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2,543 results for “Tomography”
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 8-bit Sub-Volumes
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of each dataset is 500x1000x1000. Below is a summary of the pixel-sizes and associated datasets on Zenodo.</p> <blockquote> <p>Key:</p> <ul> <li>160695 = 0.3125 Micron = https://zenodo.org/records/13327692</li> <li>169066 = 0.8125 Micron = https://zenodo.org/records/13327682</li> <li>169067 = 1.625 Micron = https://zenodo.org/records/13327651</li> <li>169068 = 2.6 Micron = https://zenodo.org/records/12206815</li> </ul> </blockquote> <p>The purpose of this dataset is to provide an easy to download sub-volumes of the larger (>50GB) datasets in the above Zenodo entries.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
3D Tomography Images of wheat grains for several development stages
<p>Images of wheat grains acquired by 3D tomography at various stages of the early development of the grain. This data set serves as companion for the article "Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development" submitted to the "Plant Methods" journal.</p> <p><strong>Grain samples</strong></p> <p>A total of 41grains obtained at height different stages was imaged. The files correspond to the collections of grains at each stage:</p> <ul> <li>060 degree-days: 5 grains</li> <li>080 degree-days: 5 grains</li> <li>100 degree-days: 5 grains</li> <li>120 degree-days: 5 grains</li> <li>180 degree-days: 6 grains</li> <li>210 degree-days: 5 grains</li> <li>270 degree-days: 5 grains</li> <li>310 degree-days: 5 grains</li> </ul> <p>A more detailed description is provided in the file "<a href="https://zenodo.org/api/files/923a7508-b325-4264-8553-6a62092bb84d/wheatGrainTomoDataset.pdf">wheatGrainTomoDataset.pdf</a>".</p> <p><strong>Image format</strong></p> <p>All images are in TIFF format.</p> <p>Two kinds of images are provided: the volumes of the whole grains after conversion tu 256 gray levels, and the results of the segmentation of the grains as described in the manuscript. </p>
Slice-by-Slice X-ray Tomography dataset of Dog Toy
<p>This submission contains a dataset used in the paper</p> <p>"Ajinkya Kadu, Felix Lucka, and K. Joost Batenburg. "Single-shot Tomography of Discrete Dynamic Objects." <em>arXiv preprint <a href="https://arxiv.org/abs/2311.05269">arXiv:2311.05269</a></em> (2023)."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT). To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector, which results in 956 detector pixel with an effective length of 149.6 <strong>μ</strong>m each. Between source and detector there is a rotation stage, upon which the sample was mounted. The sample that we imaged was a dog toy in a shape of a bone made of a rubber. The X-ray tube voltage was 90kV and a copper filter was used to block the low-energy part of the spectrum to limit beam-hardening artifacts. The source-to-detector distance was 487.9 mm, while the source-to-origin of the sample was 374.5 mm in a fan-beam geometry. We acquired 673 z-slices with 0.25 mm distance between slices. Further information about the technical details of X-ray CT can be found in the <a href="https://arxiv.org/abs/2311.05269">above paper</a> and in </p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>The upload consists of two files, namely:</p> <ol> <li>GrayBone90kV4Filter.zip: contains the raw measurement data.</li> <li>GrayBone90kV4FilterPreprocessed.mat: contains preprocessed data to be used in the MATLAB script provided to do pseudo-dynamic tomography. It also contains reference reconstruction obtained via Filtered Back Projection (FBP) algorithm. </li> </ol> <p>In the Github repository <a href="https://github.com/ajinkyakadu/DynamicXRayCT">https://github.com/ajinkyakadu/DynamicXRayCT</a>, we provide the scripts to read and process the raw data. The Github repository also contains all the scripts to reconstruct the dynamic solution using advanced algorithms. Furthermore, the raw data formats are described in great details in <a href="https://www.nature.com/articles/s41597-023-02484-6">Kiss et al 2023</a> paper referenced above. </p>
Electrical Resistivity Tomography (ERT) datasets from the Otemma glacier forefield and outwash plain
