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73
datasets available to search
ShareScore release 0.9.0
Dataset results
73 results for “MRI dataset”
Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
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Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART II)
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Dataset related to publication "In silico evaluation of the thermal stress induced by MRI switched gradient fields in patients with metallic hip implant"
<p>The datasets reported in the figures of the article "In silico evaluation of the thermal stress induced by MRI switched gradient fields in patients with metallic hip implant", published on Phys. Med. Biol. 64 245006, 2019.</p> <p>This work focuses on the in silico evaluation of the energy deposed by MRI switched gradient fields<br> in bulk metallic implants and the consequent temperature increase in the surrounding tissues. The results show that the gradient coils can generate local increases of temperature up to some kelvin when acting without radiofrequency excitation. Hence, their contribution in general should not be disregarded when evaluating patients’ safety.</p>
MRI datasets
<p>These datasets are used to reproduce the results from the paper <a href="https://arxiv.org/abs/2205.14546">https://arxiv.org/abs/2205.14546</a>.</p>
Dataset related to article "Polynomial Chaos Expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data"
<p>Dataset related to simulations described in article:</p> <p>Polynomial Chaos Expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data. Article citation: Bottauscio et al 2024 <em>Phys. Med. Biol.</em> <a href="https://doi.org/10.1088/1361-6560/ad5070" target="_blank" rel="noopener">https://doi.org/10.1088/1361-6560/ad5070</a></p>
MRI Dataset: Diffusion MRI as a potential non-invasive biomarker of neuroinflammation following intracerebral haemorrhage
<p>Raw MRI dataset of T2 and NODDI scans.</p> <p>Subjects: Adult male Sprague-Dawley rats, aged 10-12 weeks, 300-450g. Subjected to intracerebral haemorrhage induction by intracranial injection of 0.2 Type VII collagenase, recovered for 7 days prior to these MRI scans being performed. Further methodological information available upon request.</p>
Example SMS and SB Real-time Cardiac MRI Datasets at 0.55T
<p>This repository contains the raw data for Simultaneous Multi-Slice (<strong>SMS</strong>) and Single-Band (<strong>SB</strong>) real-time cardiac MRI acquired at 0.55T scanner. The data is stored as a MATLAB struct: <em>k-space</em> contains the raw data [Nsample x Narms x Ncoil], and <em>kspace_info</em> contains relevant acquisition-related information.</p> <p>Code to process and reconstruct the data is available here: <a href="https://github.com/usc-mrel/SMS-RT">https://github.com/usc-mrel/SMS-RT</a></p> <p>Please see the linked paper for more information about how the data is acquired.</p>
Characterizing diffusion-controlled release of small-molecules using quantitative MRI: Application to orthopedic infection - dataset
<p>Dataset for paper submitted to Scientific Reports titled "Characterizing diffusion-controlled release of small-molecules using quantitative MRI: Application to orthopedic infection"</p>
MRI Distortion QA Tool Phantom Test Dataset
<p>This data consists of CT and MRI Phantom images captured for the purpose of developing and testing an MRI Distortion QA tool and is released under the Creative Commons Attribution 3.0 Unported License.</p>
Data from: Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project
Diffantom is a whole-brain diffusion MRI (dMRI) phantom publicly available through the Dryad Digital Repository (doi:10.5061/dryad.4p080). The dataset contains two single-shell dMRI images, along with the corresponding gradient information, packed following the BIDS standard (Brain Imaging Data Structure, Gorgolewski et al., 2015). The released dataset is designed for the evaluation of the impact of susceptibility distortions and benchmarking existing correction methods. In this Data Report we also release the software instruments involved in generating diffantoms, so that researchers are able to generate new phantoms derived from different subjects, and apply these data in other applications like investigating diffusion sampling schemes, the assessment of dMRI processing methods, the simulation of pathologies and imaging artifacts, etc. In summary, Diffantom is intended for unit testing of novel methods, cross-comparison of established methods, and integration testing of partial or complete processing flows to extract connectivity networks from dMRI.
