Find research datasets worth reusing

opencc-by-4.0Oct 2009View details →

Localizing Spherical Fiducials in C‐arm Based Cone‐Beam CT

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>. 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. The full MATLAB code for fiducial localization described in the manuscript is available <a href="https://www.yanivresearch.info/software/cbctSphericalFiducialLocalization.zip">here</a>.

opencc-by-nc-sa-4.0Aug 2020View details →

4D cone beam computed tomography phantom data set

This data set accompanies the following Medical Physics publication: <a href="https://doi.org/10.1002/mp.14441">Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-5631</a>.It comprises 6 time-resolved (4D) cone-beam computed tomography scans with the following scan configurations:<ul><li>4D CBCT Scanner: Varian TrueBeam (the detailed scan geometry and further details can be found in Scan.xml included in each scan)</li><li>Phantom: <a href="https://www.cirsinc.com/products/radiation-therapy/dynamic-thorax-motion-phantom/">Dynamic Thorax Phantom: Model 008A</a></li><li>The following motion patterns are included:<ol><li>SI amplitude of insert: ±10mm, pattern: sin, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 7.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 7.5s</li></ol></li></ul>

Cone-Beam Computed Tomography Dataset of a Seashell

Summary This dataset is a collection of X-ray projection images of a seashell imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and photographs of the sample and the measurement setup.   Description Sample Information The sample is an empty seashell of an unknown species, approximately 4.3 cm in length and 2.5 cm in diameter. The sample was placed in a plastic tube filled with cotton wool to prevent unwanted motion during the scan. Scanner 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). Scan Settings 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 50 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. Data Post-Processing Two correction images were acquired before scanning the sample. 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. Data Format The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. Notes 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.   Research Group 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>.   Additional Links 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>   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

Cone-Beam Computed Tomography Dataset of a Walnut

Summary This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters.   Description Sample Information 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. Scanner 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). Scan Settings 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. Data Post-Processing Two correction images were acquired before scanning the sample. 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. Data Format The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. Notes 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 5 pixels, using circular boundary conditions, before performing any other operations on the projections.   Research Group 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>.   Additional Links This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at <a href="https://www.youtube.com/watch?v=CWUomAmUDys">https://www.youtube.com/watch?v=CWUomAmUDys</a>. 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>. Please note that this is a an entirely separate dataset from the Walnut dataset accessible at <a href="https://zenodo.org/record/1254206">https://zenodo.org/record/1254206</a>, although both datasets have been created by the same research group.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

Cone-Beam Computed Tomography Dataset of a Pine Cone

Summary This dataset is a collection of X-ray projection images of a pine cone imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and a photograph of the sample.   Description Sample Information The sample is an open cone of a Baltic pine (Pinus sylvestris), approximately 3 cm in diameter. For the scanning process sticky tack was used to attach the sample to a plastic tube placed into the rotation stage. Scanner 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). Scan Settings 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. Data Post-Processing Two correction images were acquired before scanning the sample. 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. Data Format The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. Notes 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 5 pixels, using circular boundary conditions, before performing any other operations on the projections.   Research Group 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>.   Additional Links This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at <a href="https://www.youtube.com/watch?v=CWUomAmUDys">https://www.youtube.com/watch?v=CWUomAmUDys</a>. 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>   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

Cone-Beam Computed Tomography Dataset of a Chicken Bone Imaged at 4 Different Dose Levels

Summary 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.   Description Sample Information 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. Scanner 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). Scan Settings 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: <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> The scans were made in sequence, proceeding from the lowest dose to the highest dose. Data Post-Processing 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. Data Format 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. Notes 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.   Research Group 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>.   Additional Links 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>.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

opencc-by-4.0Jan 2018View details →

Two cone beam test dataset for RECAST3D

This upload contains two dataset acquired specifically for the paper <blockquote> J. Buurlage, H. Kohr, W.J. Palenstijn, K.J. Batenburg. Real-time quasi-3D tomographic reconstruction. 2018 (in preparation). </blockquote> where the dataset are used to showcase the abilities of the real-time visualization software, RECAST3D.   The dataset are named <ol> <li>woodenEgg: A carved wooden egg, placed on a uniform, plastic tube. Collected on 2018-01-11.</li> <li>poppySeeds_playdoh: two tall and skinny Play-Doh pieces, hidden in a plastic container filled with poppy seeds. Collected on 2018-12-19. </li> </ol> The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings. Full information can also be found in the paper above.    These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="https://github.com/cicwi">GitHub page</a>. For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl (independent use of dataset)</li> <li>j.buurlage [at] cwi.nl (for the use of RECAST3D)</li> </ul>

opencc-by-4.0May 2024View details →

Cone-Beam Computed Tomography Dataset of a Walnut Imaged at 4 Different Dose Levels

