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3,036 results for “ct”
Evaluation of IDEAL-CT test in Utah for Balanced Mix Design
<p>The data contained herein are the results of asphalt mixtures tested at 25 C using the indirect tensile configuration based on the IDEAL tests developed by Zhou et al. (Zhou, F., Newcomb, D., Gurganus, C., Banihashemrad, S., Park, E., Sakheeifar, M., and Lytton, R.: Experimental Design for Field Validation of Laboratory Tests to Assess Cracking Resistance of Asphalt Mixtures. Final Project Report, NCHRP project 9-57. April 2016)</p> <p>The work was performed in the State of Utah as part of a research contract between the Department of Transportation and the University of Utah. PEPG, LLC., a consulting lab was also involved. The complete report can be found at the Utah Department of Transportation website (<a href="http://www.udot.utah.edu">www.udot.utah.edu</a>), Research and Innovation Division.</p>
Confocal and micro-CT data of Miniomma chenkuni, holotype, NIGP173375
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Miniomma chenkuni</em> (NIGP173375) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "A miniaturized ommatid beetle in mid-Cretaceous Burmese amber (Coleoptera: Archostemata: Ommatidae)" (DOI:10.11606/1807-0205/2020.60.63).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using 488 nm Argon laser excitation line. Both original CZI files and exported TIFF stacks are provided.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 2.1437 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 4 s; projections, 3001. The TIFF stack is provided.</p>
Treinamento Micro-CT
<p>Treinamento de Micro-CT (X-Ray Micro-Computed Tomography) realizado no micro-tomógrafo Xradia Versa 510 da Zeiss nas instalações da Universidade Federal Fluminense por André Pereira, no contexto de diversos projetos de pesquisa, financiados pela Shell e por diversos órgãos de fomento à pesquisa, em especial ao CNPq e a FAPERJ.</p>
Phase-contrast CT of BDL rats liver-2 week
<p>Datasets of paper "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo PCCT"</p>
DICOM images of abdomen CT phantom in axial, helicoid, iterative and dual energy scans.
<p>The two ZIP files contain the images of a CT phantom of abdomen. DualEnergy.zip contains files of acquisition at 80 kVp and 140 kVp. The second file HelicalAxialIterative.zip contains acquisition done in axial, helicoidal (aka spiral) and iterative reconstruction (Saphire) modes. Axial images have AbdSeq in the name, Helicoidal have Abdomen and B in the filter name, Iterative have Abdomen and I in the filter name. You can compare Exposure in mAs for each acquisition modality, how it diminishes from sequential/helicoidal/axial through helicoidal/spiral to iterative. This scanner if programmed with iterative acquisition can still reduce exposure from helicoidal scan. The acquisition was done on Siemens-Healthineers Somatom Confidence CT scanner. </p> <p>Antonio Ortiz Lora antonio.ortiz.lora.sspa@juntadeandalucia.es</p> <p>Marcin Balcerzyk mbalcerzyk@us.es</p> <div class="ms-editor-squiggler"> </div>
Cranial CT Scan, Astrocytoma patient after recent tumor removal surgery
<p>A cranial CT scan from a patient with an astrocytoma brain tumor surgically removed August 19, 2014. Scan taken November 19, 2014. Patient: Steven Keating. Patient age: 26 at time of scan. Open access (CC0) with approval from patient. More information available on patient at www.stevenkeating.info.</p>
Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - DICOM Database
<p>This dataset contains DICOM versions of the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE.</p> <p>NRRD versions can be found in http://dx.doi.org/10.5281/zenodo.20766 (doi:10.5281/zenodo.20766).</p>
Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms - NRRD Database
<p>This dataset contains the 24 anthropomorphic pulmonary CT phantoms accompanying the manuscript "Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms" submitted to PLoS ONE.</p> <p>DICOM versions can be found at http://dx.doi.org/10.5281/zenodo.32740 (doi:10.5281/zenodo.32740)</p>
Ct Laboratory Sentinel Testers 2008-2014
<p>Dataset comprises tests sampled in the German Chlamydia trachomatis laboratory sentinel during 2008 and 2014.</p>
Figure 21. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 21. - Eupolybothrus cavernicolus Komerički & Stoev sp. n., paratype, 3D model, volume rendering, created with CTVox, virtual rotation and dissection. Movie available at: YouTube.
