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243 results for “X-ray tomography”
Zinc Doped Zeolite 13X DIAD X-Ray Diffraction Computed Tomography - 25 and 50 micron spot-size
<p>This repository contains X-Ray Diffraction Computed Tomography (XRD-CT) data of a zinc doped zeolite 13X sample on the Dual Imaging and Diffraction (DIAD / K11) at Diamond Light Source.</p> <p>XRD-CT data is provided at a diffraction spot size of 25 microns for three region of interest slices, with a dataset size of 40x2000x80. Both the raw and reconstructed data is provided, along with the code to perform the reconstructions. </p> <p>XRD-CT data is also provided at a diffraction spot size of 50 microns for a full 1.05mm volume, with a dataset size of 20x2000x40. Scans start at 43336 and finish at 43401, with a movement of 0.05mm vertically upwards between each scan. The raw and reconstructed data is provided, along with the code used to perform the reconstructions. Note: Scan 43401 is excluded as a phase-based reconstruction could not be performed.</p> <p>Powder X-Ray Diffraction data can be found in an alternative repository at 10.5281/zenodo.13329670 which provides the q-values of the peaks for both the Zn and Na phase to allow the best reconstructions.</p> <p>A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 0.8125 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169066_raw.hdf</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
X-ray tomographic datasets associated with the article "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography" (DOI: 10.1016/j.conbuildmat.2024.139091)
<p>This Zenodo repository provides two sets of 3D images, which constitute part of the dataset base for the article titled "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography", written by the same authors cited here, together with other co-authors. The article is published in the journal "Construction and Building Materials". It can be reached <em>via</em> the following URL: <a href="https://doi.org/10.1016/j.conbuildmat.2024.139091" target="_blank" rel="noopener">https://doi.org/10.1016/j.conbuildmat.2024.139091</a>.</p> <p>The core specimens were obtained in 2019 from semi-dense asphalt (SDA) pavement sections located in the Swiss Canton of Zürich. For each of three pavement sections, labelled in the following as SDA4-1yr, SD4-5yr and SDA8, 100 mm diameter cores were extracted, both inside (I) and outside (O) of the wheel path, in order to see the effect of the traffic load on the pore space characteristics. Out of the original cores for the SDA4 pavements, 5 30 mm diameter sub-cores were drilled out of their centers, both in- and out-of the wheel path, and investigated with X-ray tomography. Only 1 30 mm core was analyzed for SDA8, both in- and out- of the wheel path. The asphalt in that pavement type has lower porosity, making it less interesting from the sound absorption viewpoint.</p> <p>The whole dataset consists of .7z archive files. Such files have the following designations: SDA_J_K_L_Tomogram.7z or SDA_J_K_L_PoreSpaceBinTomogram.7z, where J = 1,2, K = I,O and L = 1,2,3,4,5. When referring to the specimen naming within the corresponding article, the first index, J, refers to the specimen "age": J = 1 indicates the 1-year old specimens (called SDA4-1yr within the article); J = 2 refers to the 5-year old ones (SDA4-5yr). The second index, K, refers to the location of the specimen within the pavement section course ("I" for in-wheel path and "O" for out-of-wheel path). The final index L just enumerates the distinct specimens of the same group.</p> <p>There are two additional groups of archive files: LNA_I_Tomogram.7z/LNA_I_PoreSpaceBinTomogram.7z refers to the single in-wheel-path, 7-year old specimen (called SDA8 within the article); LNA_O_Tomogram.7z/LNA_O_PoreSpaceBinTomogram.7z refers to the single out-of-wheel path, 7-year old specimen.</p> <p>The two sets/types of 3D images can be recognized by the different file naming.</p> <p>The first set includes the raw X-ray tomograms of the 22 specimens analyzed. Each tomogram is stored in the form of a "stack" (or series) of 16-bit unsigned integer 2D TIFF image file, being one 2D cross-section (also called "slice", in tomographic jargon) from the "tomographed" volume. Such slices are contained in a folder. The folder was then archived in a .7z archive file.</p> <p>The second set of 3D images is characterized by the filename pattern SDA_J_K_L_PoreSpaceBinTomogram.7z. Each zipped folder contains the slices of the binary tomogram of the whole pore space of the respective specimen, segmented according with the 3d image analysis workflow described within the article. Each slice of such tomogram was stored as a 8-bit unsigned integer 2D TIFF image file, whose pixels can have only two possible values: 255, if the pixel is inside the segmented pore space; 0 if the pixel is outside it.</p> <p>Almost all of the acquired tomograms have an isotropic voxel size of 0.0214 mm, meaning that each slice is separated in space from the next one by such distance. The samples SDA_2_O_1 and SDA_2_I_1 have a voxel size of 0.0220 mm, while the sample LNA_I has a voxel size of 0.0223 mm.</p>
