Tomography of Materials and Structures最新文献

{"title":"Recent developments in fly scan methods for phase and multi-contrast x-ray micro-CT based on amplitude modulated beams","authors":"Grammatiki Lioliou ,&nbsp;Oriol Roche i Morgó ,&nbsp;Alberto Astolfo ,&nbsp;Amir Reza Zekavat ,&nbsp;Marco Endrizzi ,&nbsp;David Bate ,&nbsp;Silvia Cipiccia ,&nbsp;Alessandro Olivo ,&nbsp;Charlotte Hagen","doi":"10.1016/j.tmater.2024.100034","DOIUrl":"https://doi.org/10.1016/j.tmater.2024.100034","url":null,"abstract":"<div><p>Beam tracking and edge illumination are phase contrast imaging techniques that rely on amplitude modulated x-ray beams to generate sensitivity to refraction and scattering. While each technique has its advantage (“single shot” three-contrast imaging in beam tracking; the ability to work with relatively large pixels in edge illumination), they also share a common drawback, namely that the modulator shields parts of the sample and, thus, prevents those areas from contributing to the image (under-sampling). Sample stepping, by which frames are acquired with the sample in a different position relative to the modulator (sometimes referred to as “dithering”) can produce well-sampled images. However, in computed tomography (CT), stepping must be performed at each rotation angle, enforcing step-and-shoot acquisitions and leading to long scan times. To enable faster acquisitions, fly scan compatible scanning schemes based on “roto-translating” the sample in the modulated x-ray beam were recently developed. This article reviews these schemes and provides practical guidance for their implementation.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"5 ","pages":"Article 100034"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X24000111/pdfft?md5=ecc26bd547fd0e42a885641b61baa530&pid=1-s2.0-S2949673X24000111-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241389","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}

{"title":"Protocols for preparation of biomineralized samples for computed tomography","authors":"Nina Kølln Wittig ,&nbsp;Carsten Pedersen ,&nbsp;Jonas Palle ,&nbsp;Maja Østergaard ,&nbsp;Thorbjørn Erik Køppen Christensen ,&nbsp;Maik Kahnt ,&nbsp;Anastasiia Sadetskaia ,&nbsp;Jesper Skovhus Thomsen ,&nbsp;Annemarie Brüel ,&nbsp;Henrik Birkedal","doi":"10.1016/j.tmater.2024.100027","DOIUrl":"https://doi.org/10.1016/j.tmater.2024.100027","url":null,"abstract":"<div><p>Computed tomography spans a versatile set of techniques that range several length scales and modalities. It is common to them all that the sample must be prepared in a way which allows for addressing the scientific question(s) posed as well as being suitable for the specific setup of the experiment. We present two lathe-based sample preparation workflows developed to prepare biomineralized samples (here bone) for two very different experiments in terms of setup, types of questions asked, and sample requirements. The first experiment, involving the measurement of high throughput (synchrotron) micro-computed tomography, required the preparation of many samples with homogeneity in size, shape, and bone site. This was achieved through a particular sequence of cutting and embedding steps finalized by lathe milling. The resulting samples were cylindrical in shape with diameters close to the field of view of the ensuing tomography experiment, which allowed maximizing the investigated sample volumes. The second experiment was a combined ptychography and X-ray fluorescence nano-computed tomography experiment, which required preparation of a few-micrometer-sized sample. Moreover, the scientific interest was in a specific, localized feature in bone. Thus, the sample had to be extracted from a precise location from within the whole bone. Again, the developed workflow comprised many steps, including both lathe milling and focused ion beam milling. Importantly, localized preparation was enabled by measuring in-house X-ray micro-computed tomography at crucial points in the workflow. The presented workflows provide examples of preparation pathways that can be standardized and strongly increase the throughput, quality, and success rate of tomography experiments.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"5 ","pages":"Article 100027"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X24000044/pdfft?md5=90efaeb31f66bd0610e9489c7c5e6c53&pid=1-s2.0-S2949673X24000044-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113492","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}