Summary 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.   Description Sample Information 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. Scanner 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). Scan Settings 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: <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> Data Post-Processing 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. Data Format 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. Notes 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.   Research Group 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>.   Additional Links 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>. 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.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

opencc-by-4.0Jul 2017View details →

A cone beam scan of a rat skull

This upload contains a circular cone beam dataset of a rat skull with teeth still attached. This dataset is suitable for testing reconstruction and image segmentation algorithms. Additional renderings of the reconstructions are included in this submission as well as the projections at angles 0 and 90.    The dataset is acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel.    The dataset was collected over a 360 degrees in circular and continuous motion with 1200 projections distributed evenly over the full circle. The uploaded dataset is not normalized; a single dark and two (pre- and post-) flat fields are included for the scan. The dataset is binned by 2, meaning the size of each projection is 972-by-768 pixels. The dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor positions at the start of the scan.   Sample is of a rat skull used for various scientific experiments. The sample was mounted in a sponge to allow mounting vertically, and to dampen any vibrations from rotation. The sample was provided by Udo van Hes from Technology Centre, University of Amsterdam. The teeth of the rat are still attached to the skull and can be segmented out. We include the projections at angles 0 and 90 for viewing purposes.    These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>. <ul> </ul>   For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl</li> </ul>

opencc-by-4.0Nov 2018View details →

Oatmeal Data: Experimental cone-beam tomographic data for techniques to improve image resolution

This submission contains supplementary material to A.A. Hendriksen et al., (2019). Throughout the description, we mention this publication as the "referred paper".   Description Summary "Oatmeal Data" is a collection of experimental tomographic data of a plastic jar filled with store-bought mixed-size flake oatmeal. The dataset was collected to test methods specifically developed for improving image resolution in a reconstructed volume (primarily for machine learning). As well as the raw (unprocessed) tomographic data, this submission includes training and test data, weights and geometry parameters, and a text file (Makefile) to reproduce the results presented in the referred paper. The full list of contents is given below.    Apparatus The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel.    Sample Information The sample consists of a thin-walled plastic far filled with store-bought mixed-sized oatmeal flakes. This sample was chosen due to its repeating/self-similar structure. This self-similarity is an important factor and is exploited in the algorithm for improving image resolution, presented in the referred paper.   Experimental Plan Three experiments at two magnification levels were performed, which are denoted by Zoom1 and Zoom4: Zoom1 is the dataset collected with magnification factor of 1.09, with the plastic jar filled with oatmeal fully in view. Zoom4 - centre is the sample scanned at magnification of 4.38, with the field of view aligned with the centre of the same (i.e. the centre of the original view in Zoom1). Zoom4 - top is where the tube and the detector are positioned to show the top of the sample (surface level of the oatmeal flakes is visible at the top of the field of view). The datasets were collected over a 360° in circular and continuous motion with 2000 projections distributed evenly over the full circle. The upload includes the raw data collected following the experimental plan, which includes additional projections for normalization: one dark-field (closed shutter) and two (pre- and post-) flat-field (open shutter) images. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor positions at the start of the scan. In addition, ASTRA geometry format file is included for each data in the geometries folder.    List of Contents The contents of the submission (sorted into folders) is given below. <ul> <li>data: The raw (uncorrected) datasets (including dark and flat fields) for <ul> <li>Zoom1,</li> <li>Zoom4-centre,</li> <li>Zoom4-top.</li> </ul> </li> <li>geometries: The set of parameters needed for the ASTRA forward and back projectors.</li> <li>test: Test dataset compiled as detailed in the referred paper, or in <a href="https://ahendriksen.github.io/on_the_fly/readme.html">On-the-fly GitHub documentation.</a></li> <li>train: Training dataset as detailed in the referred paper, or in <a href="https://ahendriksen.github.io/on_the_fly/readme.html">On-the-fly GitHub documentation.</a></li> <li>weights: Weights as detailed in the referred paper, or in <a href="https://ahendriksen.github.io/on_the_fly/readme.html">On-the-fly GitHub documentation.</a></li> <li>Makefile: File containing steps to reproduce results presented in the referred paper.</li> </ul>   Additional Links These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.   Contact Details For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl</li> <li>allard.hendriksen [at] cwi.nl</li> </ul>

