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2,174 results for “MR”
CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes
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SpatialCalibration MR 2D Sagittal
<p>Included are:</p> <ul> <li>vendor-reconstructed DICOM images in NIfTI format (dicom_as_nifti.nii)</li> <li>the scanner raw MR data (meas_MID00749_FID151779_t2_tse_sag.dat)</li> <li>the raw MR data in h5 format, converted using siemens_to_ismrmrd (meas_MID00749_FID151779_t2_tse_sag.h5)</li> <li>the MR data reconstructed with SIRF in .h5 format (SIRF_recon.h5)</li> <li>the MR data reconstructed with SIRF that has been converted to NIfTI format (SIRF_recon.nii)</li> <li>the script used to generate the data (SIRF_recon.sh)</li> </ul>
Data from "Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography"
<p>Data presented in the Science Advances manuscript "<em>Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography</em>" by Yushchenko M., Sarracanie M., Salameh N.</p> <p>See further details in <em>Description.txt.</em></p> <p>The 3D wave datasets acquired in humans at 0.1 T can be used for elastogram reconstruction with appropriate methods.<br> <br> </p>
Diffusion weighted MR imaging of post-mortem rat brain to allow reconstruction of the cortical connectome
<h2>Brief description</h2> <p> </p> <p>These data accompany the article by Sinke et al. (Sinke et al., 2018). It contains the dMRI image volumes of 10 rats, a subset of these data was used for the tractography procedures described in the article. In addition high-resolution 3D balanced SSFP data are provided with high contrast between grey and white matter and CBF. The data are also accompanied by T<sub>1</sub> weighted 3D spoiled gradient echo volumes at three different echo times (5,10 and 15 ms) which can be used for T<sub>2</sub>* measurements.</p> <h2>Animals</h2> <p> </p> <p>All animal procedures were approved by the Animal Experiments Committee of the University Medical Center Utrecht and Utrecht University. Experiments were performed in accordance with the guidelines of the European Communities Council Directive. Ten healthy adult (12–13 weeks old) male Wistar rats have been used and are described in the RCR_table.csv file. Animals were sacrificed and their brains were fixed with transcardial perfusion-fixation. Brains were extracted scanned.</p> <p> </p> <h2>MR acquisition</h2> <p> </p> <p>MRI was performed on a 9.4 T horizontal bore MR system (Varian, Palo Alto, CA, USA) equipped with a 6 cm ID gradient insert with gradients up to 1 T/m. A custom made solenoid coil with an internal diameter of 2.6 cm was used for excitation and reception of the MR signal. The perfusion-fixed brains were inserted with the skulls intact in a custom-made holder and immersed in non-magnetic oil (Fomblin, Solvay Solexis). Diffusion MR used a 3D diffusion-weighted spin-echo sequence with an isotropic spatial resolution of 150 mm, where the read- and phase- encode direction were acquired using 8-shot EPI encoding and the second phase direction was linearly phase-encoded (TR/TE 500/32.4 ms, 220*128*108 matrix, FOV 33*19.2*16 mm<sup>3</sup>, D/d 15/4 ms, b 1031,2078,3994,6038,7756 s/mm<sup>2</sup>, 60 diffusion-weighted images in non-collinear directions and 24 images without diffusion weighting (b=0), number of averages 1, total number of images 325). Four 3D BSSFP images were acquired with an isotropic spatial resolution of 100 mm (TR/TE 15.4/7.7 ms, flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 6 averages, pulse angle shift 0°, 90°, 180° and 270°). The four images were added as complex images to obtain a single BSSFP image with reduced banding artifacts in the brain. If scanning time allowed, three spoiled gradient-echo acquisitions were also performed with varying echotimes of 15, 10 and 5 ms respectively and TR 20 ms (flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 24 averages, pulse angle shift 117°).</p> <h2>Data structure</h2> <p> </p> <p>The repository contains the following data:</p> <p>- READ_ME.txt: this file</p> <p>- RCR_table.csv : Table containing acquisition dates and numbers for the scanned animals.</p> <p>- rawdata.zip : Zipped data directory ‘rawdata’ containing acquired images in NIfTI data format per animal. Data can be unzipped using the ‘unzip’ command. Directory rawdata contains subdirectories RCR01 to RCR10 (individual rat directories). Each rat directory contains the following NIfTI files:</p> <p>o bal.nii.gz and balsumcom.nii.gz : The separate acquisitions of the BSSFP experiment and the complex summation of the data respectively.</p> <p>o dtitot.nii.gz : The diffusion weighted volumes in the order that they were acquired.</p> <p>o bvals and bvecs : Text files containing the b-values and b-vectors in the order that they were acquired, so this corresponds with the dtitot.nii.gz file.</p> <p>o zerob: Text file containing the image numbers where images with no diffusion weighting were acquired.</p> <p>o ubal1.nii.gz, ubal2.nii.gz and ubal3.nii.gz : The three 3D spoiled gradient acquisitions with TE 15,10, and 5 ms respectively.</p> <p>- derivatives.zip : Zipped data directory ‘derivatives’ containing calculated images of the diffusion parameters after application of FMRIB’s diffusion toolbox DTIfit. In addition it contains a file dti3D_b0.nii.gz which is a summation of all the b0-images and a file mask.nii.gz containing the ‘brain’ mask used for application of DTIfit.</p> <p>- Sinke_BrainStructureFunction2018.pdf : The article based on (part) of these data.</p>