{"title":"The role of networks to overcome large-scale challenges in tomography: The non-clinical tomography users research network","authors":"Paul M. Gignac ,&nbsp;Valeria Aceves ,&nbsp;Stephanie Baker ,&nbsp;Jessica J. Barnes ,&nbsp;Joshua Bell ,&nbsp;Doug Boyer ,&nbsp;Deborah Cunningham ,&nbsp;Francesco De Carlo ,&nbsp;Morgan H. Chase ,&nbsp;Karly E. Cohen ,&nbsp;Matthew Colbert ,&nbsp;Theresa De Cree ,&nbsp;Juan Daza ,&nbsp;Edwin Dickinson ,&nbsp;Valerie DeLeon ,&nbsp;Lindsay Dougan ,&nbsp;Franklin Duffy ,&nbsp;ChristiAna Dunham ,&nbsp;Catherine M. Early ,&nbsp;Dave R. Edey ,&nbsp;Christopher M. Zobek","doi":"10.1016/j.tmater.2024.100031","DOIUrl":"https://doi.org/10.1016/j.tmater.2024.100031","url":null,"abstract":"<div><p>Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"5 ","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X24000081/pdfft?md5=c2f896ba5b3416d400569fbf4fea0392&pid=1-s2.0-S2949673X24000081-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140548235","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}

{"title":"pt4: Phantom-4D, an open-source software tool for creating time-evolving 3D phantoms","authors":"Stephen Catsamas,&nbsp;Glenn Myers,&nbsp;Andrew Kingston","doi":"10.1016/j.tmater.2024.100035","DOIUrl":"https://doi.org/10.1016/j.tmater.2024.100035","url":null,"abstract":"<div><p><span>pt</span>4, an open-source software tool to describe time-evolving phantoms is presented. <span>pt</span>4 allows users to create detailed time-evolving phantoms for testing novel 4D-CT reconstruction algorithms. Ground-truth volumes and simulated X-ray projections can be produced at arbitrary time-points during time-evolution and with customisable pixel dimensions, noise models, and X-ray source trajectories. Phantoms are built up from 3D primitives whose parameters and attenuation can be made arbitrary functions of time. This feature permits both complex continuous and discontinuous time-evolution necessary for thorough testing of 4D-CT reconstruction algorithms. Various phantoms built using <span>pt</span>4 are also presented to demonstrate the versatility of <span>pt</span>4 phantom description. <span>pt</span>4 is written in C++ and is highly parallelised leading to a performant implementation which is feasible to use for up to thousand-of-voxel volume pixel dimensions.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"5 ","pages":"Article 100035"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X24000123/pdfft?md5=44925b625819e749d704eaca8266c7f9&pid=1-s2.0-S2949673X24000123-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315141","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}

{"title":"evoSegment: 4D image segmentation of microstructural evolution using joint histograms","authors":"Johan Hektor ,&nbsp;Jonas Engqvist ,&nbsp;Stephen A. Hall","doi":"10.1016/j.tmater.2023.100023","DOIUrl":"10.1016/j.tmater.2023.100023","url":null,"abstract":"<div><p>A method for semantic segmentation of microstructure evolution from 4D imaging data is described and demonstrated. The method is based on a joint histogram describing the time history of the grayscale in each voxel of the images. After identifying and labeling clusters in the joint histogram, the labels are mapped back to the image. The results demonstrate accurate segmentation and characterization of sample evolution. The advantages of the proposed method include automatic segmentation of many time steps and the ability to track grayscale evolution over time and thereby discriminate similar evolution in different material phases. The method is demonstrated through application to 4D X-ray tomography datasets of temperature cycling in cement mortar and tensile testing of a cast iron sample. Water and air exchange in a pore inside the cement mortar is successfully segmented as a function of temperature. In the case of the deforming cast iron sample, several damage mechanisms are identified and segmented. The method is implemented in an open-source Python package called <em>evoSegment</em>.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"4 ","pages":"Article 100023"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X23000219/pdfft?md5=6a504130962c2f4955beceeccd218164&pid=1-s2.0-S2949673X23000219-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139025169","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}

