using two spectral µCT setups. Chemical elements with a Z higher than molybdenum and a concentration of at least some weight percentage were correctly identified using K-edge imaging. The suitability of the different high-Z spectral detectors depends largely on the availability of prior knowledge of the sample composition. Quantifying elemental concentrations is element- and sample specific and currently does not allow for optimal automated mineralogy solutions. Improving the three-dimensional identification of minerals can be achieved with dedicated analyses of the energy-dependent µ curve and therefore will remain the focus of future work.
{"title":"Spectral X-ray computed tomography for the chemical identification of critical minerals","authors":"Florian Buyse , Matthieu N. Boone , Frederic Van Assche , Stéphane Faucher , Peter Moonen , Stijn Dewaele , Veerle Cnudde","doi":"10.1016/j.tmater.2025.100059","DOIUrl":"10.1016/j.tmater.2025.100059","url":null,"abstract":"<div><div>Differentiating minerals using high-resolution X-ray tomography (µCT) relies on distinct differences in the attenuation coefficient <em>µ</em>. The <em>µ</em> value depends on an interplay between the material density <em>ρ</em> and the effective atomic number <em>Z</em><sub><em>eff</em></sub> of a mineral phase. Difficulties in identifying mineral phases arise when this interplay gives similar <em>µ</em> values and thus limited contrast within µCT images. Untangling these two dependencies is essential to improve the three-dimensional chemical identification of critical minerals. Lab-based methods and techniques often incorporate different measures, but only show a limited application potential on multiphase geological samples. Using high-<em>Z</em> spectral laboratory-based µCT we studied the potential of directly identifying chemical elements within the practical margins of high-<em>Z</em> spectral detectors. This paper compares the results from three mineral deposits using two spectral µCT setups. Chemical elements with a <em>Z</em> higher than molybdenum and a concentration of at least some weight percentage were correctly identified using K-edge imaging. The suitability of the different high-<em>Z</em> spectral detectors depends largely on the availability of prior knowledge of the sample composition. Quantifying elemental concentrations is element- and sample specific and currently does not allow for optimal automated mineralogy solutions. Improving the three-dimensional identification of minerals can be achieved with dedicated analyses of the energy-dependent <em>µ</em> curve and therefore will remain the focus of future work.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"8 ","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article highlights the use of synchrotron X-ray computed tomography (SXCT) in examining the production technology of two faience beads dating to 3000 BCE (5000 BP). Through one blue and one green colored sample, we discuss the competence of the ID10-BEATS beamline at SESAME (Jordan) for non-invasive analysis of archaeological objects. We present different protocols for the examination of silica-based objects with sub-cm size using SXCT. The results validate the cementation technique for the production of tiny beads (≤ 1 cm). The application of high-resolution 3D imaging, in combination with X-ray phase-contrast enhancement, allows for the non-invasive characterization of faience production, which opens a venue for broader discussions on ancient technology and technological knowledge transfer among ancient communities in Southwest Asia.
{"title":"Synchrotron computed tomography of 5000 years old faience beads from Southeastern Anatolia (Türkiye)","authors":"Gonca Dardeniz , Gülistan Büyükgedik , Onur Kaya , Suat Özkorucuklu , Fareeha Hameed , Gianluca Iori","doi":"10.1016/j.tmater.2025.100056","DOIUrl":"10.1016/j.tmater.2025.100056","url":null,"abstract":"<div><div>This article highlights the use of synchrotron X-ray computed tomography (SXCT) in examining the production technology of two faience beads dating to 3000 BCE (5000 BP). Through one blue and one green colored sample, we discuss the competence of the ID10-BEATS beamline at SESAME (Jordan) for non-invasive analysis of archaeological objects. We present different protocols for the examination of silica-based objects with sub-cm size using SXCT. The results validate the cementation technique for the production of tiny beads (≤ 1 cm). The application of high-resolution 3D imaging, in combination with X-ray phase-contrast enhancement, allows for the non-invasive characterization of faience production, which opens a venue for broader discussions on ancient technology and technological knowledge transfer among ancient communities in Southwest Asia.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.tmater.2025.100055
Jukka Kuva , Mohammad Jooshaki , Ester M. Jolis , Juuso Sammaljärvi , Marja Siitari-Kauppi , Filip Jankovský , Milan Zuna , Alan Bischoff , Paul Sardini
Investigating the heterogeneous transport properties of rock is vital for accurate assessment of radionuclide migration, which is essential for the safety assessment of a nuclear waste disposal facility. Previous studies have combined x-ray computed tomography (XCT) with other methods to obtain three-dimensional (3D) mineral and porosity maps, but such approaches are time consuming and somewhat dependent on the operator. To address these limitations, we have developed a deep learning-based method that combines XCT with fast and modern characterization techniques such as scanning micro x-ray fluorescence (μXRF) and carbon 14 polymethylmethacrylate (PMMA) autoradiography. This innovative approach produces 3D mineral and porosity maps with minimal operator dependency and manual work. The results obtained from our analysis of various rock samples demonstrate the method’s suitability for transport simulation studies in various geological settings.