<p><strong>Electrical Resistivity Tomography (ERT) datasets collected in the Otemma forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup> and James Irving<sup>3</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p><sup>3</sup> Institute of Earth Sciences (ISTE), University of Lausanne, 1015 Lausanne, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>Electrical Resistivity Tomography (ERT) profiles were collected around the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). All data were collected with a <a href="http://www.iris-instruments.com/syscal-pro.html">Syscal Pro</a> Switch 48 from Iris Instruments, using an array of maximum 48 electrodes with a spacing between 1 and 10 meters. For each line, measurements were performed using a Dipole-Dipole (dd) and a Wenner-Schlumberger (ws) electrode configuration.</p> <p>All ERT lines locations can be visualized in <em><strong>ERT_map_lines_2019-2021.jpg</strong>.</em></p> <p>A result overview can be vizualized in <em><strong>ERT_allResults_3Doverview.png</strong>.</em></p> <p><strong>Data Structure</strong></p> <p>Two <a href="https://jupyter.org/">Juypter Notebook</a> files are provided and can be used to reproduce all inversion analyses.</p> <ul> <li><em><strong>1_createInput_prosys_to_pygimli.ipynb</strong></em> : Transforms the raw data from Syscal Pro (exported with <a href="http://www.iris-instruments.com/download.html">ProsysII</a> software as .csv) to a processed .dat file formated for inversion using the <a href="https://www.pygimli.org/">pyGIMLi</a> library.</li> <li><em><strong>2_ERT_inversion.ipynb</strong></em> : Reads the processed .dat file and performs a 2D robust inversion for a set of regularization parameters for the selected line.</li> </ul> <p>In <strong>ERT_data.zip</strong>, 3 folders with similar structure contain all data for year 2019, 2020 and 2021. Each folder contains :</p> <ol> <li><strong>GPS </strong>: folder with electrodes coordinates for each ERT line</li> <li><strong>inputGiMLi</strong> <ul> <li><strong>prosys_csv</strong>: contains the raw field measurements (downloaded from the Syscal device using ProsysII)</li> <li><strong>input_ERT </strong>: stores the processed .dat file. (created from notebook 1)</li> <li><strong>results_lambda</strong> : contains a .png image with the inversion results using different values of the regularization parameter lambda used to assess the sensitivity of the inversion results (over/underfitting). Analysis is performed for each line and each electrode configuration (dd or ws). (created from notebook 2)</li> <li><strong>results_final</strong> : contains a .png image with the final inversion results for each line and electrode configuration (dd or ws) using the optimal lambda parameter only (all arrays are shown from East to West). (created from notebook 2)</li> <li><strong>vtk</strong> : contains a .vtk file for each final results for 3D vizualization in the <a href="https://www.paraview.org/">Paraview</a> software.</li> </ul> </li> <li><strong><em>ERT_line_description_yyyy.csv</em> </strong>: a file describing the ERT arrays characteristics (read in notebook 1 and 2)</li> </ol> <p>The <strong>results </strong>folder contains :</p> <ul> <li><strong>ERT_3Dview_paraview</strong> folder : contains Paraview state files (.pvsm) for 3D vizualization of all results, as well as image files.</li> <li><em><strong>ERT_results_all.pdf</strong></em> : A summary of all final results for all years (similar content as <em>ERT/inputGiMLi</em><strong>/</strong><em>results_final</em> folders)</li> <li><em><strong>ERT_results_bedrock.pdf</strong></em> : Contains the vizualization of specific ERT profiles in the outwash plain and their field location . The separation between a surface layer of water-saturated sediments (resistivity <2500 Ωm) and the underlying bedrock is delimited. The likely presence of buried ice blocks (isolated blocks with resistivity >5000-10000 Ωm) is also highlighted.</li> <li><em><strong>ERT_timelapse_salt_tracer.gif</strong></em> : results of a time-lapse ERT measurement performed on 9 August 2019 to track the movement of a salt plume injected at 06 am, 9.38 meters upslope (see paper by<em> Müller et al., 2022</em> for detailed analysis). The tracer starts to appear at 12:45 at a distance of 30m on the array. Minimum resistivity is reached at between 16:45 and 17:45.</li> </ul>
Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'
<p>This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5.</p> <p>Each file can be opened with numpy.load(filename)</p> <p>The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number.</p> <p>The README file inside provides a detailled overview of the files included.</p>
Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples
<p>The repository contains cross-sections of 83 wood samples derived from X-ray computed tomography (CT) data. The samples are a part of the LEIZA reference collection, which were created within the framework of the project "Mass Finds in Archaeological Collections", which was funded by the "Kulturstiftung des Bundes" and the "Kulturstiftung der Länder" from 15.04.2008 to 31.12.2011 as part of the "Program for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur).</p> <p>Around 10 years later, during the CuTAWAY project (ConservaTion And Wod AnalYses), the wood samples were digitized using an in-house laboratory X-ray CT system (Diondo d2, Germany) at HSLU with a nominal voxel size between 27 and 44 μm in order to analyse the structure of the interior. You can download the cross-sectional images of the data here. The 3D data acquisition was carried out during November 2019 - April 2021.</p> <p>The CuTAWAY project was funded by the German Research Association (DFG) and the Swiss National Science Foundation (SNSF) from 2019 to 2023 (CuTAWAY - Conservation and Wood Analyses, DFG - 416877131 and SNSF - 200021E_183684).</p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322#</p> <p> </p> <p>The size of the .h5 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 > 43334_raw.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 1.625 microns.</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 .h5 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 > 169067_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size
<p>This repository contains processed data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 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 > 43334_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.325 microns.</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 .h5 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 > 169065_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.8125 microns.</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 .h5 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_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 2.6 microns.</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 .h5 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 > 169068_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-23219-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed exemplarily in DOI: 10.1038/s41467-018-03115-0 (Fig. 6a "Se+Na2Se treatment") by Torsten Schwarz and coworkers.</p> <p>2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.<br> The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><br> <strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><br> <strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
Phase unwrapping using Deep Learning in Holographic Tomography - dataset.
<p>This dataset contains two types of data: phase images and trained model files.</p> <ol> <li> <p><strong>Real phase images</strong> - these phase images are contained with the files named with the prefix "real_". The type of the data files is "<strong>.npz</strong>", to be loaded with NumPy (<em>np.load()</em>), as a dictionary. The data is stored within the key ["<em>arr_0</em>"]. The images depict cells [1], organoids [2], phantoms [3-4] and regular 3D printed structures with high scattering properties [5]. The images have been augmented in order to expand the volume of the training dataset. All images are of shape (<em>256,256,1</em>). It is a big dataset containing 27,189 images of each type for training the unwrapping model:</p> <ul> <li> <p>unwrapped - continuous phase distribution (<em>float32</em>)</p> </li> <li> <p>wrapped - phase wrapped into mod2<span class="math-tex">\(\pi\)</span> (<em>float32</em>)</p> </li> <li> <p>wrapcount - wrap count phase maps coded in the integer form (0,1,2...) (<em>uint8</em>)</p> </li> </ul> </li> <li> <p><strong>Synthetic phase images</strong> - phase images in these files were generated algorithmically in the MATLAB programming language. The files containing this dataset have a prefix "synthetic_". The type of the data files is "<strong>.npz</strong>", to be loaded with NumPy (<em>np.load()</em>), as a dictionary. The data is stored within the key ["<em>arr_0</em>"]. Phase images contained in the synthetic dataset can be split into 3 types by their type: spherical distribution, simulated cells w/ spherical background and simulated cells w/ introduced linear tilt. All images are of shape (<em>256,256,1</em>). This dataset contains 10,000 images of each type for training the unwrapping and denoising models:</p> <ul> <li> <p>unwrapped - continuous phase distribution (<em>float32</em>)</p> </li> <li> <p>wrapped - phase wrapped into mod2<span class="math-tex">\(\pi\)</span> (<em>float32</em>)</p> </li> <li> <p>wrapcount - wrap count phase maps coded in the integer form (0,1,2...) (<em>uint8</em>)</p> </li> <li> <p>noised - wrapped phase images w/ synthetic noise (<em>float32</em>).