A longitudinal MRI dataset of young adult C57BL6J mouse brain
<p>This dataset contains 101 labeled young adult C57BL6J mouse brain MRI scans acquired with a 11.7T MR scanner. As part of the source domain data of Brain Extraction Net (BEN), specific analysis and usage are provided at <a href="https://github.com/yu02019/BEN">github</a> and in the <a href="https://www.biorxiv.org/content/10.1101/2022.05.25.492956v2.abstract">manuscript</a>.</p> <p>Since T2WI is the most commonly used modality for rodent brain imaging and we have accumulated extensive brain scans with high-quality annotations in our previous research studies, we first trained the model on the Mouse-T2WI-11.7T dataset, which served as the source-domain dataset, following the conventional fully supervised training strategy.</p> <p>In particular, we focus on the following features:</p> <ul> <li><strong>Transferability and flexibility</strong>: BEN can adapt to different species, modalities and platforms through its adaptive batch normalization module and semi-supervised learning module.</li> <li><strong>Automatic quality assessment</strong>: Unlike traditional toolboxes, which rely on manual inspection to assess the brain extraction quality, BEN incorporates a quality assessment module to automatically evaluate its brain extraction performance.</li> <li><strong>Speed</strong>: As a DL-based method, BEN can process an MRI volume faster (<1 second) than traditional toolboxes (several minutes or longer).</li> </ul> <p> </p>
Magnetic Resonance Fingerprinting DICOM Validation Datasets for Real-Time Automated Quality Control for Quantitative MRI
<p>12 DICOM validation datasets (in DICOMDIR .zip format) from two Magnetic Resonance Fingerprinting sequences at two slice thickness across three test-retest sets per configuration. Also included are the resulting vial extraction reports and data for each dataset. </p>
A multi-center MRI dataset for bladder cancer and baseline evaluations of federated learning in its clinical application
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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
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Data from: Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project
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Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis
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EDEN2020 Human Brain MRI Datasets for Brain Glioma Patients
<p>The datasets available in this folder regard the acquisition of high-resolution MRI images of a cohort of 30 brain glioma adult patients acquired on a 3 Tesla Philips scanner at the Neuroradiology Unit and CERMAC <em>(Center of Excellence for High Field Magnetic Resonance)</em>, Vita-Salute San Raffaele University and IRCCS Ospedale San Raffaele, Milano, Italy. The acquisition is carried out in the context of the EU’s Horizon EDEN2020 project as part of the work for <em>Deliverable D2.2 Human Brain Imaging Database</em>. Histopathological and molecular details of this cohort of patients are reported in the Patients' Table included in the <em>Readme.md</em> file. </p> <p>The data includes:</p> <ul> <li>3D_FLAIR_Tra: Fluid‑Attenuated Inversion Recovery volumetric sequence acquired on the axial plane for morphological characterization of the lesion. </li> <li>raw_data_DTI_32: Diffusion Tensor Imaging raw data. This is a diffusion-weighted Spin Echo EPI single-shot pulse sequence acquired on the axial plane along 32 gradient directions at a b-value of 1000 s/mm<sup>2</sup> and one volume without diffusion-weighting (b0 image).</li> <li>Axial (AD), Radial (RD), Mean Diffusivity (MD) and Fractional Anisotropy maps derived from 'raw_data_DTI_32' sequence using the Philips IntelliSpace Portal software platform, version 8.0 (Philips Healthcare, Best, The Netherlands) and saved using the ‘_map_DTI_32_gray' suffix.</li> <li>raw_data_NODDI: multi-compartmental dMRI sequence for advanced tractography and NODDI analyses, including an axial high angular resolution diffusion-weighted imaging (HARDI) acquisition along 60 gradient directions at a b-value of 3000 s/mm<sup>2 </sup>a DTI acquisition along 35 directions at a b-value of 711 s/mm<sup>2</sup> and 11 volumes without diffusion-weighting (b0 images). Phase-encoding direction was anterior-to-posterior for all these acquisitions.</li> <li>B0_reverse: a sequence without diffusion-weighting having the same geometrical parameters of the ‘raw_data_NODDI’ images,but acquired using a reversed phase-encoding direction (posterior-to-anterior). This volume allowed estimation and correction for susceptibility-induced distortions.</li> <li>SWI_axial: Susceptibility‑Weighted Imaging sequence acquired on the axial plane for detection of Intratumoral Susceptibility Signals (ITSS), microhemorrages and calcifications. Phase map and magnitude image are separated in two distinguished files, using the ‘_ph’ suffix for phase maps.</li> <li>s3DI_MC_HR: three-dimensional high-resolution time of flight (TOF) MR angiography acquisition to visualize flow within the arterial vessel. </li> <li>Post-gadolinium T1_3D_PROSET_Sag: T1-weighted volumetric sequence acquired on the sagittal plane for morphological characterization of the lesions and detection of areas of contrast enhancement.</li> </ul> <p>Note that all MRI data files were converted from DICOM series using Chris Rorden's dcm2niiX version v1.0.20200331.</p>
Dataset related to article "Apparent Diffusion Coefficient standardization: multi-center and multi-vendor validation study across 1.5T and 3T scanners in a diffusion MRI phantom"
<p>The dataset includes ADC measurements collected for MRI harmonisation across centers and vendors, performed as part of the multicenter RESPECT project, using the QIBA-NIST diffusion MRI phantom.</p> <p>The dataset includes the following sheets:</p> <p>"ADC" sheet: ADC measurements (averaged over 4 repeated measures) for each scanner, comprising both first and second acquisition and 3 imaging planes. </p> <p>"temperature" sheet: temperature measurement of the phantom for each acquisition session.</p> <p>"repeatability" sheet: ADC measurements of the 4 repeated measures for the first acquisition. Average values and CVs are also reported.</p>
Dataset_PostOP_MRI_findings_following_conventional_and_extralevator_APE_in_LRC
<p>Dataset on which the research presented in the above article is based. </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.