{"title":"Assessment of low-velocity impact response of a hybrid carbon-aramid braided composite by algorithmic quantification of volumetric structures","authors":"A. Dondish,&nbsp;G.W. Melenka","doi":"10.1016/j.tmater.2023.100022","DOIUrl":"https://doi.org/10.1016/j.tmater.2023.100022","url":null,"abstract":"<div><p>This work investigates the impact response of a hybrid two-dimensional (2D) tubular braided composite through an ex-situ examination of its internal structure. An inter-ply hybrid laminate of biaxial braided carbon and aramid layers was manufactured. The sample underwent split Hopkinson pressure bar (SHPB) impact testing, where the applied energy induced barely visible impact damage (BVID). Volumetric representations of the sample were created before and after impact testing using micro-computed tomography (µCT). Novel algorithms were developed to assess properties of the braided composite sample. First, voids in the sample before and after impact were identified through a series of image processing techniques, after which various void properties were extracted. Each void was then matched between datasets to track property change caused by impact. Next, damage in the sample after impact was identified through another series of image processing techniques, then quantified and characterized in the context of the full sample. Statistical analyses in the form of paired-sample t-tests were performed for void properties and overall surface topologies. Additionally, measurement of 3D strain within selected regions of the sample was performed using digital volume correlation (DVC). Results showed that the impact damage did not cause significant void enlargement, but instead caused surface topology shifts, particularly at the inner surface, owing to plastic deformations caused by impact damage, which were primarily caused by delamination and developed from large voids at the site of impact. The hybrid material configuration exhibited a phenomenon where the inner and outer layers minimized cracking in the middle layer, causing plastic deformation to be the primary impact response of the middle layer. Through the analysis processes developed in this work, quantitative assessment of tubular braided composites was achieved by measuring changes in voids caused by impact at the individual level, and by examining the damage profile while accounting for the voids. The methodology can be applied to various material configurations to provide meaningful insight into the effects of hybridization in the impact response of braided composites.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"4 ","pages":"Article 100022"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X23000207/pdfft?md5=0b74476e0e3901055ce1056cab849c51&pid=1-s2.0-S2949673X23000207-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138557764","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}

{"title":"3D Visualization of cyanobacterial biofilms using micro-computed tomography with contrast-enhancing staining agents","authors":"Laurenz Schröer ,&nbsp;Tim Balcaen ,&nbsp;Karel Folens ,&nbsp;Nico Boon ,&nbsp;Tim De Kock ,&nbsp;Greet Kerckhofs ,&nbsp;Veerle Cnudde","doi":"10.1016/j.tmater.2024.100024","DOIUrl":"https://doi.org/10.1016/j.tmater.2024.100024","url":null,"abstract":"<div><p>Currently, biofilms colonizing surfaces are mainly imaged in 2D by conventional techniques, such as optical or scanning electron microscopy. Confocal laser scanning microscopy or optical coherence tomography can visualize biofilms in 3D, but they suffer from a limited penetration depth and cannot visualize biofilms in opaque materials. Micro-computed tomography (µCT) can overcome these issues, but µCT cannot easily distinguish biofilm structures from water due to a lack of contrast difference. Within this research, five contrast-enhancing staining agents (CESAs) were evaluated for their staining potential of cyanobacterial biofilms, aiming to visualize these biofilms in 3D. Isotonic Lugol and 1:2 hafnium(IV)-substituted Wells-Dawson polyoxometalate (Hf-WD 1:2 POM) were the most promising, as they allowed visualization of the biofilms and revealed structures in the stained biofilms. Staining with isotonic Lugol could clearly visualize bundles of filaments within the biofilm, while Hf-WD 1:2 POM revealed a smooth biofilm. It is assumed that both CESAs have a different affinity towards the biofilms and could thus be used complementary. Monolacunary Wells-Dawson polyoxometalate (Mono-WD POM) showed moderate discrimination while staining with cationic iodinated CA4+ and Hexabrix® (Guerbet) containing anionic ioxaglate did not allow to distinctly visualize the biofilms. These results indicate that µCT, together with CESAs such as isotonic Lugol and Hf-WD 1:2 POM, can be used as a tool to image extensive biofilms or microbial mats in 3D. Further research will determine whether these CESAs are suitable for visualizing biofilms within opaque porous media.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"4 ","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X24000019/pdfft?md5=cd3647016772b1dd88e1fd1ace94772c&pid=1-s2.0-S2949673X24000019-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139653894","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}

