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190 results for “computational imaging”
Subjective human thresholds over computer generated images
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 3) was used to generate these images.</p> <p>By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view.</p> <p>The proposed image database is composed of the following files:</p> <ul> <li><strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones;</li> <li><strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view.</li> </ul> <p><em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em></p> <p><strong>Note:</strong> Some of the proposed scenes come from:</p> <ul> <li><a href="https://pbrt.org/scenes-v3">https://pbrt.org/scenes-v3</a></li> <li><a href="https://benedikt-bitterli.me/resources/">https://benedikt-bitterli.me/resources/</a></li> </ul> <p><strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.</p> <p> </p>
Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.</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>
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
<p>This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++. The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets. The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well. This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 . The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906.</p> <p>Format of the data:</p> <p>Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality. The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset. The pre-processing set is CassavaSlices. The output set for Soybean is SoybeanResultsJul11. The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C. _B is the largest, and only contains the results overwritten on the original X-Ray images. Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.</p> <p> </p> <p> </p><p> </p><p> </p> <p></p> <p></p>
EOL computer vision pipelines: Classification for Image Tagging: Flower Fruit
<p>Angiosperms: Stats from Colab:</p> <ul> <li>Number of positive identified reproductive structures: 490</li> <li>Number of possible identified reproductive structures: 4611</li> <li>Number of negative identified reproductive structures: 14833</li> </ul> <p> </p>
EOL computer vision pipelines: Classification for Image Tagging: Image Type: Anura
<p>Produced by the EOL Image Type Classifier. Classifies images as map, phylogeny, illustration, herbarium sheet, or none. Dataset generated for EOL Anura images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-vs-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
EOL computer vision pipelines: Classification for Image Tagging: Image Rating: Chiroptera
<p>Produced by the EOL Image Rating Classifier. Classifies images as bad or good quality (used for image gallery sorting). Dataset generated for EOL Chiroptera images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-or-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections
<p>Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. For detailed information concerning this dataset, please refer to our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2]. </p> <p>We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [3] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [4]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [5].</p> <p>We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [6,7], and the National Lung Screening Trial (NLST collection) [8,9]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [10] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data.</p> <p>IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the <em>dcmqi</em> [12], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using <em>dcmqi </em>and <em>highdicom</em> [13]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection. </p> <p>The dataset is available in IDC, and is accompanied by our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2]. This pre-print details how the data were generated, and how the resulting DICOM objects can be interpreted and used in tools. Additionally, for further information about how to interact with and explore the dataset, please refer to our <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/">repository</a> and accompanying <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/blob/main/usage_notebooks/scientific_data_paper_usage_notes.ipynb">Google Colaboratory notebook</a>. </p> <p>The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files. </p> <ul> <li>nsclc <ul> <li>2d-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_lowres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_fullres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_gt <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files. </p> <ul> <li>nlst <ul> <li>3d_fullres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>The query used for NSCLC-Radiomics is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NSCLC_Radiomics_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nsclc_radiomics_series_analyzed.csv">here</a>. The query used for NLST is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NLST_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nlst_series_analyzed.csv">here</a>. The two csv files that describe the series analyzed, <em>nsclc_series_analyzed.csv</em> and <em>nlst_series_analyzed.csv</em>, are also available as uploads to this repository. </p> <p><em>Version updates: </em></p> <p><em>Version 2: For the regions SR and landmarks SR, changed to use a distinct TrackingUniqueIdentifier for each MeasurementGroup. Also instead of using TargetRegion, changed to use FindingSite. Additionally for the landmarks SR, the TopographicalModifier was made a child of FindingSite instead of a sibling.</em></p> <p><em>Version 3: Added the two csv files that describe which series were analyzed </em></p> <p><em>Version 4: Modified the landmarks SR as the TopographicalModifier for the Kidney landmark (bottom) does not describe the landmark correctly. The Kidney landmark is the "first slice where both kidneys can be seen well." Instead, removed the use of the TopographicalModifier for that landmark. For the features SR, modified the units code for the Flatness and Elongation, as we incorrectly used mm units instead of no units. </em></p>
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
<p>We uploaded the raw data related to extracted features of the manuscript "Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11. PMID: 37697033."</p>
Computer Code; Raw Image_Mangoes
<p>Exporting mangoes to foreign countries while maintaining quality is a challenge for distributors. The quality and maturity of mangoes are inhomogeneous, even when mangoes are harvested from the same tree at the same time. While quality affects the product value at the time of harvest, maturity affects the product value over time after harvest. The maturity of mangoes also greatly affects the storage and transport time. This data is collected in Vietnam with many mango cultivars and used to train machine learning models for the automatic classify mango system.</p>
Data: Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography.
