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3,038 results for “CT”
ds-uct-001: Cast Iron GGG40: X-Ray micro-CT of a nodular cast iron sample class GGG40.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of a nodular cast iron sample class GGG40, including both raw projection data and the final reconstructions, for three different resolutions (voxel sizes of 1 μm, 3 μm and 11 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1 (1024) - Voxel size: 1 μm; Sample-source: 26 mm; Sample-detector: 150 mm; Optical magnification: 4.0X; Filter: HE#6; Beam energy: 160 kV; Power: 10 W; Exposure time: 60.0 sec; Projections: 1600.<br> .Tomo2 (1024) - Voxel size: 3 μm; Sample-source: 28 mm; Sample-detector: 35 mm; Optical magnification: 4.0X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 10.0 sec; Projections: 3200.<br> .Tomo3 (1024) - Voxel size: 11 μm; Sample-source: 30 mm; Sample-detector: 158 mm; Optical magnification: 0.4X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 3.0 sec; Projections: 3200.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-001.txt<br> .ds-uct-001_cast_iron_ggg40_01um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_03um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_11um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_01um_1600p.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_recon.txm</p>
ds-uct-002: Root Canal Strain: X-Ray micro-CT of four teeth before and after root canal procedure.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of four teeth before (TomoB) and after (TomoA) simulation of root canal treatment and retreatment procedures instrumented with strain-gauge, including reconstructions, for two different resolutions (TomoB and TomoA with voxel sizes of 20.0 μm and 10.5 μm, respectively).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1B/Tomo2B/Tomo3B/Tomo4B (1024) - Voxel size: 20.0 μm; Sample-source: 45.0 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 60 kV; Power: 5 W; Exposure time: 2.0 sec; Projections: 1600.<br> .Tomo1A/Tomo2A/Tomo3A/Tomo4A (2048) - Voxel size: 10.5 μm; Sample-source: 48.2 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#2; Beam energy: 60 kV; Power: 5 W; Exposure time: 7.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-002.txt<br> .ds-uct-002_root_canal_strain_tomo1b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo2b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo3b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo4b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo1a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo2a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo3a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo4a_10um_8bits.zip<br> .PB_PARECER_CONSUBSTANCIADO_CEP_2650528.pdf</p>
ds-uct-007: Asphalt Concrete: X-Ray micro-CT of an asphalt concrete sample.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of an asphalt concrete sample, including both raw projection data and the final reconstructions, for one single resolution (voxel size of 7 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .Image data segmented with four different segmentation techniques: DL (Deep Learning), ML (Machine Learning), TH (Thresholding) and WS (Watershed).<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.<br> <strong>Details</strong>:<br> .Tomo - Voxel size: 7 μm; Sample-source: 31 mm; Sample-detector: 274.25 mm; Optical magnification: 0.4X; Filter: LE#6; Beam energy: 100 kV; Power: 9 W; Exposure time: 4.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-007.txt<br> .ds-uct-007_asphalt_concrete_07um_16bits.zip<br> .ds-uct-007_asphalt_concrete_07um_1600p.txrm<br> .ds-uct-007_asphalt_concrete_07um_1600p_Drift.txrm<br> .ds-uct-007_asphalt_concrete_07um_1600p_recon.txm<br> .ds-uct-007_asphalt_concrete_07um_DL.zip<br> .ds-uct-007_asphalt_concrete_07um_ML.zip<br> .ds-uct-007_asphalt_concrete_07um_TH32.zip<br> .ds-uct-007_asphalt_concrete_07um_WS.zip</p>
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
Hyperspectral X-ray CT data set of mineralised ore sample with Au and Pb deposits
