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ShareScore release 0.9.0
Dataset results
8 results for “T2-weighted”
T2-weighted Kidney MRI Segmentation
<p>A dataset containing 100 T<sub>2</sub>-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. </p> <p>For more information about the dataset please refer to <a href="https://doi.org/10.1002/mrm.28768">this article.</a></p> <p>For an executable that allows automated segmentation of the kidneys from this dataset please refer to <a href="https://github.com/alexdaniel654/Renal_Segmentor">this software.</a></p>
Annotated T2-weighted MR images of the Lower Spine
<p><strong>Annotated T2-weighted MR images of the Lower Spine</strong></p> <p>Chengwen Chu, Daniel Belavy, Gabriele Armbrecht, Martin Bansmann, Dieter Felsenberg, and Guoyan Zheng </p> <p><strong>Introduction</strong><br /> The Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland, Charité - University Medicine Berlin, Centre of Muscle and Bone Research, Free University & Humboldt-University Berlin, Germany, Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University Burwood Campus, Australia and Institut für Diagnostische und Interventionelle Radiologie, Krankenhaus Porz Am Rhein gGmbH, Köln, Germany, are making this dataset available as a resource in the development of algorithms and tools for spinal image analysis.</p> <p><strong>Description</strong><br /> The database consists of T2-weighted turbo spin echo MR spine images of 23 anonymized patients, each containing at least 7 vertebral bodies (VBs) of the lower spine (T11 – L5). For each vertebral body, reference manual segmentation is provided in the form of a binary mask. All images and binary masks are stored in the Neuroimaging Informatics Technology Initiative (NIFTI) file format, see details at http://nifti.nimh.nih.gov/. Image files are stored as "Img_xx.nii" while the associated annotation files are stored as "Img_xx_Labels.nii", where "xx" is the internal case number for the patient. </p> <p>Image annotations were prepared by Mr. Chengwen Chu (no professional training in radiology). </p> <p><strong>Acknowledgements</strong></p> <ul> <li>The acquisition of original images was supported by the Grant 14431/02/NL/SH2 from the European Space Agency, grant 50WB0720 from the German Aerospace Center (DLR) and the Charité Universitätsmedizin Berlin.</li> <li>Preparation of this data collection was made possible thanks to the funding from the Swiss National Science Foundation (SNSF) through project: 205321 157207/1.</li> </ul> <p><strong>Reference</strong><br /> C. Chu, D. Belavy, W. Yu, G. Armbrecht, M. Bansmann, D. Felsenberg, and G. Zheng, “Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via A Learning-based Method”, <strong>PLoS One</strong>. 2015 Nov 23;10(11):e0143327. doi: 10.1371/journal.pone.0143327. eCollection 2015.</p>
Respiratory-triggered MRCP acquisition at 3T using a T2-weighted TSE (3D SPACE) sequence for Deep Learning-based reconstruction of MRCP
<h1>Description</h1> <p>This dataset is the sample data for MRCP_DLRecon (<a href="https://github.com/JinhoKim46/MRCP_DLRecon">GitHub</a>). <br>Place this dataset in the "Sample_data/" directory along with the "dataset.csv" file. </p> <h1>Data</h1> <p>The provided 3D MRCP data were acquired at 3T (Skyra, Siemens Healthineers AG, Erlangen) using the 3D-SPACE (3D T2w TSE) sequence for a single healthy volunteer. We provide two 2x and one 6x 3D MRCP data to ensure various training and testing scenarios. Each data in the HDF5 format contains the following structures:</p> <ul> <li>Datasets <ul> <li><strong>grappa</strong>: target data (y * x *<em> </em>slice)</li> <li><strong>kdata_raw</strong>: Raw <em>k</em>-space data (x2 or x6) (nCoil * PE * RO * slice)</li> <li><strong>kdata_fs</strong>: Fully-sampled k-space data from <strong>kdata_raw</strong> using GRAPPA (nCoil ×× PE ×× RO ×× Slice)</li> <li><strong>sm_espirit</strong>: ESPIRiT-based sensitivity maps (nCoil * y * x * slice)</li> </ul> </li> </ul> <h1>Citation</h1> <p>Please cite the following <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70002" target="_blank" rel="noopener">paper</a> if this dataset is helpful for your research :)</p> <blockquote> <p>Kim, J., Nickel, M. and Knoll, F. (2025), Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38: e70002. https://doi.org/10.1002/nbm.70002 </p> </blockquote>
Prostate MRI T2-weighted images with peripherial and trasition zone segmentations including corresponding PIRADS and PSA values
<p>This dataset contains 114 t2-weighted MRI images of the prostate with corresponding segmentations.The segmentations include two labels, 1 - Transition Zone, 2 - Peripherial Zone. Most of the images include corresponding PIRADS and PSA values, which are available in the file PSA_PIRADS.csv.</p> <p>For more information concerning the images, see the following article.</p> <p>Please cite the following articles, if you are using this dataset:</p> <p>Gibala, S.; Obuchowicz, R.; Lasek, J.; Schneider, Z.; Piorkowski, A.; Pociask, E.; Nurzynska, K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. <em>J. Clin. Med.</em> <strong>2023</strong>, <em>12</em>, 2836. https://doi.org/10.3390/jcm12082836</p> <p>Gibała, S.; Obuchowicz, R.; Lasek, J.; Piórkowski, A.; Nurzynska, K. Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 9871. https://doi.org/10.3390/app13179871</p>
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
ClinicalTrials.gov study NCT05820113. IPD Sharing: NO. Countries: 1. Publications: 19.
Data from: Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study
Open the record for dataset details and reuse information.
Assessing Lumbar Paraspinal Muscle CSA and Fat Composition with T1 versus T2-Weighted MRI: Reliability and Concurrent Validity.
<p>Raw measurement data to support manuscript publication</p>
Evaluation of Rapid T2-weighted and DWI MR Sequences Reconstructed by Deep Learning for Prostate Imaging
ClinicalTrials.gov study NCT06094322. IPD Sharing: NO. Countries: 1. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.