<p>This data set includes all the raw data collected for the following article: "Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography."</p>
Cone-Beam Computed Tomography Dataset of a Chicken Bone Imaged at 4 Different Dose Levels
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a chicken leg bone imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is a chicken bone obtained from a cooked chicken. The bone was boiled to remove soft tissues, after which it was left to dry in room temperature for several months to remove extra moisture. For the scan the sample was placed directly into the rotation stage and secured with a screw.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings</em></p> <p>The dataset consists of four different scans of the same sample. For each scan 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were:</p> <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> <p>The scans were made in sequence, proceeding from the lowest dose to the highest dose.</p> <p><em>Data Post-Processing</em></p> <p>Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Multienergy Fan Beam Computed Tomography Dataset of a Bird Chest Imaged with 3 Different X-ray Spectra
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection data of a biological imaging phantom (a bird chest) imaged in an X-ray microtomography scanner, using three different X-ray spectra. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters, as well as photographs and example reconstructions. The dataset is designed for use in algorithm development for multienergy computed tomography.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is the chest of a common quail (<em>Coturnix coturnix</em>) bird obtained frozen from a local supermarket. The chest section of the frozen bird was removed using a handsaw, and left to melt and settle in a sample holder before imaging.</p> <p><em>Scanner</em></p> <p>The measurement data were acquired using an X-ray microtomography scanner in the University of Helsinki Micro-CT Laboratory. The scanner uses cone beam geometry and it is equipped with an end-window tube with a tungsten target.</p> <p><em>Scan Settings</em></p> <p>The dataset consists of three consecutive scans made using identical geometry but different X-ray spectra and detector exposure times. For each scan, 720 X-ray projections were acquired using an angle increment of 0.5 degrees. Multiple frames were averaged for each projection in order to increase signal-to-noise ratio. The scan geometry and the energy-specific settings are summarized in the following two tables.</p> <p><strong>Table 1.</strong> Imaging geometry used for collecting the data.</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Focus-center distance</td> <td>252 mm</td> </tr> <tr> <td>Focus-detector distance</td> <td>420 mm</td> </tr> <tr> <td>Geometric magnification</td> <td>5/2</td> </tr> <tr> <td>Detector pixel size</td> <td>0.200 mm</td> </tr> <tr> <td>Effective pixel size</td> <td>0.120 mm</td> </tr> <tr> <td>Projection size</td> <td>552 x 576 pixels</td> </tr> <tr> <td>Angular range</td> <td>360'</td> </tr> <tr> <td>#projections</td> <td>720</td> </tr> </tbody> </table> <p><strong>Table 2.</strong> Energy-specific settings used for collecting the data.</p> <table> <tbody> <tr> <td>Energy label</td> <td><em>U</em> (kV)</td> <td>Filtration</td> <td><em>I</em> (μA)</td> <td>Exposure time (ms)</td> <td>Frame averaging</td> </tr> <tr> <td><em>E1</em></td> <td>50</td> <td>None</td> <td>300</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E2</em></td> <td>80</td> <td>1 mm Al</td> <td>180</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E3</em></td> <td>120</td> <td>0.5 mm Cu</td> <td>120</td> <td>250</td> <td>4</td> </tr> </tbody> </table> <p><em>Data Post-Processing</em></p> <p>Before the scans were made, a dark current image and flat-field image were acquired for each scan setting. During the scans, dark current subtraction and flat-field correction were automatically applied to the X-ray projections by the measurement software.</p> <p><em>Data Contents</em></p> <p>This dataset contains the following files:</p> <ul> <li>The raw projection data (.tif format) for each scan and a metadata file (.txt format) describing the measurement setup, with formatting that is both human-readable and machine-readable.</li> <li>Pre-created 2D sinograms for each energy level. The sinograms have been created from the central plane of the cone beam, which reduces to fan beam geometry. The sinograms are stored in Matlab's .mat file format in data structures which also contain metadata on the measurement.</li> <li>Photographs taken during the measurement process.</li> <li>Example filtered backprojection (FBP) reconstructions of the central plane of the phantom for each energy. The reconstructions were computed using the Phoenix datos|x CT software provided with the microtomography scanner</li> </ul> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>) in collaboration with the Computational Physics and Inverse Problems research group at the University of Eastern Finland, Finland (<a href="https://sites.uef.fi/inverse">https://sites.uef.fi/inverse</a>) and the X-ray Laboratory at the Department of Physics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/x-ray-laboratory">https://www.helsinki.fi/en/researchgroups/x-ray-laboratory</a>).