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73 results for “MRI dataset”
Postnatal Affective MRI Dataset
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Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</p>
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</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. 2023.</p>
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 2
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Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3
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Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
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Dataset In-vivo estimation of axonal morphology from MRI and EEG data
<p>This dataset includes the data underlying the conclusions made in the scientific article:<br> "In-vivo estimation of axonal morphology from MRI and EEG data"<br> Rita Oliveira, Andria Pelentritou, Giulia Di Domenicantonio, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://www.frontiersin.org/articles/10.3389/fnins.2022.874023/full">https://www.frontiersin.org/articles/10.3389/fnins.2022.874023</a></p> <p>The main objective is to use data collected in-vivo in humans to estimate microscopic morphologic features of the white matter tracts.</p> <p>The in-vivo data estimated along a white matter tract of interest includes:<br> • the MRI g-ratio sampled along the visual transcallosal white matter tract<br> • a measure of conduction velocity estimated from an EEG measure of interhemispheric transfer time (IHTT)</p> <p>The microscopic morphologic features of white matter we estimate are:<br> • the axonal radius distribution, P(r)<br> • the g-ratio dependence on the radius, g(r)</p> <p>-------------------------------------------------------------------------<br> CONTENT:</p> <p>This package includes data for all the 14 subjects used in the corresponding scientific article:<br> • G-ratio values sampled along the transcallosal visual tract <br> double vector (# MRI_gratio samples x 1): G_ratio_samples.mat<br> • Length of the transcallosal visual tract <br> double: Tract_length.mat<br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the left brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat <br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the right brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat <br> • Vector of the time sample of the EEG epochs<br> double vector (1 x #time points): time_vec.mat</p> <p>The codes used in the analysis of this data are available on our online repository: <a href="https://github.com/LREN-physics/AxonalMorphology">https://github.com/LREN-physics/AxonalMorphology</a>.</p> <p>-------------------------------------------------------------------------<br> AUTHORS:</p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p>
QUIQI II Dataset - Statistical analyses of motion-corrupted MRI relaxometry data
<p>QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.</p> <p>The complete support package for the QUIQI method includes:</p> <ol> <li>A copy of the original analysis code used to compile the results presented in the original scientific publication (doi: 10.5281/zenodo.7612032) </li> <li>A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data.</li> </ol> <p><br> The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.<br> <strong>The material provided here only concerns part 2. of the QUIQI support package described above - subset of the data used in the original publication.</strong></p>
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research [V1.6]
<p>V1.6 release notes:</p> <ul> <li>The test subset is released, which contains T1w (6 repetitions/subject), T2w (6 repetitions/subject), and FLAIR (4 repetitions/subject) data from 25 new subjects. These data have passed motion inspection, but one should note that due to the doubled repetition numbers, the average inter-contrast motions are around twice larger than those in the training and validation subsets. To facilitate users, we release the ground truth images as well, but please do not use them during hyperparameter tuning.</li> </ul> <p>V1.5 release notes:</p> <ul> <li>T1w Gradient echo (GRE) data for all 183 subjects are released. Note that the phase encoding direction for GRE data is in the AP direction, different from other contrasts. These GRE data were not checked for motions.</li> <li>A few incorrect records of patient_id were corrected in the H5 files.</li> <li>Scans 2022062708 and 2022062709 were removed due to duplication. Two new scans were added to replace them.</li> </ul> <p>V1.1 release notes:</p> <ul> <li>Please refer to https://www.nature.com/articles/s41597-023-02181-4 for details.</li> </ul>
InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification.