{"title":"Characterizing heterogeneous rocks in 3D with a multimodal deep learning approach – Implications for transport simulations","authors":"Jukka Kuva , Mohammad Jooshaki , Ester M. Jolis , Juuso Sammaljärvi , Marja Siitari-Kauppi , Filip Jankovský , Milan Zuna , Alan Bischoff , Paul Sardini","doi":"10.1016/j.tmater.2025.100055","DOIUrl":"10.1016/j.tmater.2025.100055","url":null,"abstract":"<div><div>Investigating the heterogeneous transport properties of rock is vital for accurate assessment of radionuclide migration, which is essential for the safety assessment of a nuclear waste disposal facility. Previous studies have combined x-ray computed tomography (XCT) with other methods to obtain three-dimensional (3D) mineral and porosity maps, but such approaches are time consuming and somewhat dependent on the operator. To address these limitations, we have developed a deep learning-based method that combines XCT with fast and modern characterization techniques such as scanning micro x-ray fluorescence (μXRF) and carbon 14 polymethylmethacrylate (PMMA) autoradiography. This innovative approach produces 3D mineral and porosity maps with minimal operator dependency and manual work. The results obtained from our analysis of various rock samples demonstrate the method’s suitability for transport simulation studies in various geological settings.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100055"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-05DOI: 10.1016/j.tmater.2025.100054
Marek Zemek , Pavel Blažek , Jakub Šalplachta , Tomáš Zikmund , Michal Petřík , Robert H. Schmitt , Jozef Kaiser
Advances in micro-manufacturing and materials science create a demand for dimensional measurements using computed tomography with sub-micrometer resolution (submicron CT). Correction of the scale of CT data is essential for this task, but existing tools, which are used in CT modalities with lower resolutions, are often not suitable for submicron CT. The following study adapts scale correction to submicron CT using a miniature reference object with two ruby balls, which fits into a field of view with a sub-millimeter diameter and features a calibrated ball center-to-center distance of approximately 450 μm. CT data of the reference object were analyzed to determine a scale correction factor, which was applied to measurements of two additional reference objects of a similar scale and composition. The average bias of measurements for one of the objects was reduced from 3.35 μm to 0.26 μm, and the measurement uncertainty was lowered from 3.4 μm to 1.2 μm. Similar results were also achieved for the second object. The extended scan time of the reference object and the potential for sample drift, which are both typical for submicron CT, were mitigated by angular undersampling. Finally, a complementary scale correction approach is demonstrated using projection data of the reference object. This approach avoids tomographic artifacts caused by very radio-opaque objects, and it is practical for applications that utilize lower-energy X-rays.
{"title":"Scale correction in submicron computed tomography with a submillimeter field of view","authors":"Marek Zemek , Pavel Blažek , Jakub Šalplachta , Tomáš Zikmund , Michal Petřík , Robert H. Schmitt , Jozef Kaiser","doi":"10.1016/j.tmater.2025.100054","DOIUrl":"10.1016/j.tmater.2025.100054","url":null,"abstract":"<div><div>Advances in micro-manufacturing and materials science create a demand for dimensional measurements using computed tomography with sub-micrometer resolution (submicron CT). Correction of the scale of CT data is essential for this task, but existing tools, which are used in CT modalities with lower resolutions, are often not suitable for submicron CT. The following study adapts scale correction to submicron CT using a miniature reference object with two ruby balls, which fits into a field of view with a sub-millimeter diameter and features a calibrated ball center-to-center distance of approximately 450 μm. CT data of the reference object were analyzed to determine a scale correction factor, which was applied to measurements of two additional reference objects of a similar scale and composition. The average bias of measurements for one of the objects was reduced from 3.35 μm to 0.26 μm, and the measurement uncertainty was lowered from 3.4 μm to 1.2 μm. Similar results were also achieved for the second object. The extended scan time of the reference object and the potential for sample drift, which are both typical for submicron CT, were mitigated by angular undersampling. Finally, a complementary scale correction approach is demonstrated using projection data of the reference object. This approach avoids tomographic artifacts caused by very radio-opaque objects, and it is practical for applications that utilize lower-energy X-rays.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100054"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.tmater.2025.100053
Tebogo Ledwaba , Christine Steenkamp , Agnieszka Chmielewska-Wysocka , Bartlomiej Wysocki , Anton du Plessis