</p> </li> </ul> </li> <li> <p><strong>Trained models</strong> - trained model files. These model files are in the format "<strong>.h5</strong>", which contains the model architecture and the weights. They have been developed and saved with the <em>keras</em> library, and are loaded with the <em>keras.models.load_model()</em> function. The models list:</p> <ul> <li> <p><em>Unet_Denoising.h5 </em>- U-Net model used for denoising as an image translation task. The input is a wrapped phase image with noise and the output is the same wrapped phase distribution, but denoised. Model is trained on the synthetic phase dataset.</p> </li> <li> <p><em>Attn_Unet_Unwrapping.h5 </em>- U-Net model with Attention Gates and Residual Blocks trained for the semantic segmentation task. The input of the model is the wrapped phase image and its output is the wrap count map. Model is trained on the real phase dataset.</p> </li> </ul> </li> </ol> <p> </p> <p> </p> <p>[1] M. Baczewska, W. Krauze, A. Kuś, P. Stępień, K. Tokarska, K. Zukowski, E. Malinowska, Z. Brzózka, and M. Kujawińska, “On-chip holographic tomography for quantifying refractive index changes of cells’ dynamics,” in Quantitative Phase Imaging VIII, vol. 11970 Y. Liu, G. Popescu, and Y. Park, eds., International Society for Optics and Photonics (SPIE, 2022), p. 1197008.<br> [2] P. Stępień, M. Ziemczonok, M. Kujawińska, M. Baczewska, L. Valenti, A. Cherubini, E. Casirati, and W. Krauze, “Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging,” Biomed. Opt. Express 13, 5709–5720 (2022).<br> [3] M. Ziemczonok, A. Kuś, P. Wasylczyk, and M. Kujawińska, “3d-printed biological cell phantom for testing 3d quantitative phase imaging systems,” Sci. Reports 9, 1–9 (2019).<br> [4] M. Ziemczonok, A. Kuś, and M. Kujawińska, “Optical diffraction tomography meets metrology — measurement accuracy on cellular and subcellular level,” Measurement 195, 111106 (2022).<br> [5] W. Krauze, A. Kuś, M. Ziemczonok, M. Haimowitz, S. Chowdhury, and M. Kujawińska, “3d scattering microphantom sample to assess quantitative accuracy in tomographic phase microscopy techniques,” Sci. Reports 12, 1–9 (2022).</p>
STEMPO - dynamic X-ray tomography phantom
<p>The Spatio-TEmporal Motor-Powered (<strong>STEMPO</strong>) phantom is a physical phantom designed for collecting dynamic X-ray tomography data. The dynamic part of the phantom is computer controlled allowing for wide variety of different measurements and sampling setups to be used. The primary goal is to help mathematical community test and validate novel dynamic tomography reconstruction methods.</p> <p>Detailed documentation of the phantom, the included data (volume 1 only) and some examples can be found on the related publication: <a href="https://doi.org/10.1007/978-981-97-6769-4_1">https://doi.org/10.1007/978-981-97-6769-4_1</a> (available as an arXiv preprint: <a href="http://arxiv.org/abs/2209.12471">http://arxiv.org/abs/2209.12471</a>).</p> <p>This data set can be appended with new data in the future. Current version (<strong>1.2</strong>) includes<br> <strong>Data - vol.1 (v1.0)</strong></p> <ul> <li>stempo_static_2d_b*.mat</li> <li>stempo_static_3d_b*.mat</li> <li>stempo_cont360_2d_b*.mat</li> <li>stempo_cont360_3d_b*.mat</li> <li>stempo_seq8x45_2d_b*.mat</li> <li>stempo_seq8x45_3d_b*.mat</li> <li>stempo_data_geometries.csv</li> </ul> <p> <strong>Data - vol.2 (added in v1.2)</strong></p> <ul> <li>stempo_seq8x180_2d_b*.mat</li> <li>stempo_seq8x180_3d_b*.mat</li> </ul> <p>where b* denotes downsampling or binning of the data by a factor of (4, 8, 16 or 32). These are 2D and 3D data collected from a <em>static</em> object for reference, or from a dynamic target in a <em>continuous</em> 360 projection scan or <em>sequence</em> of 8 rotations, each consisting of 45 or 180 projections (with <strong>seq8x45</strong> and <strong>seq8x180</strong> data respectively). Finally stempo_data_geometries.csv is a simple table containing the key parameters of the measurement geometry in text format. Note that the height of the phantom for volume 2 data is slightly different compared to volume 1, including the static scan (mostly relevant for comparing 3D reconstructions).