opencc-by-4.0Nov 2023View details →

Geometric calibration of a double-line X-ray cone-beam tomography system

This dataset correspond to the raw data and measurements presented in the article "Geometric calibration of a double-line X-ray cone-beam tomography system". In this study, an existing single-line calibration method is extended to the case of a double-line X-ray tomograph, based only on the geometrical relation between the different components, and using a simplified calibration phantom. The methodology is tested on the new double tomograph system at INSA Lyon. Walnuts are used as testing phantoms due to their natural irregularity and outstanding internal texture under X-rays.Two scanning scenarios are selected to represent the wide range of possible geometries that the system can resolve. The first test, referred to as NXGP04, corresponds to an assembly of walnuts inside a container, scanned at a voxel size of 111 μm, and a geometric magnification (i.e., the ratio between SDD and SOD) of 2.7. The second test, referred to as NXGP05, constitutes a scan of a single walnut using a voxel size of 28 μm and a geometric magnification of 10.7. For a given test, all the geometric parameters (i.e., SOD and SDD), as well as the scanning parameters (e.g., voltage and exposure time) are set equally for both lines. Calibration phantoms are used to measure the geometry of the system for each test. They consist of a set of 7 metallic spheres attached to a foam board arranged along a line. For the test NXGP04, the spheres have 2.5 mm in diameter and are placed using a spacing of 20 mm, while for the test NXGP05, the spheres have 0.8 mm in diameter and spacing of 8.75 mm. In both cases, the size of the spheres in the detectors is such that more than 700 pixels are used to determine their centre, which increases the sensitivity of the calculation.

opencc-by-4.0Oct 2017View details →

High-resolution cone-beam scan of a pomegranate with two dosage levels

We release two tomographic scans of a pomegranate with two levels of radiation dosage for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels.    The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel.    Both dataset were collected over a 360 degrees in circular and continuous motion with 501 projections distributed evenly over the full circle. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 50kV, 20W, respectively. The total scanning time were 7 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.   These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.   For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl</li> </ul>

opencc-by-4.0Jul 2024View details →

Cone beam computed tomography dataset of a knee phantom

This is a cone beam computed tomography dataset of a custom-made knee phantom. The data was collected with Planmeca Viso G7 scanner. A total of 500 projections were collected. 100 kV with 80 mAs was used during the measurement. The data is stored in a mat-file that can be easily accessed in MATLAB, GNU Octave, Python, Julia, C/C++ and other languages. Only the flat field correction has been preapplied to the projection images, no other corrections have been done. Included are the FOV size, size of one detector pixel, the flat value, number of columns and rows in each projection image, the offset value for the object, the radians for the rotation of the panel (yaw/roll), the projection images themselves, the projection angles, the source to center of rotation and source to panel distances and the coordinates for the source and the center of the panel for each projection. The OMEGA software includes an example on how to use this dataset (all CBCT examples) in MATLAB/Octave and Python.

opencc-by-4.0Feb 2018View details →

Evaluation of orthodontically induced external root resorption following orthodontic treatment using Cone Beam Computed Tomography (CBCT): a systematic review and meta-analysis

Datasets for all analyses performed in the paper.

opencc-by-4.0Oct 2018View details →

High-resolution cone-beam scan of an apple and pebbles with two dosage levels

We release two tomographic scans with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The objects are referred to as apple and pebbles (more specific, hydrograins), respectively. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels. The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel.    Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.   These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.   For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>

opencc-by-4.0Oct 2018View details →

High-resolution cone-beam scan of two pomegranates with two dosage levels

We release tomographic scans of two pomegranates with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels. The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel.    Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.   These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.   For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42

This upload contains samples 38 - 42 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37

This upload contains samples 33 - 37 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32

This upload contains samples 25 - 32 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 17-24

This upload contains samples 17 - 24 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019) Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation." The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a> The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a> For more information or guidance in using these dataset, please get in touch with <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>