{"title":"Grain structure evolution during heat treatment of a semisolid Al-Cu alloy studied with lab-based diffraction contrast tomography","authors":"Jun Sun ,&nbsp;Jules M. Dake ,&nbsp;Jette Oddershede","doi":"10.1016/j.tmater.2024.100025","DOIUrl":"https://doi.org/10.1016/j.tmater.2024.100025","url":null,"abstract":"<div><p>3D experimental data of simultaneously high temporal and spatial resolution are key to validating computational models of materials phenomena. In this study, we exploit lab-based X-ray imaging, combining absorption and diffraction contrast tomography, to capture the evolution of grain structure over a series of interrupted heat treatments of a semisolid Al-Cu alloy. The time resolved response measured on the present Al-Cu model system provides insights into the rearrangement, densification and coarsening of powder compacts at late-stage sintering. The initial Al-Cu microstructure containing 1934 grains dropped to 934 grains after ten annealing steps, while the mean grain size increased from 194 µm to 247 µm. The grain maps of all eleven temporal states are made publicly available to the scientific community for further analysis via the Materials Data Facility. Preliminary statistical investigations of the growth of individual grains show a clear tendency for disappearing grains to be among the smaller grains at the beginning of the experiment. In addition, the rotations of individual grains are generally small fluctuations, but when an abruptly large rotation is observed, it is more likely to occur for a smaller grain at the last annealing step(s) before the grain vanishes. The nature of the data also enables interrogating a few grains that display rotation bursts within the context of their entire local environment to reveal the impact of crystallography and grain contacts upon the microstructural evolution.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"4 ","pages":"Article 100025"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X24000020/pdfft?md5=00f761ed5a3a3778e900780c694484bb&pid=1-s2.0-S2949673X24000020-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749234","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}

{"title":"Dual-energy computed tomography for improved contrast on a polyphase graphitic ore","authors":"Leonard T. Krebbers ,&nbsp;Natalia Grozmani ,&nbsp;Bernd G. Lottermoser ,&nbsp;Robert H. Schmitt","doi":"10.1016/j.tmater.2023.100021","DOIUrl":"https://doi.org/10.1016/j.tmater.2023.100021","url":null,"abstract":"<div><p>Proper ore characterisation is essential for understanding ore deposits and developing efficient mineral processing flow sheets. Conventional mineralogical and chemical techniques are usually used to study ores, but they can be destructive and, in some cases, provide only 2D information. Computed tomography (CT) is an emerging technology in the raw materials sector enabling the non-destructive 3D analysis of the ore mineralogy and microstructure. However, single-energy CT (SECT) has some limitations concerning the accurate imaging and differentiation of polyphase geomaterials comprising a broad range of attenuation properties. By contrast, dual-energy CT (DECT) uses two different X-ray energies to acquire data, which can be used to distinguish between materials with similar attenuation properties. This study explored the application of DECT for the analysis of a polyphase graphitic ore. A sequential fusion approach was utilized to combine data obtained from different X-ray energy scans at high spatial resolution, and varying weighting factors were applied to determine the optimal contribution of each energy level and spectrum. Both, SECT and DECT datasets were quantitatively evaluated based on the contrast-to-noise-ratio (CNR) and Q factor. The findings demonstrate that DECT significantly improves image contrast compared to SECT while further increases image sharpness. As a result, DECT may enable more accurate segmentation and, therefore, more accurate quantitative 3D analysis of graphite ores.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"4 ","pages":"Article 100021"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X23000190/pdfft?md5=20f5e6a696e6f49372b11bfc70f07da0&pid=1-s2.0-S2949673X23000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549372","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}

{"title":"Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography","authors":"Daniil Kazantsev ,&nbsp;Lucas Beveridge ,&nbsp;Vigneshwar Shanmugasundar ,&nbsp;Oxana Magdysyuk","doi":"10.1016/j.tmater.2023.100019","DOIUrl":"https://doi.org/10.1016/j.tmater.2023.100019","url":null,"abstract":"<div><p>Tomographic imaging supports a great number of medical and material science applications. The collected projection data usually has different types of imaging artefacts and noise. Various image pre-processing and reconstruction methods are used to obtain volumetric datasets of high quality for further analysis. In order to minimise reconstruction artefacts, one can apply either filtering and/or data completion/inpainting techniques which can recover the data. Deep learning (DL) methods to remove artefacts and noise have been successfully applied in the past. In this paper, we present a novel approach based on conditional generative adversarial networks (cGANs) to remove stripe artefacts. The novelty of the presented technique is in how the training data for DL is extracted from the same tomographic dataset that needs recovery. We also provide new deterministic stripe detection and inpainting algorithms to support the development. The presented methods are compared with other stripe removal algorithms and applied to 3D and 4D high-resolution X-ray data collected at Diamond Light Source synchrotron, UK. The proposed DL method delivers reconstructed images with minimised ring artefacts while being a parameter-free approach. A similar DL strategy can also be applied to remove other types of artefacts in images.</p></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"4 ","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949673X23000177/pdfft?md5=326f58e724060e858d2642768513a170&pid=1-s2.0-S2949673X23000177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138466459","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}