<p><strong>General data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection data set of a mineralised ore sample with small gold and galena deposits. It was acquired in a laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The data included contains all the relevant files required for reconstruction, following a hyperspectral scan of a mineralised ore sample. The sample contains a number of mineral phases, of varying concentration, distributed throughout. Some phases (including gold, and lead-based Galena) produce unique absorption edges, which act as spectral identifiers that can be measured by an energy-sensitive detector.</p> <p><strong>File descriptions:</strong></p> <p>The data set consists of one .txt file and three .mat (MATLAB) data files.</p> <p>Au_rock_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections. The number of horizontal detector pixels accounts for the fact that a set of 5 tiled scans of the sample were collected and later stitched together.</p> <p>Au_rock_sinogram_full.mat contains the full 4D sinogram constructed following flat-field normalisation of the raw projection data. The data matrix contains the total number of energy channels acquired during scanning, as well as the conventional elements of vertical/horizontal detector pixel number and total projection angles.</p> <p>commonX.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed.</p>
Localizing Spherical Fiducials in C‐arm Based Cone‐Beam CT
<p>This dataset was acquired as part of the work described in: "Localizing spherical fiducials in C-arm based cone-beam CT", Z. Yaniv, Med. Phys., Vol. 36(11), pp. 4957-4966, 2009, <a href="https://doi.org/10.1118/1.3233684">doi.org/10.1118/1.3233684</a>.</p> <p>The data includes two phantom imaging studies acquired with a Cone-Beam CT (CBCT) system. Each of the two datasets includes projection images (cine loops) and 3D reconstructions. Additionally, the data includes the CBCT system's projection matrices and MATLAB code for reading the projection images and overlaying epipolar lines onto them. All images are stored in the DICOM format.</p> <p>The full MATLAB code for fiducial localization described in the manuscript is available <a href="https://www.yanivresearch.info/software/cbctSphericalFiducialLocalization.zip">here</a>.</p> <p> </p>
Raw X-Ray CT data of CFC-Cu ITER monoblock mock-up
<p>Raw X-Ray CT data for CFC-Cu ITER monoblock mock-up. The monoblock was manufactured at Politecnico di Torino, Italy (Dr Valentina Casalegno) and X-ray tomography scanning was performed at the Manchester X-ray Imaging Facility, University of Manchester, UK (Dr Llion Evans).</p> <p>This data was used for the publication Evans, Ll.M. et al. "Transient Thermal Finite Element Analysis of CFC-Cu ITER Monoblock Using X-ray Tomography Data", Fusion Engineering and Design 2015. DOI: 10.1016/j.fusengdes.2015.04.048</p>
A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs - Underlying CT data
<p>Underlying CT data of <strong>"A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs"</strong></p> <p>Paola Zgouro1, Orestis L. Katsamenis3,4, Thomas Moschakis5, Georgios K. Eleftheriadis6, Athanasios S. Kyriakidis6, Konstantina Chachlioutaki1,2, Paraskevi Kyriaki Monou1,2, Marianna Ntorkou7, Constantinos K. Zacharis7, Nikolaos Bouropoulos8,9, Dimitrios G. Fatouros1,2, Christina Karavasili1, Christos I. Gioumouxouzis1</p> <p><em>1 Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece</em><br><em>2 Center for Interdisciplinary Research and Innovation (CIRI-AUTH), 57001 Thessaloniki, Greece</em><br><em>3 μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</em><br><em>4 Institute for Life Sciences, University of Southampton, University Rd, Highfield, Southampton, SO17 1BJ, UK</em><br><em>5 Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, GR-541 24 Thessaloniki, Greece</em><br><em>6 Pharmacare Premium Limited, R&D Department, HHF003 Hal Far Industrial Estate, Birzebbugia BBG3000, Malta</em><br><em>7 Laboratory of Pharmaceutical Analysis, Department of Pharmacy, Aristotle University of Thessaloniki, GR-54124, Greece</em><br><em>8 Department of Materials Science, University of Patras, 26504 Rio, Patras, Greece</em><br><em>9 Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, Patras, Greece</em></p> <p><strong>Microfocus Computed Tomography (μCT)</strong></p> <p>X-ray microfocus computed tomography (μCT) was employed for the characterization of the microstructure of the printed object, assessing the overall volume, porosity, local thickness and other printing defects. The imaging took place at the University of Southampton’s μ-VIS X-ray Imaging Centre (<a title="&mu;-VIS X-ray Imaging Centre at the University of Southampton" href="https://www.muvis.org" target="_blank" rel="noopener">www.muvis.org</a>) / 3D X-ray Histology facility using a customized μCT scanner optimized for 3D X-ray histology (<a title="3D X-ray Histology facility at University of Southampton" href="https://www.xrayhistology.org" target="_blank" rel="noopener">www.xrayhistology.org</a>) (<a title="A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications" href="https://doi.org/10.12688/wellcomeopenres.19666.2" target="_blank" rel="noopener">Katsamenis et al., 2023</a>) based on Nikon’s XTH225ST system (Nikon Metrology, Castle Donington, UK). The scanner was operated at 110 kVp / 90 μA (9.9 W), with the X-ray beam prefiltered using 0.04 mm of aluminum. The source-to-object and source-to-detector distances were 28.4 mm and 1136.7 mm, respectively, resulting in a magnification factor of 40x. Acquisition parameters included 2201 projections, averaging 4 frames per projection, with an exposure time of 177 ms per projection. The 2850 x 2850 dexels detector was binned 2x (virtual detector: 1425 × 1425 dexels), resulting in an isotropic voxel edge of 7.5 μm. The reconstructed data underwent visualization and analysis using Dragonfly software (Comet Technologies Canada Inc.; software available at http://www.theobjects.com/dragonfly).</p>
Supporting data for "Unveiling Vertebrate Development Dynamics in Frog Xenopus laevis using Micro-CT Imaging"
<p>The dataset contains X-ray Micro Computed Tomography data of Xenopus laevis frog. There are twenty datasets of ten individual animals. Each animal was CT scanned twice – once as a native scan to visualize the hard tissues, and once contrast-stained to visualize the soft tissues. The datasets include nine developmental stages (NF44-45, NF52, NF53, NF54, NF57, NF59, NF62, NF66 and adult). There are two adults, one male and one female. The CT data (in 8bit .tiff format compressed as .tar.gz files) are supported by .stl files created from each dataset. The database also includes .stl files of selected structures of interest (body, skeleton, skull, brain and guts of individual animals).</p>
Covid-19 CT dataset for Body Part Regression Tutorial
<p>The dataset is a subset of CT scans from the <a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443">Covid-19-AR</a> dataset from the Cancer Image Archive. The data were converted from the DICOM file format to the NifTI file format for better and easier handling. The converted dataset was created for a tutorial of the <a href="https://github.com/MIC-DKFZ/BodyPartRegression">bpreg</a> python package.</p> <p>Acknowledgment:<br> The dataset was funded with federal funds from the National Center for Advancing Translational Sciences UL1 TR003107 and the National Cancer Institute, Contract No. 75N91019D00024, Subcontract 20X023F. </p>
Multi-resolution X-Ray micro-CT images of Bentheimer Sandstones
<p>This dataset consists of multi-resolution X-Ray micro-tomography images of two Bentheimer sandstone rock cores. The rock cores were first used experimentally in [1] with further modelling in [2]. This new dataset is used directly in the publication [3] - preprint available at https://arxiv.org/abs/2111.01270. </p> <p>The original dataset from [1] (of the same rock cores) is hosted on the BGS National Geoscience Data Centre, ID #130625 at dx.doi.org/10.5285/5f899de8-4085-4370-a45e-e613f27e8f1d and there is also a subvolume image dataset, for easier download available on the Digital Rocks Portal, project 229, DOI:10.17612/KT0B-SZ28 at digitalrocksportal.org/projects/229. </p> <p>The images provided herein are from two distinct Bentheimer rock cores -- core 1 and core 2. The cores have diameter, 12.35mm, lengths 73.2mm and 64.7mm, core-averaged porosities of 0.203 and 0.223 and permeabilities of 1.636D and 0.681D for core 1 and 2, respectively. Core 2 has a clear low permeability lamination occurring at 2/3 of the total core length, whereas core 1 has a general fining towards the outlet of the core creating a reduction in porosity [1].