Datasets used in the benchmarking study of MR methods
<p>We conducted a benchmarking analysis of 16 summary-level data-based MR methods for causal inference with five real-world genetic datasets, focusing on three key aspects: type I error control, the accuracy of causal effect estimates, replicability, and power.</p> <p>The datasets used in the MR benchmarking study can be downloaded here:</p> <ol> <li>"dataset-GWASATLAS-negativecontrol.zip": the GWASATLAS dataset for evaluation of type I error control in confounding scenario (a): Population stratification</li> <li>"dataset-NealeLab-negativecontrol.zip": the Neale Lab dataset for evaluation of type I error control in confounding scenario (a): Population stratification;</li> <li>"dataset-PanUKBB-negativecontrol.zip": the Pan UKBB dataset for evaluation of type I error control in confounding scenario (a): Population stratification;</li> <li>"dataset-Pleiotropy-negativecontrol": the dataset used for evaluation of type I error control in confounding scenario (b): Pleiotropy;</li> <li>"dataset-familylevelconf-negativecontrol.zip": the dataset used for evaluation of type I error control in confounding scenario (c): Family-level confounders;</li> <li>"dataset_ukb-ukb.zip": the dataset used for evaluation of the accuracy of causal effect estimates;</li> <li>"dataset-LDL-CAD_clumped.zip": the dataset used for evaluation of replicability and power;</li> </ol> <p>Each of the datasets contains the following files:</p> <ol> <li> "Tested Trait pairs": the exposure-outcome trait pairs to be analyzed;</li> <li>"MRdat" refers to the summary statistics after performing IV selection (p-value < 5e-05) and PLINK LD clumping with a clumping window size of 1000kb and an r^2 threshold of 0.001.</li> <li>"bg_paras" are the estimated background parameters "Omega" and "C" which will be used for MR estimation in MR-APSS.</li> </ol> <p>Note:</p> <ol> <li>The formatted dataset after quality control can be accessible at our GitHub website (https://github.com/YangLabHKUST/MRbenchmarking).</li> <li>The details on quality control of GWAS summary statistics, formatting GWASs, and LD clumping for IV selection can be found on the MR-APSS software tutorial on the MR-APSS website (https://github.com/YangLabHKUST/MR-APSS).</li> <li>R code for running MR methods is also available at https://github.com/YangLabHKUST/MRbenchmarking.</li> </ol>
BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution
<p><strong>BigBrain-MR</strong> is a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI contrasts and properties that affect image generation. This phantom was generated from the publicly available <a href="https://bigbrainproject.org/">BigBrain histological dataset</a> and from lower-resolution in-vivo 7T-MRI data, using a new image processing framework that allows mapping the general properties of in-vivo data into the fine anatomical scale of BigBrain.</p> <p>The <strong>dataset</strong> includes:</p> <ul> <li>BigBrain original contrast and a new atlas with 20 ROIs;</li> <li>T<sub>1</sub>-weighted image and T<sub>1</sub> map;</li> <li>T<sub>2</sub>*-weighted images and R<sub>2</sub>* map;</li> <li>Magnetic susceptibility map (QSM);</li> <li>Background magnetic field map;</li> <li>Complex coil sensitivity maps (32ch-receive RF array);</li> <li>Bias field map.</li> </ul> <p>Information about each image/map (including data type and amplitude scaling) is provided in <em>data_info.txt</em>.</p> <p>Additionally, we have included a script with <strong>usage examples</strong> in Python that illustrate how the data can be loaded, processed and combined for diverse simulation purposes.</p> <p>BigBrain-MR is presented, described and tested in the following <strong>peer-reviewed article</strong>:</p> <p>C. Sainz Martinez, M. Bach Cuadra, J. Jorge. <em>BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development</em>. NeuroImage 2023. <strong>DOI:</strong> <a href="https://doi.org/10.1016/j.neuroimage.2023.120074">10.1016/j.neuroimage.2023.120074</a></p> <p> </p>
2D Cardiac black-blood TSE MR raw data
<p>Raw data in ismrmrd format obtained with a 2D black-blood TSE sequence on a 3T Siemens Verio scanner in three different orientations. This data is used as test data for the comparison of different open-source image reconstruction packages provided here: <a href="https://github.com/ckolbPTB/OpenSourceMrRecon">OpenSourceMrRecon</a></p>
MR Spectra from rat hippocampus with LCModel quantification and the corresponding basis set
<p>This folder contains the LCModel quantifications of spectra acquired in hippocampus from 7 rats. The spectra were quntified using six different DKNTMN (spline stiffness) values (0.1, 0.25, 0.4, 0.5, 1, 5). In the folder Control_files_Basis_set you can find all the control files used in this quantification along with the corresponding basis set (metabolites/simulated using NMRScopeB from jMRUI and <em>in vivo </em>parameters + full MM spectrum).</p> <p>Please cite the following manuscript if you are using the data</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/34268821/">In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times - PubMed (nih.gov)</a><br> </p>
Acceleration response of a benchmark 3 Story Structure (SAC) with MR damper under earthquake excitation
<p>This file contains acceleration response of benchmark 3 story structure equipped with MR damper under the Chuetsu-Oki earthquake in Niigata prefecture, Japan, which occurred on July 16th, 2007. Acceleration response of each story is separated and saved in separate text files. Text file contains four columns that the first one has been devoted to time series, while the remained (second, third, fourth) columns present acceleration responses in three perpendicular axes, X, Y, Z, respectively.</p>
Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration
<p>This dataset contains the data used to arrive at the conclusions in the research article <em>Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration</em>, by Balfour et al [<em>BioMedical Engineering OnLine</em> 2015, <strong>14</strong>:85].</p> <p>This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction ("STIR", see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33).</p> <p>Three types of data are provided, which should be sufficient for repeating the findings of the study:</p> <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins ("gates") for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> <p>These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images.</p> <p>For more detailed information regarding the method, please refer to the article.</p> <p>The PET data is split into several sub-categories:</p> <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> <p>Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.</p> <p> </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>
2D Cartesian MR raw data
<p>2D Cartesian acquisition of the brain including raw MR data in ISMRMRD format and image data in dicom and nifti format.</p>
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark
<p>This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.</p> <p>Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:</p> <p>1.    Modic changes (type I, II or III)</p> <p>2.    Upper and lower endplate changes / Schmorl nodes (binary)</p> <p>3.    Spondylolisthesis (binary)</p> <p>4.    Disc herniation (binary)</p> <p>5.    Disc narrowing (binary)</p> <p>6.    Disc bulging (binary)</p> <p>7.    Pfirrman grade (grade 1 to 5). </p> <p>All radiological gradings are provided per IVD level.</p> <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div> </div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>
Monte-Carlo-simulated MR spectra of the rat brain and their quantification results obtained with QUEST and QUEST-MM jMRUI algorithms
<p>MC-simulated spectra of the rat brain along with the corresponding basis set (metabolite signals simulated using NMRScopeB from jMRUI) and the QUEST-MM, QUEST, QUEST(Met+Back) and QUEST(Met+MM) quantification results are stored in the MC_results folder.</p> <p>Results of an in-vivo rat brain MRS signal (SPECIAL, dead time t0 = 0.134 ms, TE = 2.8 ms, at 9.4 T) quantification performed with the QUEST-MM jMRUI algorithm (origin for MC-simulation) are stored in the folder rat_results.</p> <p>All files can be loaded to jMRUI software version 5 and later.</p>
MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)
<p>The dataset of the abstract "MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)" for ISMRM 2022, London, UK. The data processing code with instructions could be found <a href="https://github.com/BRAIN-TO/girfISMRM2022">here</a>.</p> <p> </p> <p>Meas1.zip and Meas2.zip contain the first and the second measurements of the raw T2* decay signal acquired with the phantom-based method. Note that the coil dimension has been averaged to save data volume for demonstration purposes. This will lead to a lower SNR of the calculated output gradient and GIRF.</p> <p> </p> <p>CalculatedGIRF.zip provides the author's pre-calculated GIRFs using the data without coil averaging. This data is used for all the postprocessing (e.g. SNR and stability analysis, etc.) in the published abstract with the source code provided in the same Github repository.</p> <p> </p>
Mice MR Images (lungs and pulmonary metastases)
<p>55 mice were imaged after injection on a 7T Bruker BioSpec system equipped with a gradient coil of 660 mT/m maximum strength and 110 μs rise time. 27 mice with Crispr/Cas9 il34 KO gene and 28 controls. Animals were imaged every week: from day 6 to day 32 post-implantation for control mice and from day 8 till their condition deteriorated (up to day 141 at most) for il34 mice. Two additional healthy mice were scanned 3 times, two times without repositioning and one time after waking them up in order to evaluate the reproducibility of the lungs’ segmentations.</p> <p>The balanced Steady State Free Precession (bSSFP) sequence was chosen, as it has previously been shown that high tumor contrast can be obtained in the brain and in the liver (<a href="https://doi.org/10.1002/jmri.21449">https://doi.org/10.1002/jmri.21449</a> ; <a href="https://doi.org/10.1002/jmri.22593">https://doi.org/10.1002/jmri.22593</a> ; <a href="https://doi.org/10.1002/jmri.24688">https://doi.org/10.1002/jmri.24688</a>). Combined with the Self-Gating (SG) method, it enables to delete echoes affected by motion and consequently obtain abdominal images without motion artifact (<a href="https://doi.org/10.1002/jmri.24688">https://doi.org/10.1002/jmri.24688</a>). </p> <p>Corresponding masks were manually drawn around the lungs on every slice of 185 3D images; masks were manually drawn around the pulmonary metastases on every slice of 62 3D images containing metastases. Both tasks were performed by two different investigators.</p> <p>Link to the code : https://github.com/cbib/DeepMeta</p>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
Dataset: Mr. Cooper Group Inc. (COOP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
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.