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444 results for “CT scanning”
Fig. 11 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 11. Ventral view of the Tribodus braincase, anterior at top (surface rendering). No scale.
Fig. 5 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 5. Tribodus limae, UERJ-PMB-114. A, dorsal; B, ventral views. Scale bar is 2 cm.
Fig. 4 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 4. Tribodus limae, UERJ-PMB-40. A, dorsal; B, ventral views. Scale bar is 2 cm.
Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
<p>We uploaded the Table of included articles in the systematic review "Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment"</p>
Ferron Coal CT Scan
<p>CT scan of a Subbituminous Ferron Coal sample, cut at 1.5 diam x 3" length, obtained from underground mine in San Rafael Swell, Utah. Cleats and fracture distribution is observed in dark areas. The white bands are denser material as bedding planes.</p>
MICCAI FLARE22 Challenge Dataset (50 Labeled Abdomen CT Scans)
<p>This dataset was used as the labeled training set in MICCAI FLARE 2022 Challenge https://flare22.grand-challenge.org/.</p> <p>The CT images and pancreas annotations are from http://medicaldecathlon.com/</p> <p>The other organ annotations are from AbdomenCT-1K (research purpose only).</p> <p>If this dataset is useful in your research, please give credit to the following two papers:</p> <p>A large annotated medical image dataset for the development and evaluation of segmentation algorithms</p> <p>https://arxiv.org/abs/1902.09063</p> <p>AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?</p> <p>https://ieeexplore.ieee.org/document/9497733</p>
Non-Contrast CT Scans Dataset for Subarachnoid Hemorrhage: Bleeding Segmentation
<p>This dataset comprises non-contrast CT scans of patients admitted with subarachnoid hemorrhage. All scans are provided in the NIfTI format. Details of the image preprocessing, which encompasses anonymization, resampling, skull stripping, and intensity normalization, are available at <a href="https://github.com/smcch/Subarachnoid_Hemorrhage_segmentation_and_mortality_prediction">https://github.com/smcch/Subarachnoid_Hemorrhage_segmentation_and_mortality_prediction</a>.</p> <p>Each scan is accompanied by a corresponding NIfTI volume, showcasing the expert manual segmentation of the hemorrhage.</p>
micro CT scan of Ice core, publication DOI:10.1109/TGRS.2023.3334867
<p>The dataset is used in the paper:</p> <p><strong>Bagherzadeh, Faramarz, et al. "Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Model." <em>IEEE Transactions on Geoscience and Remote Sensing</em> 61 (2023): 1-11.</strong><br><strong>DOI:10.1109/TGRS.2023.3334867</strong></p> <p><br>Polar ice cores 2D images of Micro CT scans that could be used for training and testing a deep neural network for image segmentation. This dataset is connected with the corresponding paper:<br>Title: "Ice Core micro-CT Image Segmentation with Deep Learning and Gaussian Mixture Model"</p> <p>There are mainly 3 specimens from Polar regions (snow, firn, and bubbly ice) with 60-micron resolution and a corresponding ground truth per input which was derived from a higher-resolution image with the methodology explained in the article.</p>
Recombinant Thyrotropin PET-CT Fusion Scanning in Thyroid Cancer
ClinicalTrials.gov study NCT00181168. IPD Sharing: Not stated. Countries: 2. Publications: 3.
Bone Structure and Strength Evaluated by Extreme-CT Scan Before and After Treatment of Hyper- and Hypothyroidism
ClinicalTrials.gov study NCT02005250. IPD Sharing: NO. Countries: 1. Publications: 2.
PET Scan and CT Scan in Evaluating Response in Patients Undergoing Radiofrequency Ablation for Lung Metastases
ClinicalTrials.gov study NCT00382252. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Low Dose Ionizing Radiation Using CT Scans as a Potential Therapy for Alzheimer's Dementia: A Pilot Study
ClinicalTrials.gov study NCT03597360. IPD Sharing: NO. Countries: 1. Publications: 17.
Data from: Automatic segmentation of early Triassic vertebrate fossil CT scans: Reducing human annotation time through deep learning
Open the record for dataset details and reuse information.
Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel with dynamic changes in the scanning geometry
<p><strong>Summary</strong></p> <p>This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel.</p> <p>This is the second experiment detailed in Case Study 3 in [Coban 2020], and this submission can be treated as a follow up to [Coban&Lucka 2019]. </p> <p> </p> <p><strong>Apparatus</strong></p> <p>The 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 2020].</p> <p> </p> <p><strong>Sample Information</strong></p> <p>The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.</p> <p> </p> <p><strong>Experimental Plan</strong></p> <p>This experiment is performed such that 150 projections are collected over 360 degrees, for a total of 166 rotations, with exposure time 12 ms for each projection. This means that in total the submission contains 25000 projections. This experiment took 5 minutes of acquisition time, during which we (at user's command) zoom in onto the bottom of the sample holder (i.e. where the tablet rests). We later (again, at user's command) shift the view (i.e. the tube and the detector) upwards to the top of the sample to observe foaming on the surface. Finally, before the end of the 5-minute acquisition period, we zoom out to the original magnification. Every time the geometry undergoes a major change such as zooming in (which would affect the reconstruction), the system creates a new data settings file with the new geometrical information, appended by the projection number, therefore marking the change. However, since there is no major change created by the vertical shift of the tube and detector (as in no change in geometry that would affect the reconstructed images), there is no new data settings file for this event. </p> <p>The spatial resolution for this data is 193μm at the beginning (or end) of the experiment, which at an arbitrary point changes to 76μm. For a smooth data transfer, each projection image is binned down to the size of 486px-by-384px. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed.</p> <p>All raw data (i.e. with no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission is given below.</p> <ul> <li><strong>scan_1</strong>: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif,</em></li> <li>pre flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>post flat-field (or open-shutter after acquisition) image, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em> (25000 projections in total),</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata (this is the final geometry info file),</li> <li><em>data settings XRE_5220.txt</em> (geometry info recorded after the zoom-in)</li> <li><em>data settings XRE__22610.txt</em> (geometry info recorded after the zoom-out, same as <em>data settings XRE.txt</em>)</li> </ul> </li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> </ul> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.</p>
Petermann Fjord Sediment Core Computed Tomography (CT) Scans (Cruise OD1507)
<p>Computed tomography (CT) scans of sediment cores collected from Petermann Fjord during the PETERMANN15 expedition of the Swedish Icebreaker Oden, OD1507. Included cores, 03TC, 03PC, 04GC, 06PC, 08GC, 10PC, 10TC, 40PC, 40TC, and 41GC. Data include 2 mm thick coronal slices in DICOM format and SedCT products, including images and CT numbers.</p>
Figure 15. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 15. Cleveland tyrannosaur skull, CMNH 7541. Volume renderings of digitally extracted right mandible derived from CT data in A, dorsal; B, medial; C, lateral views. A–C are stereopairs. D, close-up of mandible in lateral view; numbers correspond to tooth positions, of which there are 16. Scale bars equal 10 cm.
Figure 12. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 12. Cleveland tyrannosaur skull, CMNH 7541. Volume (A) and surface (B) renderings of digitally extracted left palatine derived from CT data in lateral view. Both sets are stereopairs. Surface rendering (B) is partially transparent to reveal the internal pneumatic sinuses; note that the two sinuses do not communicate. Scale bar equals 2 cm. See Appendix for abbreviations.
Figure 11. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 11. Cleveland tyrannosaur skull, CMNH 7541. Volume (A) and surface (B) renderings of digitally extracted left quadratojugal derived from CT data in lateral view. Both sets are stereopairs. Surface rendering (B) is partially transparent to reveal the internal pneumatic sinus. In the actual specimen, the jugal process was displaced relative to the rest of the bone, but has been digitally reattached here. Scale bar equals 5 cm. See Appendix for abbreviations.
Figure 9. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 9. Cleveland tyrannosaur skull, CMNH 7541. Surface renderings of digitally extracted braincase derived from CT data, made partially transparent to reveal brain endocast (light blue) and internal pneumatic sinuses, in A, left lateral; B, left rostroventrolateral; C, caudoventral views. Scale bar equals 10 cm. See Appendix for abbreviations.
Figure 6. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 6. Cleveland tyrannosaur skull, CMNH 7541. Surface renderings of digitally extracted braincase derived from CT data in A, left lateral; B, left rostroventrolateral; C, caudal views. Figure 5A–C shows corresponding stereopairs of volume renderings. Scale bar equals 10 cm. See Appendix for abbreviations.
ScienceDex guides
Understand access before you commit
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.