Figure 22. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 22. - Movie of Eupolybothrus cavernicolus Komerički & Stoev sp. n., holotype, filmed ex-situ in a plastic container. Movie available at: YouTube.
Data from: Seeing the Invisible: On Aortic Valve Reconstruction in Non-Contrast CT
<p><strong>Abstract</strong></p> <p>In this dataset, we present supplementary material from the paper by Bujny et al. (2024). The material includes STL models of aorta segmented in contrast Computed Tomography (CT) scans by a medical expert and using the Machine Learning (ML) model described in the paper. A gallery of ML-based segmentations of the aortic root in non-contrast CT, with the corresponding scans and aortic valve meshes registered from contrast CT using the Iterative Closest Point (ICP) algorithm is provided, as well. Finally, we include the source code of the ICP-based accuracy evaluation method proposed in the paper, with an example in Jupyter Notebook. All of the data included in the dataset has been generated based on the CT scans from the openly available orCaScore dataset (Wolterink et al. 2016).</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>The root folder contains 3 catalogs:</p> <ul> <li>contrast_aorta_stls – set of 19 Ground Truth (GT) segmentations of aorta in contrast CT, with the corresponding inferences of the contrast ML model described in the paper, for the scans from the open-source orCaScore dataset (Wolterink et al. 2016).</li> <li>icp_code – Python code of the ICP-based accuracy evaluation method proposed in the paper.</li> <li>icp_gallery – HTML gallery of aorta segmentations based on the non-contrast ML model described in the paper, with the corresponding CT scans and meshes of the aortic valves segmented in contrast CT, which were rigidly registered to the non-contrast scans using the proposed ICP-based approach. In the CT scans, blue and red contours were used to depict non-contrast-based and registered contrast-based ML segmentations, respectively.</li> </ul> <p> </p> <p><strong>References</strong></p> <p>M. Bujny, K. Jesionek, J. Nalepa, T. Bartczak, K. Miszalski-Jamka, M. Kostur, “Seeing the Invisible: On Aortic Valve Reconstruction in Non-Contrast CT,” 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Marrakesh, Morocco, 2024 [accepted].</p> <p>J. M. Wolterink et al., “An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework: Evaluation of cardiac CT-based automatic coronary calcium scoring,” Med. Phys., vol. 43, no. 5, pp. 2361–2373, 2016.</p>
CT reconstruction and structured light scan of a small wooden block
<p><strong>Summary</strong></p><p>This submission contains a reconstruction from a tomographic dataset and a structured light scan of a small wooden block.</p><p>The data is made available as part of [Bossema et al., 2023] and is meant to serve as a practice dataset for the Blender plugin that is published along the article. The code for the plugin can be found here and on github. </p><p> </p><p><strong>Apparatus</strong></p><p>The CT dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban et al., 2020].</p><p>The structured light scan was acquired using an Artec Spider.</p><p><strong>Sample Information</strong></p><p>The sample is small wooden block (6x5x3cm).</p><p><strong>List of Contents</strong></p><p>The content of the submission is given below.</p><ul><li><strong>CT_intact_woodblock_130micron:</strong> CT reconstruction, TIFF files, resolution 130 micron.</li><li><strong>Surface_intact_woodblock: </strong>structured light scan</li></ul><p>Acquisition details can be found in the supplementary material of [Bossema et al, 2023]</p><p>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI), in collaboration with Rijksmuseum Amsterdam.</p><p><strong>Contact Details</strong></p><p>For more information or guidance in using these datasets, please get in touch with </p><ul><li>bossema [at] cwi.nl</li></ul><p> </p><p><strong>Acknowledgements</strong></p><p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p>
