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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.

zenodo40/100

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-4.0May 2024View details →

zenodo40/100

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.

zenodo36/100

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-4.0Jul 2024View details →

zenodo36/100

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-4.0Feb 2018View details →

ClinicalTrials.gov36/100

Dual Energy Cone-Beam Computed Tomography (DE-CBCT) Assessment of Jaw Bone Density

ClinicalTrials.gov study NCT04686084. IPD Sharing: NO. Countries: 1. Publications: 0.

opencc-by-4.0Jul 2019View details →

zenodo32/100

dataset for root canal configuration of mandibular first and second premolars using in vivo cone-beam computed tomography imaging

dataset for root canal configuration of mandibular first and second premolars using in vivo cone-beam computed tomography imaging

IV Contrast-Enhanced Cone Beam Computed Tomography (CBCT) in Radiotherapy

ClinicalTrials.gov study NCT04199754. IPD Sharing: NO. Countries: 1. Publications: 7.

Cone Beam Computed Tomography for Evaluating Corticotomy-assisted Maxillary Expansion

ClinicalTrials.gov study NCT02574117. IPD Sharing: Not stated. Countries: 1. Publications: 7.

Jumping Gap Dimension at Maxillary Teeth: Cone-Beam Computed Tomography (CBCT) Study

ClinicalTrials.gov study NCT04636385. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.

Diagnostic Efficiency of Low-dose Cone-beam Computed Tomography in Post-graft Evaluation.

ClinicalTrials.gov study NCT06395077. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Diagnosis of Decreased Bone Density by Dental Cone Beam Computed Tomography

ClinicalTrials.gov study NCT02303340. IPD Sharing: Not stated. Countries: 1. Publications: 9.

Exact Localisation of Impacted and Supernumerary Teeth by Cone Beam Computer Tomography

ClinicalTrials.gov study NCT01002131. IPD Sharing: Not stated. Countries: 1. Publications: 1.

A Retrospective Cone Beam Computed Tomography Study of The Lateral Wall Bony Window

ClinicalTrials.gov study NCT06309706. IPD Sharing: NO. Countries: 1. Publications: 2.

Clinical Evaluation and Cone Beam Computed Tomography Analysis of Different Biomaterials for Apexogenesis of Immature Permanent Molars

ClinicalTrials.gov study NCT07336498. IPD Sharing: Not stated. Countries: 1. Publications: 1.

EFFECT OF LIMITED VOLUME CONE BEAM COMPUTED TOMOGRAPHY ON MICRONUCLEI CELLS COUNT OF BUCCAL MUCOSA

ClinicalTrials.gov study NCT05532514. IPD Sharing: Not stated. Countries: 1. Publications: 6.

Assessment of Cone-beam Computed Tomography (CBCT) Assistance to Video-assisted Thoracoscopic Surgery

ClinicalTrials.gov study NCT02966847. IPD Sharing: NO. Countries: 1. Publications: 1.