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135 results for “cbct”
DATASET Volumetric Stability of Biphasic HAp/b-TCP/Collagen Implants in Malar Zone Augmentation: A CBCT Study in Orthognathic Surgery Patients
<p>This dataset repository contains data and analysis scripts related to the study on the volumetric stability of biphasic HAp/b-TCP/collagen implants used for malar zone augmentation in orthognathic surgery patients. The study evaluates the changes in implant volume over time using Cone Beam Computed Tomography (CBCT) scans. The dataset includes CSV files with clinical data and HTML files documenting the analysis procedures.</p> <h3> </h3>
Validation dataset for the ICASSP-2024 3D-CBCT challenge
<p>Validation Dataset for the ICASSP-2024 3D-CBCT challenge</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please dowload all files into one folder, and then merge them together with:</p> <pre><code class="language-bash">$ zip -s 0 validate.zip --out validate_unsplit.zip </code></pre> <p>This will create a new zip file, "validate_unsplit.zip" that then you can unzip with your favourite tool, e.g.</p> <pre><code class="language-bash">$ unzip validate_unsplit.zip</code></pre> <p> </p> <p>The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p> </p> <p> </p> <p>Remember that to be part of the challenge you need to register in the webpage above.</p> <p> </p>
CBCT-Guided Navigational Bronchoscopy For Lung Nodules
ClinicalTrials.gov study NCT04758403. IPD Sharing: YES. Countries: 1. Publications: 24.
Evaluation of orthodontically induced external root resorption following orthodontic treatment using Cone Beam Computed Tomography (CBCT): a systematic review and meta-analysis
<p>Datasets for all analyses performed in the paper.</p>
Dual Energy Cone-Beam Computed Tomography (DE-CBCT) Assessment of Jaw Bone Density
ClinicalTrials.gov study NCT04686084. IPD Sharing: NO. Countries: 1. Publications: 0.
Imaging Study of 3D-CBCT Sialography and MRI Sialography in Non Tumor Salivary Diseases
ClinicalTrials.gov study NCT02883140. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Prediction of Primary Dental Implant Stability Preoperatively Using CBCT of the Planned Implant Site.
ClinicalTrials.gov study NCT06654518. IPD Sharing: NO. Countries: 1. Publications: 1.
Cognitive-Behavioral Conjoint Therapy (CBCT) Project
ClinicalTrials.gov study NCT02720016. IPD Sharing: NO. Countries: 1. Publications: 2.
Test Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)
<p>Test Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-2) into one folder, and then merge them together with:</p> <p><code>$ zip -s 0 test.zip --out test_unsplit.zip</code></p> <p> </p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p><code>$ unzip test_unsplit.zip</code></p> <p> </p> <p><br>The CBCT geometry required to be used</p> <blockquote> <p>image size : [300 300 300] mm<br>image shape : [256 256 256] voxels<br>voxel size : [1.171875 1.171875 1.171875] mm<br>detector shape : [256 256] pixels<br>detector size : [600, 600] mm<br>pixel size : [2.34375, 2.34375] mm<br>distance source origin (axis of rotation, center of image) : 575 mm<br>distance source to detector : 1050 mm</p> </blockquote> <p> </p> <p>We recommend zenodo_get to download the files:<br><a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p>
Test dataset for the ICASSP-2024 3D-CBCT challenge (Part 1)
<p>Test Dataset for the ICASSP-2024 3D-CBCT challenge (Part 1)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-2) into one folder, and then merge them together with:</p> <p><code>$ zip -s 0 test.zip --out test_unsplit.zip</code></p> <p> </p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p><code>$ unzip test_unsplit.zip</code></p> <p> </p> <p><br>The CBCT geometry required to be used</p> <blockquote> <p>image size : [300 300 300] mm<br>image shape : [256 256 256] voxels<br>voxel size : [1.171875 1.171875 1.171875] mm<br>detector shape : [256 256] pixels<br>detector size : [600, 600] mm<br>pixel size : [2.34375, 2.34375] mm<br>distance source origin (axis of rotation, center of image) : 575 mm<br>distance source to detector : 1050 mm</p> </blockquote> <p> </p> <p>We recommend zenodo_get to download the files:<br><a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p><br><br></p> <p> </p> <h2>Files</h2>
Alveolar ridge alterations in the maxillary anterior region after tooth extraction through orthodontic forced eruption for implant site development: A prospective CBCT case series
<p>Dataset for all analyses done in the study</p>
Head Neck CBCT CT datasets
<p><span>Planning CT images were acquired using the Brilliance CT Big Bore Scanner (Phillips Medical Systems, Andover, MA). Each CT image data includes 90-176 slices with a thickness of 3mm, and each slice has 512×512 voxels with a resolution of 1.1719 mm. Multiple CBCT images were acquired using an onboard Imager (OBI, Varian Medical Systems, Palo Alto, CA) in full-fan mode by Varian Edge linear accelerator during treatment, but only the images obtained on the first treatment day were used in this study. Each CBCT data contains 88 slices with a thickness of 3mm. Each slice has 270×270 voxels with a resolution of 1mm. All the scans have ground truth segmentations, each of which contains 5 anatomical structures.</span></p> <p><span>To keep the same spatial resolution between CT images and CBCT images, we resampled the CT image to a resolution of 1 mm×1 mm×3 mm. Both CT and CBCT images contain extraneous information, such as background regions, shoulder regions, and treatment beds. Thus, we cropped all CT and CBCT images to a standardized size and removed any irrelevant information within the field of view (FOV) to ensure that the images shared a consistent receptive field that covered only the head-neck region of interest. The final CT and CBCT images contain 240×240×64 voxels with a resolution of 1 mm×1 mm×3 mm.<br></span></p> <p> </p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 5)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 5)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <p>$ zip -s 0 train.zip --out train_unsplit.zip</p> <p> </p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p>$ unzip train_unsplit.zip</p> <p> </p> <p><br> The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p> </p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p><br> Remember that to be part of the challenge you need to register in the webpage above.</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 4)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 4)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <p>$ zip -s 0 train.zip --out train_unsplit.zip</p> <p>[Click and drag to move]</p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p>$ unzip train_unsplit.zip</p> <p>[Click and drag to move]</p> <p><br> The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p><br> Remember that to be part of the challenge you need to register in the webpage above.</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 3)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 3)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <pre><code class="language-bash">$ zip -s 0 train.zip --out train_unsplit.zip </code></pre> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <pre><code class="language-bash">$ unzip train_unsplit.zip</code></pre> <p> </p> <p>The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p>Remember that to be part of the challenge you need to register in the webpage above.</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <pre><code class="language-bash">$ zip -s 0 train.zip --out train_unsplit.zip </code></pre> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <pre><code class="language-bash">$ unzip train_unsplit.zip</code></pre> <p> </p> <p>The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p>Remember that to be part of the challenge you need to register in the webpage above.</p>
IV Contrast-Enhanced Cone Beam Computed Tomography (CBCT) in Radiotherapy
ClinicalTrials.gov study NCT04199754. IPD Sharing: NO. Countries: 1. Publications: 7.
Assessment of Vertical Pattern in Correlation With Third Molar Inclusion : A 3D CBCT Analysis
ClinicalTrials.gov study NCT06320665. IPD Sharing: UNDECIDED. Countries: 1. Publications: 17.
Jumping Gap Dimension at Maxillary Teeth: Cone-Beam Computed Tomography (CBCT) Study
ClinicalTrials.gov study NCT04636385. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Accuracy of Artificial Intelligence in Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT
ClinicalTrials.gov study NCT05350228. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
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