Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
78
datasets available to search
ShareScore release 0.9.0
Dataset results
78 results for “quantitative MRI”
MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).</p>
Data to "Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer"
<p>This record includes the DICOM images and analysed data that were used in the multicenter QA program for quantitative MRI in cervical cancer as published (<a href="https://www.sciencedirect.com/science/article/pii/S0167814020307854?via%3Dihub">https://doi.org/10.1016/j.radonc.2020.09.013</a> ).</p> <p>The DICOM data includes the acquired DICOM data for each institute selected to those that were used in the publication. Acquisitions that were not used were removed. Data was anonymized with conquest dicom server tools.</p> <p>The analyzed data files are included giving per measurement the estimated quantitative parameter values as well as the position of the ROIs and extracted signal intensity values per phantom sample. An explanation of the structure of the files is added in the readme file. The analysis was done with in-house written code in matlab.</p> <p>Included are a description of the sequence parameters for each institute (IQEMBRACE_PhantomQA_OverviewInstitutionalSequenceParameters_20241114) and details on the choices in the analysis of the data (IQEMBRACE_PhantomQA_OverviewPhantomData_20241114). As background also the description of the measurements was added, giving more information on how the measurements were performed.</p> <p>This work was in preparation for the IQ-EMBRACE trial (clinicaltrials.gov NCT03210428)</p>
Relaxation anisotropy of quantitative MRI parameters in biological tissues
<p>Dataset for the manuscript "Relaxation anisotropy of quantitative MRI parameters in biological tissues" published in Scientific Reports 2022</p>
7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.</p>
Orientation anisotropy of quantitative MRI relaxation parameters in ordered tissue
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><br> <strong>Orientation anisotropy of quantitative MRI relaxation parameters in ordered tissue</strong></p> <p>Scientific Reports | DOI:10.1038/s41598-017-10053-2</p> <p>Hänninen Nina(1,2), Rautiainen Jari(1), Rieppo Lassi(2,3), Saarakkala Simo(2,3,4) and Nissi Mikko Johannes(1*)</p> <ol> <li>Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland</li> <li>Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 5000, FI-90014 Oulu, Finland</li> <li>Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland</li> <li>Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland</li> </ol> <p> </p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p><br> Keywords: relaxation anisotropy, orientation, cartilage, MRI, quantitative</p> <p> </p> <p><br> Included folders and files are:</p> <ul> <li>article_figures: all figures published in the manuscript</li> <li>data: MRI measurement data and pre-processed PLM measurement data</li> <li>matlab_functions: matlab functions used in data analysis with subfolders: <ul> <li>aedes_plugins: plugins for aedes (http://aedes.uef.fi) for calculation of relaxation time maps</li> <li>fitting_functions: miscellaneous functions for fitting relaxation times etc, used by the functions in above folder</li> <li>miscellaneous_functions: small helper functions for a number of small tasks utilized by the other scripts and functions</li> </ul> </li> <li>plm_new_data: histological data measured by quantitative polarized light microscopy.</li> <li>sample_holder_3D_model: .stl files for the 3-D printable sample-holder which allows rotation of the specimen</li> <li>carbon_data_collector_ROT_for_publication.m: master data collection and analysis script that reads in all the data and performs all the calculations to produce the images of the study. This function relies on all the matlab-functions in the subfolder (i.e. the subfolders need to be indexable by matlab) and Aedes analysis software (http://aedes.uef.fi) and matlab R2013b or later.</li> <li>README.txt: this file</li> </ul> <p><br> Notes for setting up Aedes correctly for this dataset:<br> Run Aedes -> Tools -> Edit VNMR Defaults:</p> <ul> <li>Return: FT + K-space</li> <li>DC: off</li> <li>Zeropadding: off</li> <li>Sorting & fastread: on</li> <li>Precision: single</li> <li>Read_fcn: readfid (old)</li> <li>Orient: no</li> </ul> <p>See more info in separate readme files included in each folder.</p> <p><br> (Mikko Nissi, Aug 15, 2017)</p> <p> </p>
7 T MRI rawdata for (part 1): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.</p>
7 T MRI rawdata for (part 2): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.</p>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
Longitudinal stability of brain and spinal cord quantitative MRI measures