</p> <p> </p> <p><strong>Previous Use</strong></p> <p>This dataset has been used in the following publications:</p> <p>Jussi Toivanen, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen. Joint reconstruction in low dose multi-energy CT. <em>Inverse Problems and Imaging</em>, 2020, 14(4): 607-629. doi: <a href="https://doi.org/10.3934/ipi.2020028" target="_blank" rel="noopener">10.3934/ipi.2020028</a>.</p> <p>E. Cueva, A. Meaney, S. Siltanen, M. J. Ehrhardt. Synergistic multi-spectral CT reconstruction with directional total variation. <em>Philos Trans A Math Phys Eng Sci</em>. 2021 Aug 23;379(2204):20200198. doi: <a href="https://doi.org/10.1098/rsta.2020.0198">10.1098/rsta.2020.0198</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected by the Inverse Problems research group, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We wish to thank laboratory engineer Heikki Suhonen for his guidance and assistance in conducting the measurements.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Cone-Beam Computed Tomography Dataset of a Walnut Imaged at 4 Different Dose Levels
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is a walnut in its shell. For the scanning process double-sided tape was used to attach the sample to a plastic tube placed into the rotation stage.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings</em></p> <p>The dataset consists of four different scans of the same sample. For each scan 360 X-ray projections were acquired using an angle increment of 1 degrees, with one additional frame taken at the end to estimate sample movement. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were:</p> <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> <p><em>Data Post-Processing</em></p> <p>Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 255 images taken with the X-ray source off. A flat-field image was created by averaging 255 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p>Please note that this is a an entirely separate dataset from the Walnut datasets accessible at <a href="../record/1254206">https://zenodo.org/record/1254206</a> and <a href="https://doi.org/10.5281/zenodo.6986012">https://doi.org/10.5281/zenodo.6986012</a>, although both datasets have been created by the same research group.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Data support for: "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms"
<p>Provided tomographic projection data supports the publication in SoftwareX journal "<strong>CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms</strong>" published in 2019.</p> <ul> <li><em>TomoSim_data1550671417.h5</em> - is a simulated 3D tomographic projection data with noise and artifacts. The simulation is implemented using <a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> software.</li> <li><em>DendrData_3D.h5 - </em>is a real dataset obtained at I13 branchline of Diamond Light Source. It features a selected time frame out of dynamically collected tomographic data. Data shows a <a href="https://www.sciencedirect.com/science/article/pii/S1359645418302994?via%3Dihub">dendritic grain growth in Mg alloys</a>.</li> </ul> <p>The scripts to replicate the results shown in the paper are available at the Github page of the project: <a href="https://github.com/vais-ral/CCPi-Regularisation-Toolkit">CCPi-Regularisation-Toolkit</a></p> <p> </p> <p> </p>
High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging
<p>This repository contains datasets associated with the paper titled "High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging," accepted at ACS Materials Letters Journal.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Optimized XYZ Coordinates:</strong> The hydrocarbon molecules' XYZ coordinates, obtained using the B3LYP functional and the 6-31g(d,p) basis set in Gaussian 16 software, used to simulate the IR spectra (including transition energies and absorption intensities) of the molecules.</p> </li> <li> <p><strong>Broadened Molar Absorptivity IR Spectra:</strong> The dataset's IR spectra, broadened using a Lorentzian band shape with a gamma (half-width at half-height) value of 5 cm⁻¹. Molecules with imaginary frequencies have been excluded.</p> </li> <li> <p><strong>Related SMILES Strings:</strong> Contains SMILES strings for these hydrocarbons.</p> </li> <li> <p><strong>NUMBERS_SMILES.csv:</strong> Provides the associated SMILES string for each numerated XYZ coordinate.</p> </li> </ol> <p>For any inquiries, please contact Dr. Maliheh Shaban Tameh at malihe.shaban<a rel="noreferrer">@gmail.com</a></p>
EOL computer vision pipelines: Object Detection for Image Cropping: Aves
<p>Produced by an detection model pretrained on MS COCO 2017. Automatically crops images of birds (Aves) to square dimensions centered around animal(s). </p> <p>388,166 rows </p>
EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon
<p>Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>
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.