Data Accompanying "Dynamics of Water Absorption in Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography"
<p>These are the datasets analysed in the publication "Dynamics of Water Absorption in<br> Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography" by Stavropoulou <em>et al.</em> in 2020 in Frontiers in Earth Science, DOI: <a href="https://doi.org/10.3389/feart.2020.00006">https://doi.org/10.3389/feart.2020.00006</a></p> <ol> <li>File 1 contains the 3D reconstructed x-ray and neutron tomography volumes analysed in the paper. State 002 is taken as a reference and the greylevels of all images in the times series for both x-ray and neutrons are rescaled using two characteristic image features (top-cap and air in the case of x-rays) linearly to align with 002. Neutron volumes are rescaled to the same pixel size as 2-bin x-ray volumes, and a mean registration is applied to align neutrons with x-rays.<br> Furthermore, a bilateral filter is applied to the neutron tomographies (domain sigma = 1, range sigma=3000).<br> Full scale images are available at <a href="https://doi.ill.fr/10.5291/ILL-DATA.UGA-42">https://doi.ill.fr/10.5291/ILL-DATA.UGA-42</a><br> </li> <li>File 2 contains the joint histograms for registered pairs of images, as visible in Figure 4 and Figure 5.<br> </li> <li>File 3 contains the results of the digital volume correlation performed with the <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam</a> tookit.<br> The overall "registration" is used to create Figure 6<br> The global correlations available in "gdic" are used to create Figures 7, 8 and 9.</li> </ol>
X-ray computed tomography of bedded halite and halite crystals from the Bonneville Salt Flats
<p>X-ray computed tomography of bedded halite and halite crystals from the Bonneville Salt Flats, Utah. </p>
Dataset accompanying the article: Analyzing X-Ray tomographies of granular packings
<p>This dataset (and the added analysis software) belong to the article: <em>Analyzing X-Ray tomographies of granular packings</em> in Review of Scientific Instruments.</p> <p>The abstract of the article: Starting from three-dimensional volume data of a granular packing, as e.g. obtained by X-ray Computed Tomography, we discuss methods to first detect the individual particles in the sample and then analyze their properties. This analysis includes the pair correlation function, the volume and shape of the Voronoi cells and the number and type of contacts formed between individual particles. We mainly focus on packings of monodisperse spheres, but we will also comment on other monoschematic particles such as ellipsoids and tetrahedra. This paper is accompanied by a package of free software containing all programs (including source code) and an example three-dimensional dataset which allows the reader to reproduce and modify all examples given.</p> <p> </p>
Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading
<p>Ex-situ X-ray CT fatigue testing data sets published as a data in brief:</p> <p>"<em>Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading</em>", Data in brief, 2017, doi.org/10.1016/j.dib.2017.10.074.</p> <p>Together with the following article:</p> <p>K. M. Jespersen and L. P. Mikkelsen, “Three dimensional fatigue damage evolution in non-crimp glass fibre fabric based composites used for wind turbine blades,” <em>Compos. Sci. Technol. </em> (In press), 2017, 10.1016/j.compscitech.2017.10.004.</p>
X-ray scattering tensor-tomography dataset for a steel wire using the austenitic {220}-peak.
<p>Experimental data from a scanning-probe wide angle scattering experiment performed at the cSAXS beamline at teh Swiss Light Source at the Paul Scherrer Institure in Villigen, Switzerland.</p> <p>The file-format is that used in by the software package mumott (mumott.org).</p> <p>The sample is a tangled knot of hard-tempered steel. The detector images have been azimuthally re-grouped and only the intensity of the austeinte {220} peak is included in 48 separrate azimuthal bins.</p>
FIGURE 4 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 4. Ordovician bryozoan colony. One-half of hemispherical bryozoan, interior of object, bearing probably oldest boring attributable to ichnogenus Entobia Bronn, 1837. Besides semi-radial tunnels and exploratory threads, three bulbous chambers discovered near the center of the hemisphere. Darriwilian (middle Ordovician), Khrevitsa locality, St. Petersburg Region, Russia. Scale bar equals 1 cm.
FIGURE 7 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 7. Three-dimensional visualization of a shell of the recent Foraminifera Amphistegina sp. illustrating the potential of micro-CT in investigations of recent marine shelled organisms. (A) A surface view of the whole shell. (B) A transversal section through the whole shell (C, D) Details of the shells´s surface.
FIGURE 5 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 5. Minute conulariid specimen. (A) Conulariid specimen of Archaeoconularia fecunda and trepostome bryozoan colony; coated with ammonium chloride, no. NMP L21990, locality Loděnice, Upper Ordovician, Zahořany Formation (lower Katian) (B) Micro-CT visualizing of inner surfaces. Scale bar equals 5 mm.
