Find research datasets worth reusing

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>

Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16

This upload contains samples 9 - 16 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 1-8

This upload contains samples 1 - 8 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>

opencc-by-4.0Jun 2021View details →

Generalizable cone beam CT esophagus segmentation using physics-based data augmentation

This upload contains open source AAPM thoracic auto-segmentation data (http://aapmchallenges.cloudapp.net/competitions/3) augmented with physics-based data augmentation technique introduced in this paper:https://iopscience.iop.org/article/10.1088/1361-6560/abe2eb . The original data contained thoracic planning CT along with organs-at-risk segmentation masks for Esophagus, heart, lungs and spinal cord. The physics-based augmentation pipeline was used to convert planning CT images to pseudoCBCT (psCBCT) images which are routinely used in weekly radiotherapy treatment sessions for cancer patients. Further geometric data augmentations are also applied to convert one planning CT/OAR dataset into 23 perfectly paired CT/psCBCT/OAR pairs. This dataset has been used to train deep learning models for organs-at-risk segmentation from psCBCT images and multitask simultaneous CBCT to CT translation and segmentation tasks.

ClinicalTrials.gov36/100

Intraoperative Cone-beam CT for Percutaneous Nephrolithotomy

ClinicalTrials.gov study NCT04556396. IPD Sharing: NO. Countries: 1. Publications: 5.

ClinicalTrials.gov36/100

A Study Using Cone Beam CT for Head and Neck Cancer

ClinicalTrials.gov study NCT01661062. IPD Sharing: Not stated. Countries: 1. Publications: 1.

opencc-by-4.0Dec 2022View details →

zenodo32/100

The dataset of "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction"

The dataset used in the study "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction" is available upon request. Please contact the authors of the study or the responsible institution for access to the dataset.

controlledIPD-YESFeb 2026View details →

Added Value of Cone Beam CT Guidance to Electromagnetic Navigation Bronchoscopy for the Diagnosis of Pulmonary Nodule.

ClinicalTrials.gov study NCT05257382. IPD Sharing: YES. Countries: 1. Publications: 1.

Cone Beam CT Guided Transbronchial Cryobiopsy

ClinicalTrials.gov study NCT04047667. IPD Sharing: NO. Countries: 1. Publications: 2.

Internal Fit Evaluation of Indirect Restorations Using Cone Beam CT (CBCT)

ClinicalTrials.gov study NCT04784676. IPD Sharing: NO. Countries: 1. Publications: 1.

Comparison of Lateral Cephalometric and Cone Beam CT Measurements in Mandibular Symphysis

ClinicalTrials.gov study NCT05654844. IPD Sharing: NO. Countries: 1. Publications: 3.

Contrast-Enhanced Cone Beam Breast CT for Diagnostic Breast Imaging

ClinicalTrials.gov study NCT03354611. IPD Sharing: NO. Countries: 1. Publications: 12.

Cone Beam CT for Breast Imaging

ClinicalTrials.gov study NCT01880580. IPD Sharing: Not stated. Countries: 1. Publications: 2.

C-arm Cone-beam CT in Prostate Brachytherapy

ClinicalTrials.gov study NCT02322931. IPD Sharing: Not stated. Countries: 1. Publications: 12.

Cone-beam CT Guided Stereotactic Radiation Therapy for Locally Advanced Pancreatic Cancer

ClinicalTrials.gov study NCT01898741. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Assessment of the Morphological Concordance Between CT and Cone Beam in Sinus Imagery

ClinicalTrials.gov study NCT02397148. IPD Sharing: Not stated. Countries: 1. Publications: 1.