</p> <p>The images were acquired with a Zeiss Versa 510 X-Ray CT scanner. We acquired images of two sub volumes from each core, at locations 1/3rd (subvolume 1) and 2/3rds (subvolume 2) of the way along the core length, at resolutions of 2, 6 and 18 microns. We refer to the 2 micron images as high-resolution (HR), the 6 micron images as low-resolution (LR) and the 18 micron images as very-low-resolution (VLR). There are also super-resolution (SR) images created at 2 micron resolution from the LR images, using a deep-learning algorithm. There are also cubic interpolation images created from the LR image - these are labels bicubic. These have a resolution of 2 microns, and size equal to the HR and SR images. Details of the SR and LR Bicubic generation are found in [3]. The following scanning protocols were used for the direct imaging:</p> <p>2 micron images:<br> --We use a 4x microscope objective, an exposure time of 8s, 2x averaged binning, 9001 projections, a scan voltage of 80kV and a power of 7W. Each scan takes approximately 24 hours.</p> <p>6 micron images:<br> --We use a flat panel detector, an exposure time of 0.7s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 14.46 degrees and the fan angle is 22.2 degrees. Each scan takes approximately 1 hour.</p> <p>18 micron images:<br> --We use a 0.4x microscope objective, an exposure time of 1s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 12.65 degrees and the fan angle is 12.65 degrees. Each scan takes approximately 2 hours.</p> <p>We present 4 sets of the images with different levels of processing. All images are mutual registered to each other. Each image filename has a Core#_Subvol#_resolution identifier, either with the actual resolution (e.g. 6) or the short form (e.g. LR). The following name endings are used</p> <p>(1) - '_16bit_LE.raw'. These are the .raw images of little-endian format. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(2) - '_16bit_LE_normalised.raw'. These are the .raw images of little-endian format with normalised greyscale values following the procedure in [1]. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(3) - 'Core1_Subvol1_HR' etc. These are the .tiff images of (2) above, which have been converted to 8 bit. Includes bicubic interpolation images and SR images, but no 16 micron images, since these were not used in the analysis of [3]. 16 images in total. </p> <p>(4) - 'Core1_Subvol1_HR_filtered' etc. These are the .tiff images from (3) above, which have filtered using non-local means filtering. More details are found in [3]. Note there are no SR images here since they are already essentially filtered, and included in (3) above. 12 images in total.</p> <p><br> <strong>References</strong><br> <br> [1] Jackson, S.J., Lin, Q. and Krevor, S. 2020. Representative Elementary Volumes, Hysteresis, and Heterogeneity in Multiphase Flow from the Pore to Continuum Scale. Water Resources Research, 56(6), e2019WR026396</p> <p>[2] Zahasky, C., Jackson, S.J., Lin, Q., and Krevor, S. 2020. Pore network model predictions of Darcy‐scale multiphase flow heterogeneity validated by experiments. Water Resources Research, 56(6), e e2019WR026708.</p> <p>[3] Jackson, S.J, Niu, Y., Manoorkar, S., Mostaghimi, P. and Armstrong, R.T. 2021. Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling. Under review, preprint available at https://arxiv.org/abs/2111.01270 </p>
Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot
<p>Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot</p> <blockquote> <p><strong>X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil </strong>- [<a href="https://doi.org/10.1002/ppj2.20036">https://doi.org/10.1002/ppj2.20036</a>]</p> </blockquote> <p>The ZIP file contains:</p> <ul> <li><code>MCT1_Rcode.R</code> - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT1... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>MCT2_Rcode.R</code> - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT2... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>R_RooThProcessing.R</code> - R code for aggregating root traits from RooTh software.</li> <li><code>Modelling folder</code> - OpenSimRoot with model parameters and root data used in manuscript.</li> </ul>
Effects of Mouthrinsing and Gargling to CT Values of SARS CoV-2 DATASET
<p><strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value.</p> <p><strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5.</p> <p><strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5.</p> <p><strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.</p> <p><strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value.</p> <p><strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5.</p> <p><strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5.</p> <p><strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.</p>
Multimodal Dataset of 3D point clouds and CT-volumes
<p>The multimodal dataset for evaluating algorithms for aligning CT volumes and point clouds which is presented in 'Multimodal registration across 3D point clouds and CT-volumes'. (Saiti, E., and T. Theoharis. "Multimodal registration across 3D point clouds and CT-volumes." <em>Computers & Graphics</em> 106 (2022): 259-266.) The multimodal dataset consistsof real micro-CT scans and their synthetically generated 3D models (point clouds) .</p>
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data
<p>The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.</p> <p> </p>
Dual energy CT scan of ordinary objects
<p>Dual energy CT scan of ordinary objects: wires, pen, fruits (orange, avocado), pastery, bacon, butter, cheese.</p> <p>The purpose of these scans is to enable experimenting with CT scans using various kernels and iterative reconstructions. For instance, studying metal artifacts at different energies, material identification using dual energy index, examining the relation between reconstruction kernel sharpness, iterative reconstruction strength and noise.</p> <p>The dataset also can be used to set up mock trials, e.g. where readers have to choose the sharpest image, or the one with least disturbing metal artifacts. Similarly, it could serve debug purposes, e.g. testing the workflow, DICOM readers, etc.</p> <p>Zenodo-get ( https://doi.org/10.5281/zenodo.1261812 ) could be used to download the whole record at once.</p>
CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample
<h3><strong>Dataset Description</strong></h3> <p>This dataset contains high-resolution CT-scan images that capture the residual fracture surface within a core sample of Molasse de Villarlod Sandstone. The core sample was extracted after conducting a radial hydraulic fracture experiment on a 25 × 25 × 25 cm cubic block of sandstone (M03 sample). The experiment was designed to simulate fluid-driven fracture propagation and closure, and the resulting fracture path was preserved in the core sample.</p> <p><strong>Core Location in the M03 Cube Sample:</strong></p> <ul> <li><strong>Z:</strong> 12.5 cm</li> <li><strong>South-North:</strong> 12.5 cm</li> <li><strong>West-East:</strong> 11.5 cm to 1.36 cm (Coring direction)</li> </ul> <p>This spatial information specifies the exact location and orientation of the core extraction within the M03 cube sample.</p> <h4><strong>CT-scan instrument details:</strong></h4> <p>The M03 sample was analyzed using an X-ray micro-CT scanner (RX-Solutions Ultratom) under consistent scanning protocols and parameters. A reflective 230 kV microfocus X-ray source (Hamamatsu L10801) equipped with a 0.2 mm thick copper filter, a tungsten cathode, and a tungsten target was employed for the imaging process. The scans were conducted with a voltage of 120 kV and a current intensity of 80 mA.</p> <p>The volume data acquisition was performed in continuous helical mode, ensuring complete coverage of the sample’s height. For sample M03, 6 full rotations were executed, with 1312 projections captured for each 360° rotation, allowing for highly precise volume reconstruction. The X-ray beam attenuation was recorded by an XL Varex Paxscan 2530HE plane detector with a resolution of 2176 x 1792 pixels, and an exposure time of 0.50 seconds per projection.