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Free Breathing Lung MRI Dataset at 3T
<p> This is a free breathing lung MRI dataset. Data is acquired with 3D ultra-short te (UTE) radial sequence.</p>
Dataset related to publication "Heating of hip joint implants in MRI: the combined effect of radiofrequency and switched-gradient fields"
<p>The datasets reported in the figures of the article "Heating of hip joint implants in MRI: the combined effect of radiofrequency and switched-gradient fields" by Alessandro Arduino, Umberto Zanovello, Jeff Hand, Luca Zilberti, Rüdiger Brühl, Mario Chiampi and Oriano Bottauscio, accepted for publication to Magnetic Resonance Imaging on Dec. 9th, 2020. A link to the article will be given after its final publication.</p>
Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
<p>This dataset contains the data and model used in the paper</p> <blockquote> <p>Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780"><em>arXiv preprint arXiv:2311.04780</em></a> (2023).</p> </blockquote> <p>If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.</p> <p>These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a></p> <p>Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>.</p> <p>The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA).</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. 2023.</p> <p>[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." <em>Nature methods</em> 18.2 (2021): 203-211.</p>
Serial multiparametric MRI dataset of hypertensive and normotensive rats
<p>Paper: http://dx.doi.org/10.1177/0271678X241310732</p> <p>Part of: https://doi.org/10.5281/zenodo.13889416</p> <p> </p>
Dataset related to article: QUALITY ASSESSMENT OF THE MRI-RADIOMICS STUDIES FOR MGMT PROMOTER METHYLATION PREDICTION IN GLIOMA: A SYSTEMATIC REVIEW AND META-ANALYSIS
<p><strong><span>This table contains the raw data used to generate the heatmap illustrated in Fig 2. Each row of the table shows the distribution of the scores achieved by the studies for a domain. Colors from red to green denote progressive increase from minimum to maximum score obtainable for each domain.</span></strong></p>
Dataset: A scalable method to improve gray matter segmentation at ultra high field MRI.
<p><strong>Dataset description: </strong>Accompanying data for manuscript “<a href="https://www.biorxiv.org/content/early/2018/01/10/245738">A scalable method to improve gray matter segmentation at ultra high field MRI</a>” written by Omer Faruk Gulban, Marian Schneider, Ingo Marquardt, Roy Haast, Federico De Martino.</p> <p><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198335">Published in PLOS One, June 6, 2018</a>.</p> <p>The dataset consist of 7 Tesla MRI anatomical images of living human brains (whole brain; 0.7mm isotropic resolution; T1 weighted, T2* weighted, proton density weighted MPRAGE images; inversion 1, inversion 2, T1, uni, MP2RAGE images; Multi-echo 3D GRE) and hand labeled cortical gray matter images (for further details see <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198335#sec012">section 4.1 of our manuscript</a>).</p> <p>Folder structure is organized according to Brain Imaging Data Structure (BIDS). Further details can be found the README files.</p> <p><strong>Citation</strong></p> <p>Please cite the following paper together with this dataset doi:</p> <ul> <li>Gulban, O. F., Schneider, M., Marquardt, I., Haast, R. A. M., & De Martino, F. (2018). A scalable method to improve gray matter segmentation at ultra high field MRI. <em>PLOS ONE</em>, <em>13</em>(6), e0198335. http://doi.org/10.1371/journal.pone.0198335</li> </ul> <p><br> Bibtex format:</p> <p>```<br> @article{Gulban2018,<br> author = {Gulban, Omer Faruk and Schneider, Marian and Marquardt, Ingo and Haast, Roy A. M. and {De Martino}, Federico},<br> doi = {10.1371/journal.pone.0198335},<br> editor = {Pham, Dzung},<br> issn = {1932-6203},<br> journal = {PLOS ONE},<br> month = {jun},<br> number = {6},<br> pages = {e0198335},<br> title = {{A scalable method to improve gray matter segmentation at ultra high field MRI}},<br> url = {http://dx.plos.org/10.1371/journal.pone.0198335},<br> volume = {13},<br> year = {2018}<br> }<br> <br> ```</p>
NKI Translational Neuroscience Laboratory macaque MRI dataset
<p>This dataset includes whole brain MRI data from a group of three rhesus macaques. The dataset includes functional MRI data in the form of contrast (Monocrystalline iron oxide nanoparticles (MION)) enhanced echo planar images, T1-weighted and T2-weighted anatomical images, and diffusion weighted images for each subject. The fMRI data is a mix of anesthetized resting state imaging, somatosensory stimulation, and awake movie watching. </p>
Datasets for 'Automated characterization of noise distributions in diffusion MRI data'
<p>Datasets we used for the manuscript 'Automated characterization of noise distributions in diffusion MRI data'.</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>
ScienceDex guides
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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.