</p> <p>In addition the data set contains</p> <p> <strong>Additional files</strong></p> <ul> <li>stempo_ground_truth_2d_b4.mat</li> </ul> <p>which is an approximation of the true motion obtained from a single static FBP reconstruction which has been interpolated to match the location of the moving block during the <em>cont360</em> and <em>seq8x45</em> scans. Finally there are</p> <p> <strong>Example algorithms</strong></p> <ul> <li>stempo_fbp_example.m</li> <li>stempo_fdk_example.m</li> <li>stempo_pdfp_wavelet_2d_example.m</li> <li>stempo_LplusS_2d_example.m</li> </ul> <p>which are short example algorithms of well know analytic (<em>FBP</em> and <em>FDK</em>) and iterative methods. <em>stempo_pdfp_wavelet_2d.m<strong> </strong></em>uses variational regularization and wavelet transform of the 2D + time object to reach a suitable solution. The codes are adapted from [<a href="https://doi.org/10.1088/1361-6501/aa9260">1</a>,<a href="https://doi.org/10.1088/1361-6420/ab9c15">2</a>]. <em>stempo_LplusS_2d_example.m</em> attempts to split the reconstruction into low-rank component <em>L</em> and a sparse dynamic component <em>S</em>. This code is adapted from [<a href="https://doi.org/10.1002/mrm.25240">3</a>]. These are meant to give users ideas how the data can be used in different applications to match the requirements of different methods.</p> <p>Easiest way to utilize the data is with the <a href="http://www.astra-toolbox.com/">ASTRA Toolbox</a> and the <a href="https://github.com/Diagonalizable/HelTomo">HelTomo Toolbox</a>. Some of the example codes also require <a href="https://www.cs.ubc.ca/labs/scl/spot/">Spot Linear Operator Toolbox</a> (highly recommended) and the Wavelet Toolbox. However none of these are mandatory and any method (including programming languages other than MATLAB) are fine as long as the measurement geometry is respected.</p> <p><br>The author is supported by the Emil Aaltonen Foundation junior researcher grant no. 200029 and the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. The author also acknowledges the support of Academy of Finland through the Finnish Centre of Excellence in Inverse Modelling and Imaging 2018–2025, decision number 312339. Finally the author would like to thank E. Heikkilä, T. Heikkilä, A. Meaney and F.S. Moura for all their technical expertise and help in developing, building and imaging the mechanism.</p> <p>The author also thanks O. Tapaninen for helping measure the data for <strong>vol.2</strong>.</p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Gravity modeling of the Alpine lithosphere affected by magmatism based on seismic tomography
<p>The Southern Alpine regions have been affected by several magmatic and volcanic events between the Paleozoic and the Tertiary. This activity has undoubtedly had an important effect on the density distribution and structural setting at lithosphere scale. Combining the information from gravity field and a high-resolution seismic tomography has been carried out a new 3D lithosphere density model of the Alpine region.</p>
DETOX seismic tomography models
<p>-----------------------<br> DETOX tomography models<br> -----------------------</p> <p>This folder contains three tomography models, DETOX-P1, DETOX-P2 and DETOX-P3, in the following formats: </p> <p>- NetCDF (dirname: grid_nc4)<br> - VTK (dirname: vtk)<br> - xyz-value (dirname: txt_tetrahedron)<br> - JPEG for GPLATES, only high-velocities (dirname: GPLATES)</p> <p>The directories are organized as follow:<br> <br> DETOX-P1<br> ├── GPLATES<br> ├── grid_nc4<br> ├── txt_tetrahedron<br> └── vtk<br> DETOX-P2<br> ├── GPLATES<br> ├── grid_nc4<br> ├── txt_tetrahedron<br> └── vtk<br> DETOX-P3<br> ├── GPLATES<br> ├── grid_nc4<br> ├── txt_tetrahedron<br> └── vtk</p> <p>---------------------</p> <p>Citation:</p> <p>* Kasra Hosseini, Karin Sigloch, Maria Tsekhmistrenko, Afsaneh Zaheri, Tarje Nissen-Meyer, Heiner Igel, Global mantle structure from multifrequency tomography using P, PP and P-diffracted waves, Geophysical Journal International, Volume 220, Issue 1, January 2020, Pages 96–141, https://doi.org/10.1093/gji/ggz394</p>
Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography
<p>We provide: the tomographic model for dephts 68 to 768 km as text files, where the first column is the longitude, the second is the latitude and the third if the velocity perturbation; the proposed limits for the Paranapanema Block, Luiz Alves Craton and Rio Apa Craton as a csv file (Figure 12 of the paper), where the first column is the name of the feature, the second is the longitude and the third is the latitude; and the abstract for the paper "Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography".</p>
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.