</p> <p>The acquired projections were processed using RX-Solutions X-act software with Filtered Backprojection to reconstruct a corrected volume. This reconstruction yielded approximately 9000 slices in 16-bit TIFF format, with voxel dimensions of 10 x 10 x 10 microns, providing detailed insights into the internal structure of the sample.</p> <h4><strong>Key Features:</strong></h4> <ul> <li> <p><strong>Fracture Characteristics</strong>: The fracture observed in the CT-scans represents a residual opening that remains post-fracturation. It is entirely contained within the core, showcasing the internal fracture geometry resulting from the hydraulic fracturing process.</p> </li> <li> <p><strong>CT-Scan Details</strong>: The CT-scans were taken perpendicular to the fracture surface, offering a detailed cross-sectional view of the fracture at different depths. This orientation is critical for accurately capturing the fracture morphology and allows for the reconstruction of the fracture surface in 3D.</p> </li> <li> <p><strong>Material Information</strong>: The core sample is composed of Molasse de Villarlod Sandstone, a sedimentary rock which is porous (18% porosity) and permeable. This material choice is relevant for studying fracture closure subjected to the leak-off of the fluid inside the porous medium.</p> </li> <li> <p><strong>Experimental Context</strong>: The radial hydraulic fracture experiment aimed to simulate the propagation of hydraulic fracture and its closure due to the leakage of fluid inside fracture into the porous medium. The dataset provides valuable insights into fracture propagation patterns, surface roughness, and the effects of fluid-driven fractures in porous media.</p> </li> </ul> <h4><strong>Applications:</strong></h4> <p>This dataset is particularly valuable for researchers and engineers involved in:</p> <ul> <li>Fracture mechanics and surface characterization</li> <li>3D reconstruction and visualization of fracture surfaces</li> <li>Surface roughness analysis</li> <li>Hydraulic fracturing studies</li> <li>Geomechanical modeling</li> </ul> <h4><strong>File Structure:</strong></h4> <p>The dataset is organized into zip-folder contains .tif images corresponding to different depths within the core. Each tif-image is a CT-scan for that specific depth, labeled according to their position along the fracture path.</p> <h4><strong>Processing code:</strong></h4> <p>Follow the <strong>URL repository</strong> in the software section to access to the code for processing these images and reconstructing the fracture surfaces.</p> <p><strong>Acknowledgment:</strong></p> <p>We would like to extend our deepest thanks to Gary Perrenoud, Albert Taureg, and Lionel Pittet, the technical specialists of the PIXE platform at École Polytechnique Fédérale de Lausanne (EPFL). Their expertise and support in operating the CT-scan machine were important to the success of this research. We greatly appreciate their dedication and the high-quality work they provided.</p> <p><strong>Contact and Support:</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
CT-OCR-2022 (scroll001)
<p><strong>CT-OCR-2022 dataset</strong></p> <p>CT-OCR-2022 dataset contains optically scanned images for source paper document, 400 X-ray projections, 2687 CT-reconstructed cross-sections and segmentation markups for 6 model objects.</p> <p>Description of the data for each model object is presented in the table.</p> <table> <tbody><tr> <th>Files</th> <th>Data description</th> </tr> </tbody><tbody> <tr> <td>2022-ICMV-CT-OCR.[package].png</td> <td>sample projection and slice visualization</td> </tr> <tr> <td>[package].proj_src/proj_s_.log</td> <td>X-ray measurement log file</td> </tr> <tr> <td>[package].proj_src/*.tif</td> <td>preprocessed projections before rotation axe correction</td> </tr> <tr> <td>2022-ICMV-CT-OCR.