The use of X-ray computed tomography (XCT) has seen significant growth over a broad range of disciplines including biology, earth science, engineering, and many more. It is now increasingly used in additive manufacturing (AM) since its benefits are being appreciated more widely. This is due to the method being non-destructive and comprehensive, providing external and internal information of tested parts. Data processing and segmentation of XCT data is important to get as much information as possible so that a clear picture of features can be obtained and analyzed. Porosity analysis has been the most successful and widely used XCT analysis type in all fields so far, partly due to simple manual segmentation methods such as the Otsu global threshold. However, segmentation of small and narrow features such as cracks are challenging with conventional thresholding methods. Since automated conventional methods fail, manual segmentation is often used but this can be subjective, tedious, and prone to segmentation errors. The present work employs neural networks, specifically the U-Net architecture and thoroughly investigates possible solutions to a robust crack segmentation model. Intensity scale calibration, bias training weights and data augmentations were investigated in detail to find the best possible performance of trained models, when employed on new data. The results demonstrate the performance and improvement gained by each of the above factors, as well as the successful AI segmentation for various additively manufactured sample types with different cracks. This method enables clear visualization and presentation of cracks, as well as their quantification. The model strives toward a generic crack segmentation model for all AM parts that could be used directly by others. This generalizability of the model is discussed together with its limitations.
{"title":"Development of AI crack segmentation models for additive manufacturing","authors":"Tebogo Ledwaba , Christine Steenkamp , Agnieszka Chmielewska-Wysocka , Bartlomiej Wysocki , Anton du Plessis","doi":"10.1016/j.tmater.2025.100053","DOIUrl":"10.1016/j.tmater.2025.100053","url":null,"abstract":"<div><div>The use of X-ray computed tomography (XCT) has seen significant growth over a broad range of disciplines including biology, earth science, engineering, and many more. It is now increasingly used in additive manufacturing (AM) since its benefits are being appreciated more widely. This is due to the method being non-destructive and comprehensive, providing external and internal information of tested parts. Data processing and segmentation of XCT data is important to get as much information as possible so that a clear picture of features can be obtained and analyzed. Porosity analysis has been the most successful and widely used XCT analysis type in all fields so far, partly due to simple manual segmentation methods such as the Otsu global threshold. However, segmentation of small and narrow features such as cracks are challenging with conventional thresholding methods. Since automated conventional methods fail, manual segmentation is often used but this can be subjective, tedious, and prone to segmentation errors. The present work employs neural networks, specifically the U-Net architecture and thoroughly investigates possible solutions to a robust crack segmentation model. Intensity scale calibration, bias training weights and data augmentations were investigated in detail to find the best possible performance of trained models, when employed on new data. The results demonstrate the performance and improvement gained by each of the above factors, as well as the successful AI segmentation for various additively manufactured sample types with different cracks. This method enables clear visualization and presentation of cracks, as well as their quantification. The model strives toward a generic crack segmentation model for all AM parts that could be used directly by others. This generalizability of the model is discussed together with its limitations.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1016/j.tmater.2025.100052
T. Balcaen , S. Vangrunderbeeck , W.M. De Borggraeve , G. Kerckhofs
Ex vivo microCT imaging has emerged as a powerful tool for 3D histology of biological tissues, offering significant advantages in terms of spatial resolution, simplicity of protocols and acquisition speed. Among the various techniques available, contrast-enhanced computed tomography (CECT) is particularly favored for its ability to simultaneously visualize soft and mineralized tissue types through the use of contrast agents (CAs), making it suitable for laboratory-based microCT devices. This review focuses on contrast-enhancing staining agents (CESAs), a subclass of CAs, which enrich the X-ray attenuating atom content in soft tissues through interactions. Within this review, CESAs are categorized based on their chemical composition into organic, mixed (i.e. heavy metal and organic ligand) and inorganic compounds, each with specific properties and applications. Despite the growing interest and numerous studies on CESAs, the selection process often relies on trial-and-error, anecdotal knowledge, or commercial availability. This review aims to enhance understanding of the chemical interactions and distribution patterns of CESAs within biological tissues, by discussing a selection of studies grouping observations by tissues and organs, to gain a better understanding of consistent affinity patterns. The findings highlight the complexity and accompanying challenges of predicting CESA distribution. This review will provide a foundation for both intelligent CESA selection and design, tailored to specific research needs as well as a guide for the application expert in choosing relevant literature for designing their experiments.
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