FIGURE 3 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 3. Siliceous nodules of the Šárka Formation. (A, B) Pricyclopyge binodosa, complete trilobite, no. NMP L 35055, locality Praha-Šárka, Middle Ordovician (Darriwilian), (A) Enrolled trilobite coated with ammonium chloride, exterior of objects. (B) Micro-CT image showing dense burrows, interior of objects. (C, D) Rostrum with eyes of a trilobite P. binodosa, no. NMP L46892, locality Praha-Šárka, Middle Ordovician (Darriwilian). (C) Rostrum coated with ammonium chloride, exterior of objects. (D) Micro-CT visualization of tunnels, interior of objects. (E) Bivalve Redonia deshayesi, micro-CT image showing trace fossils, interior of objects, no. NMP L 51722, locality Osek, Middle Ordovician (Darriwilian). All scale bars equal 5 mm.
FIGURE 2 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 2. Custom-made holders specially adapted for each scanned specimen. (A) Plastic cup. (B) Polystyrene holder. (C) Aluminum holder for small specimens. (D) Plastic tube filled with polystyrene.
FIGURE 1 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 1. (A) Single x-ray projection. Schematic representation of positioning of the investigated object inside x-ray device. (B) Multiple x-ray projections as the object rotates. Positioning of investigated object inside micro-CT device. (C) Example of 3D dataset, i.e., a group of 2D slice images acquired by the MicroCT scanner. (D) Examples of Volume rendering; technique in visualization and computer graphics, used to display object from 3D data set in different aspects and orientations.
FIGURE 6 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 6. Tube fragments of the serpulid polychaete Pyrgopolon (Pyrgopolon) deforme. Left images show exterior of objects; right images show interior of objects. (A) Specimen encrusted with bryozoan colonies and serpulid worms, boreholes assigned to Entobia Bronn, 1837, representing the most common ichnogenus in the examined serpulid tubes, no. MHNLM EMV 2016.3.14. (B) Intensely bored specimen preserving tunnels of ichnogenera Entobia and Trypanites Mägdefrau, 1932, no. MHNLM EMV 2016.3.44. (C) Serpulid tube with Entobia boreholes and encrusting juvenile oyster, no. MHNLM EMV 2016.3.40. Scale bar equals 1 cm.
X-ray linear dichroic tomography of crystallographic and topological defects
<p>Open Data for "X-ray linear dichroic tomography of crystallographic and topological defects" published in <a href="https://www.nature.com/articles/s41586-024-08233-y">Nature <strong>636</strong>, 354 (2024) </a></p> <div> </div> <div>Full citation:</div> <div>A. Apseros, V. Scagnoli, M. Holler, M. Guizar-Sicairos, Z. Gao, C. Appel, L. J. Heyderman, C. Donnelly & J. Ihli </div> <div>X-ray linear dichroic tomography of crystallographic and topological defects.</div> <div><em>Nature <strong>636</strong>, 354</em> (2024).</div> <div>https://www.nature.com/articles/s41586-024-08233-y</div>
Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory
<p><strong>Summary</strong></p><p>This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022].</p><p> </p><p><strong>Description</strong></p><p><i>Sample information</i></p><p>The samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.</p><p><i>Scanner information</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p><p><i>Scanning geometry</i></p><p>The mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.</p><p>The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.</p><p>Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.</p><p><i>Scanning settings</i></p><p>A peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.</p><p><i>Reconstructing volumes</i></p><p>The repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset.</p><p> </p><p><strong>Research group</strong><br>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p><p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p><p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p><p><strong>References</strong></p><p>[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging<br>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p><p>If you use (parts of) this data in a publication, please consider citing the first article.</p>
X-ray computed tomography dataset of a walnut
<p>walnut_scan:</p> <ul> <li>scan performed with a conventional micro-CT</li> <li>1601 acquired projections as tiff stack</li> <li>info file containing corresponding metadata</li> </ul> <p> </p> <p>walnut_rec:</p> <ul> <li>reconstructed volume as tiff stack</li> <li>info file containing corresponding metadata</li> <li>reconstruction performed with pyXIT (see reference)</li> </ul> <p> </p>
High-resolution hard X-ray tomography and histology of a rat jaw for stem cell-mediated distraction osteogenesis
<p>Histology and microtomography of a rat jaw after distraction. These datasets appear in "<em>Combining high-resolution hard X-ray tomography and histology for stem cell-mediated distraction osteogenesis</em>" Applied Sciences 12(12) (2022) 6268.</p> <p>Micromography (hdr/img files) has pixel size of 10.0228 µm. Histology (.tif file) has pixel size of 0.243094 µm.</p> <p>Slice to volume registration scripts can be found at https://github.com/grodgers1/SliceToVolume.</p>
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