[package].png</td> <td>package single projection and slice vizualization</td> </tr> <tr> <td>[package].proj_src/*.tif</td> <td>preprocessed projections before rotation axe correction</td> </tr> <tr> <td>[package].proj_norm/proj_s_.log</td> <td>X-ray measurement and geometry correction log file</td> </tr> <tr> <td>[package].proj_norm/*.tif</td> <td>preprocessed projections after rotation axe correction</td> </tr> <tr> <td>[package].rec_XXXX/metadata.json</td> <td>reconstruction metadata</td> </tr> <tr> <td>[package].rec_XXXX/*.tif</td> <td>CT-reconstructed volume, slices with size XXXX×XXXX</td> </tr> <tr> <td>[package].seg_XXXX/*.tif.seg.png</td> <td>segmentation markup</td> </tr> <tr> <td>[package].blank.png</td> <td>sample croped from pdf</td> </tr> <tr> <td>[package].scan.png</td> <td>sample croped from scanned image</td> </tr> </tbody> </table> <p>Due to the large amount of data, folders were packed into multi-volume zip-archives. Dataset published in Zenodo service in several linked repositories.</p> <p>scroll01 - <a href="https://doi.org/10.5281/zenodo.7123495">10.5281/zenodo.7123495</a><br>scroll02 - <a href="https://doi.org/10.5281/zenodo.7157600">10.5281/zenodo.7157600</a><br>scroll03 - <a href="https://doi.org/10.5281/zenodo.7157610">10.5281/zenodo.7157610</a><br>scroll04 - <a href="https://doi.org/10.5281/zenodo.7161350">10.5281/zenodo.7161350</a><br>folded01 - <a href="https://doi.org/10.5281/zenodo.7162001">10.5281/zenodo.7162001</a>, <a href="https://doi.org/10.5281/zenodo.7164152">10.5281/zenodo.7164152</a><br>folded02 - <a href="https://doi.org/10.5281/zenodo.7267141">10.5281/zenodo.7267141</a>, <a href="https://doi.org/10.5281/zenodo.7272064">10.5281/zenodo.7272064</a></p> <p>Any questions, complaints, etc. can be directed to: polevoy@smartengines.com (Dmitry Polevoy)</p> <p><strong>Share and Cite:</strong></p> <p>D. V. Polevoy, P. A. Kulagin, A. S. Ingacheva, Zh. V. Soldatova, M. V. Chukalina, D. P. Nikolaev, V. V. Arlazarov, "From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence," Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010P (7 June 2023); https://doi.org/10.1117/12.2680132</p> <p>in BibTex format:</p> <p>@inproceedings{10.1117/12.2680132,<br>author = {D. V. Polevoy and P. A. Kulagin and A. S. Ingacheva and Zh. V. Soldatova and M. V. Chukalina and D. P. Nikolaev and V. V. Arlazarov},<br>title = {{From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence}},<br>volume = {12701},<br>booktitle = {Fifteenth International Conference on Machine Vision (ICMV 2022)},<br>editor = {Wolfgang Osten and Dmitry P. Nikolaev and Jianhong (Jessica) Zhou},<br>organization = {International Society for Optics and Photonics},<br>publisher = {SPIE},<br>pages = {127010P},<br>keywords = {virtual unrolling, virtual unwrapping, digital unfolding, computational flattening, computed tomography, non-destructive analysis, open dataset},<br>year = {2023},<br>doi = {10.1117/12.2680132},<br>URL = {https://doi.org/10.1117/12.2680132}<br>}</p> <p><strong>See also</strong></p> <p>P. A. Kulagin, D. V. Polevoy, M. V. Chukalina, D. P. Nikolaev and V. V. Arlazarov, “Fully automatic virtual unwrapping method for documents imaged by X-ray tomography,” Proc. ICDAR 2024, to be published.</p> <p><a href="https://github.com/SmartEngines/virtual-unwrapping-article-code">https://github.com/SmartEngines/virtual-unwrapping-article-code</a></p>
Optimization of abdominal CT based on a model of total risk minimization by putting radiation risk in perspective with imaging benefit
<p><span>Population of one million cases simulating a liver cancer scenario. The demographic information was taken from the USA 2019 Census Population Estimates by Age, Sex, Race, and Hispanic Origin</span><span>. The patient population was simulated in eight different cohorts with genders of male and female, and races/ethnicities of white, Black, Hispanic, and Asian. The total population sample size allowed the inclusion of a significant number of cases in each group. For each simulated patient, the age was randomly sampled from a uniform distribution, which spanned the age range of the individual demographic groups. Radiation risk, clinical risk, and total imaging procedure risk were also calculated.<br></span></p>
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.