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: raw micro-CT dataset
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = <a href="https://zenodo.org/api/records/10145102/draft/files/05_dry_bin2_merged.tif/content">05_dry_bin2_merged.tif</a></li><li>Sample fully saturated with brine = <a href="https://zenodo.org/api/records/10145102/draft/files/07_wet_bin2_merged.tif/content">07_wet_bin2_merged.tif</a></li><li>H2L during high flow = <a href="https://zenodo.org/api/records/10145102/draft/files/09_h2lh_merged.tif/content">09_h2lh_merged.tif</a></li><li>H2L during low flow = <a href="https://zenodo.org/api/records/10145102/draft/files/11_h2ll_2_merged.tif/content">11_h2ll_2_merged.tif</a></li><li>H2L at the end of drainage (no flow) = <a href="https://zenodo.org/api/records/10145102/draft/files/16_dra1_pd5_merged.tif/content">16_dra1_pd5_merged.tif</a></li><li>H2L at the end of imbibition (no flow) = <a href="https://zenodo.org/api/records/10145102/draft/files/21_imb1_pi1_merged.tif/content">21_imb1_pi1_merged.tif</a></li><li>Sample fully resaturated with brine = <a href="https://zenodo.org/api/records/10145102/draft/files/28_wet2_bin2_merged.tif/content">28_wet2_bin2_merged.tif</a></li><li>L2H during low flow = <a href="https://zenodo.org/api/records/10145102/draft/files/29_2_l2hl_merged.tif/content">29_2_l2hl_merged.tif</a></li><li>L2H during high flow = <a href="https://zenodo.org/api/records/10145102/draft/files/30_l2hh_merged.tif/content">30_l2hh_merged.tif</a></li><li>L2H at the end of drainage (no flow) =<a href="https://zenodo.org/api/records/10145102/draft/files/31_dra2_pd1_merged.tif/content">31_dra2_pd1_merged.tif</a></li><li>L2H at the end of imbibition (no flow) =<a href="https://zenodo.org/api/records/10145102/draft/files/33_imb2_pi1_merged.tif/content">33_imb2_pi1_merged.tif</a></li></ol>
micro-CT dataset: Aphrodita longipalpa (Aphroditidae: Annelida) parasitized with Veneriserva pygoclava (Dorvilleidae: Annelida)
<p>This is the complete raw micro-CT dataset and the aligned image stacks generated with Nrecon (Bruker).</p> <p>It shows an adult sized <em>Aphrodita longipalpa</em> specimen, the host of a male and a female endoparasitic <em>Veneriserva pygoclava. </em>The specimen was formalin-fixed and ethanol-preserved and is deposited at the Benthic Invertebrate Collection of Scripps Institution of Oceanography (<a href="https://sioapps.ucsd.edu/collections/bi/catalog/A10919/?q=cf.+longipalpa&image=Any&idx=2">SIO-BIC A10919</a>). The specimen was stained with a contrast enriching phosphotungstic acid solution (0.3% PTA in 70% ethanol) for 3 weeks before scanning.<br> <br> A SkyScan 1272 µCT scanner (Bruker microCT, Kontich, Belgium) was used with the following parameters: 60 kV source voltage, 166 μA source current, 972 ms exposure and a camera resolution of 1224 × 820 px. The voxel resolution was 7.4 μm. </p>
Data file for Massé et al.'s article, "Unraveling the Life History of Past Populations through Hypercementosis: Insights into Cementum Apposition Patterns and Possible Etiologies using Micro-CT and Confocal Microscopy".
