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Dataset results
9 results for “T1-weighted”
T1-weighted brain MRI acquired from awake and unrestrained sheep
<p>This dataset contains T1-weighted brain MRI images acquired from 6 awake sheep, 1 anesthetized sheep and the MRI acquisition parameters.</p> <p><strong>When using this data please cite: </strong>Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> </p> <p><strong>Note:</strong> A "Version v2" was created because the original "13332_anesthetized_T1.nii" file was corrupted.</p>
Lumbar Spine Vertebral Compression Fractures (VCFs) Dataset: MRI T1-Weighted Images for Benign and Malignant Classification
<p>This dataset was prepared for the study of classification of benign vertebral compression fractures (VCFs) secondary to osteoporosis and malignant VCFs secondary to neoplastic infiltration. The original study in which it was used aimed to assist in differentiating these conditions using three-dimensional radiomic techniques and artificial neural networks.</p> <p>This dataset was assembled from sagittal T1-weighted magnetic resonance imaging (MRI) scans of the lumbar spine obtained from consecutive patients diagnosed with benign or malignant VCFs at the University Hospital of the Ribeirão Preto Medical School (HCFMRP) between the years 2010 and 2019. The images were acquired using the Philips Achieva 1.5 T and 3 T MRI systems and were stored in the DICOM (Digital Imaging and Communications in Medicine) format.</p> <p>The compilation of the dataset followed a rigorous selection and filtering process. From the initial set of cases of vertebral fractures in the lumbar region, patients who had received prior treatment (such as chemotherapy, radiotherapy, or surgery), those with fractures of traumatic etiology, old fractures, patients under 18 years old, and cases of malignant fractures without biopsy confirmation were excluded. With these exclusions, the final set consists of 91 patients (36 men and 55 women, with a mean age of 64.24 ± 11.75 years), of which 47 have benign VCFs and 44 have malignant VCFs.</p> <p>For the segmentation of fractured vertebrae, the images were pre-processed by normalizing the intensity to 256 gray levels (0 to 255), and histogram equalization was applied to improve contrast. The vertebrae were semi-automatically segmented using the 3D Slicer software. Each segmentation was saved in the "nrrd" format, native to 3D Slicer. The entire segmentation process, as well as the definition and application of exclusion criteria, were supervised by a senior radiologist with 20 years of experience in musculoskeletal radiology.</p> <p>The structure of this dataset includes, in addition to the DICOM exams, a directory containing the requantized images to 256 gray levels in the "nrrd" format and another with the segmentation files in the "seg.nrrd" format. A spreadsheet with detailed information about each patient's class, sex, age, and which vertebral bodies were segmented is also available. All DICOM files in this dataset have been anonymized to ensure patient privacy.</p> <p>This dataset was structured to provide a robust basis for the training and validation of machine learning models focused on the classification of vertebral compression fractures. It was designed to aid in the massive three-dimensional extraction of radiomic features, enabling the search for radiomic signatures capable of assisting radiologists in the accurate characterization of these fractures.</p> <p>For more information on how the dataset was created and used, please refer to the original article: <a href="https://link.springer.com/article/10.1007/s10278-023-00847-4" target="_blank" rel="noopener"><em>Chiari-Correia NS, Nogueira-Barbosa MH, Chiari-Correia RD, Azevedo-Marques PM. A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI. J Digit Imaging. 2023;36(4):1565-1577. doi:10.1007/s10278-023-00847-4</em></a></p>
T1-Weighted MRI of a crow brain
<p>T1-Weighted MRI of a brain from a wild American crow that has been coregistered and scaled to the jungle crow atlas: http://carls.keio.ac.jp/bird_brain/bird_brain.html</p>
MR-Eye atlas: a large-scale atlas of the eye based on T1-weighted MR imaging
<p>MR-Eye atlas is a novel digital atlas contructed from MR images (T1-weighted MRI acquired at 1.5T ) of large-scale population of healthy volonteers. It gathers a male and female structural atlas constructed from 594 males and 616 females, with their corresponding probability maps of the different labels projected onto the average respective male and female templates. The atlases include 9 regions of interest lens, globe, optic nerve, intraconal and extraconal fat, and four rectus muscles (lateral, medial, inferior and superior).</p> <p>The original dataset includes:</p> <ul> <li>sub_metadata.csv: dataset summary table</li> <li>template.nii.gz: atlas of the eye images (per sex)</li> <li>max_prob_map.npy and max_prob_map.nii.gz: maximum probability maps (per sex)</li> <li>prob_map.npy and prob_map.nii.gz: probability maps (per sex)</li> </ul> <p>The new version adds:</p> <ul> <li>a combined eye atlas (image + labels)</li> <li>labels projected onto Colin27 and MNI152 T1w spaces (along with the images and their cropped versions)</li> <li>2 more preview figures (labels onto the Colin27 and MNI152, and the scheme of the process)</li> <li>updates on release_notes.md and release_notes.txt (to track changes)</li> <li>modified readme.md and readme.txt</li> </ul> <p>Detailed information on the original large-scale cohort, automated segmentation method and unbiased atlas construction is detailed in the README file.</p> <p>MR-Eye atlas is presented and described in the following article: </p> <p><strong>"Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas"</strong>, J. Barranco, A. Luyken, H. Kebiri, P. Stachs, P. M. Gordaliza, O. Esteban, Y. Aleman, R. Sznitman, O. Stachs, S. Langner, B. Franceschiello<em>, </em>M. Bach Cuadra, https://doi.org/10.1101/2024.08.15.608051</p> <p><strong>Works using any of the provided ressources should cite the above-referred article.</strong></p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne,Switzerland & CIBM Center for Biomedical Imaging. 2024.</p>
Contrast-enhanced 3D T1-weighted Gradient-echo Versus Spin-echo 3 Tesla MR Sequences in the Detection of Active Multiple Sclerosis Lesions
ClinicalTrials.gov study NCT03268239. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Whole-Body 3D T1-weighted MR Imaging Anatomical Sequences: GE mDixon vs FSE (View) Approaches in Prostate Cancer.
ClinicalTrials.gov study NCT03034070. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm
We present an ultrahigh resolution in vivo human brain magnetic resonance imaging (MRI) dataset. It consists of T1-weighted whole brain anatomical data acquired at 7 Tesla with a nominal isotropic resolution of 250 μm of a single young healthy Caucasian subject and was recorded using prospective motion correction. The raw data amounts to approximately 1.2 TB and was acquired in eight hours total scan time. The resolution of this dataset is far beyond any previously published in vivo structural whole brain dataset. Its potential use is to build an in vivo MR brain atlas. Methods for image reconstruction and image restoration can be improved as the raw data is made available. Pre-processing and segmentation procedures can possibly be enhanced for high magnetic field strength and ultrahigh resolution data. Furthermore, potential resolution induced changes in quantitative data analysis can be assessed, e.g., cortical thickness or volumetric measures, as high quality images with an isotropic resolution of 1 and 0.5 mm of the same subject are included in the repository as well.
Data from: T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm
Open the record for dataset details and reuse information.
Coronary Atherosclerosis T1-Weighted Characterization (CATCH)
ClinicalTrials.gov study NCT03504956. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.