<p>This repository provides a supporting data file for the following research article:</p> <p>Massé L., d’Incau E., Souron A., Vanderesse N., Santos F., Maureille B., Le Cabec A. (2024) Unraveling the Life History of Past Populations through Hypercementosis: Insights into Cementum Apposition Patterns and Possible Etiologies Using Micro-CT and Confocal Microscopy. <em>Biology</em>, 13, 43. doi: <span><a href="https://doi.org/10.3390/biology13010043" target="_blank" rel="nofollow noopener noreferrer">10.3390/biology13010043</a></span></p> <p>For the detailed statistical analyses performed using this dataset, see Supporting Information 1 of the article.</p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 1 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 1 and 2. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 (this one) <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
Test Dataset for 3D semantic image segmentation of the various organs from CT and MR scans
<p>These test cases are for the <a href="https://github.com/MIC-DKFZ/nnUNet/releases/tag/v1.7.1">nnUnet v1</a> models trained on the following datasets:<br><br></p> <table> <tbody> <tr> <td>Dataset </td> <td>Task</td> <td>Model Details on Zenodo</td> </tr> <tr> <td> <a href="../record/6802614">TotalSegmentator</a> and <a href="../record/5903672">FLARE21</a> datasets</td> <td>Segment Liver from CT scans</td> <td>https://zenodo.org/record/8274976</td> </tr> <tr> <td><a href="https://kits-challenge.org/kits23/">KiTS23</a> datasets and a subset of the<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=5800386#5800386566e265abf95408aa64c4917f0cbe5d9"> TCGA-KIRC </a>dataset</td> <td>Segment Kidney, Cyst, and Tumors from CT Scans</td> <td>https://zenodo.org/records/8277846</td> </tr> <tr> <td><a href="http://ji%20yuanfeng.%20(2022).%20amos%20a%20large-scale%20abdominal%20multi-organ%20benchmark%20for%20versatile%20medical%20image%20segmentation%20[data%20set].%20zenodo.%20https">AMOS</a> and <a href="http://macdonald,%20jacob%20a.,%20zhu,%20zhe,%20konkel,%20brandon,%20mazurowski,%20maciej,%20wiggins,%20walter,%20&%20bashir,%20mustafa.%20(2020).%20duke%20liver%20dataset%20(mri)%20v2%20(2.0.0)%20[data%20set].%20zenodo.%20https//doi.org/10.5281/zenodo.7774566">DUKE Liver</a> datasets</td> <td>Segment Liver from the MR scans</td> <td>https://zenodo.org/record/8290124</td> </tr> <tr> <td>Data from m <a href="../record/6624726">pi-cai</a></td> <td>Segment Prostate region from MR scans</td> <td>https://zenodo.org/record/8290093</td> </tr> </tbody> </table>
Antikythera Mechanism Main Fragment (CT)
3D surface model based on the highest resolution CT scan of the main fragment of the Antikythera Mechanism after improvement of the corrupted scan as detailed in https://doi.org/10.1371/journal.pone.0207430. The Antikythera mechanism (an ancient greek astronomical calculator) originally made of bronze, was recovered from the sea after 2000 years, over this time it had corroded and become a calcified lump. This has changed the homogeneity of the original metal (if there was any in the first place) making it incredibly difficult to extract the mechanism from the CT scan to produce a 3D model. For this reason, some parts may appear 'missing'. # Front  # Back  Orignal CT data captured by X-Tek now owned by Nikon Metrology. Improved CT data available via https://doi.org/10.7910/DVN/UCXZWU. Images: https://en.wikipedia.org/wiki/Antikythera_mechanism Model Texture: https://www.3dxo.com/textures/5320_bronze Source: Objaverse 1.0 / Sketchfab
Mechanical alphabet, TM2002 (CT)
Christopher Polhem (1661-1751) created the so-called mechanical alphabet in the years just before 1700. The wooden models illustrated the fundamentals of physics and mechanics and were intended to serve as an educational tool in teachings at the Laboratorium mechanicum established in 1697. After his death, the collection continued to serve as a didactic instrument and became part of the Swedish Royal Model Chamber. In 1925, the collection of 31 models was deposited at the newly established National Museum of Science and Technology. [TM2002](https://digitaltmuseum.se/021026545046/mekaniskt-alfabet-modell?i=23&aq=owner%3A%22S-TEK%22"tm2002") (TEKS0021480) CT-Scan. Created by Kristofer Jansson, Interspectral. Read more about [Polhem]( https://en.wikipedia.org/wiki/Christopher_Polhem"polhem") on Wikipedia. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.