<p><strong>About: </strong>Tabular (CSV) files are generated by the Courtois Neuromod<a href="https://github.com/courtois-neuromod/anat-processing"> structural data processing workflow</a>. These files contain quantitative MRI metrics such as T1, MTsat, and MTR, in addition to fundamental diffusion tensor imaging indices like RD and FA derived from brain data. The results also encompass the same metrics for spinal cord imaging data, supplemented with additional metrics for spinal cord morphometry. For details regarding the raw data please visit <a href="https://www.cneuromod.ca">https://www.cneuromod.ca</a>. </p><p>Dataset provided for NeuroLibre preprint. Author repo: https://github.com/courtois-neuromod/anat-processing-paper NeuroLibre fork:https://github.com/roboneurolibre/anat-processing-paper</p><p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/18">NeuroLibre technical screening.</a></p><p><a href="https://neurolibre.org"><strong>https://neurolibre.org</strong></a></p>
Quantitative T1 MRI
<p><strong>About:</strong> This dataset consists of Python objects in pkl format, which were generated by executing qMRLab scripts in MATLAB, and they are utilized to produce interactive visualizations with Plotly. The outputs are derived from Bloch simulations of qMRI experiments and incorporate a select number of in-vivo datasets. These outputs serve to illustrate the connection between quantitative images and their corresponding qualitative counterparts from which they were derived.</p><p>Dataset provided for NeuroLibre preprint. Author repo: https://github.com/qMRLab/t1-book-neurolibre NeuroLibre fork:https://github.com/roboneurolibre/t1-book-neurolibre</p><p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/19">NeuroLibre technical screening.</a></p><p><a href="https://neurolibre.org"><strong>https://neurolibre.org</strong></a></p>
Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field
<p>Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field</p>
Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024
<p>Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024. <a href="https://doi.org/10.1111/jon.13201">https://doi.org/10.1111/jon.13201</a></p>
Systematic review of reconstruction techniques for accelerated quantitative MRI
<p>The complete list of the papers that were selected in the categorization phase of the review "Systematic review of reconstruction techniques for accelerated quantitative MRI", in combination with the properties that describe them.</p>
DATASET RELATED TO ARTICLE "Muscle quantitative MRI in adult SMA patients on nusinersen treatment_ a longitudinal study"
<p>Excel file with raw data of muscle fat fraction before and after nusinersen treatment</p>
Quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis and healthy controls
<p>This dataset provides access to radiomic features of brain MR susceptibility-based images (QSM). Specifically, a cohort of 151 subjects, mixed of patients with multiple sclerosis (121) and healthy controls (30) was analysed, studying the Normal Appearing White Matter (NAWM) and NAWM tracts (e.g. corticospinal tract and optic radiation). Robustness analysis of those imaging descriptors can be found in Fiscone et al., <em>Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. </em></p> <p>In the .zip folder, instructions about the organization of the dataset can be found. Together with the data, the code used to assess the reliability of those features is available. </p>
Quantitative Ultrasound(DeepUSFF) vs MRI-PDFF for Liver Fat Assessment in MASLD
ClinicalTrials.gov study NCT07192159. IPD Sharing: YES. Countries: 2. Publications: 3.
Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis
<p>This dataset contains raw qMRI data and the corresponding calculated relaxation time maps, reference data and an example MATLAB script demonstrating how to access the data, comprising study:</p> <p><strong>Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis </strong></p> <p>Journal of Orthopaedic Research | DOI: https://doi.org/10.1002/jor.24780</p> <p>Kajabi Abdul Wahed*(1,2), Casula Victor(1,2), Sarin Jaakko K.(3,4), Ketola Juuso H.(1), Nykänen Olli(3), te Moller Nikae C.R.(5), Mancini Irina A.D.(5), Visser Jetze(6), Brommer Harold(5), van Weeren P. René(5), Malda Jos(5,6), Töyräs Juha(3,4,7), Nieminen Miika T.(1,2,8), Nissi Mikko J.*(1,3)</p> <ol> <li>Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland</li> <li>Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland </li> <li>Department of Applied Physics, University of Eastern Finland, Kuopio, Finland</li> <li>Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland</li> <li>Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, The Netherlands</li> <li>Department of Orthopaedics, University Medical Center Utrecht, The Netherlands </li> <li>School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia</li> <li>Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland </li> </ol> <p> </p> <p>*Corresponding authors:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627,<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Abdul Wahed Kajabi<br> Research Unit of Medical Imaging, Physics and Technology,<br> University of Oulu<br> POB 50,<br> FI-90029, Oulu, Finland<br> abdul.kajabi@oulu.fi<br> +358-50-3037425</p> <p>Keywords: cartilage, post-traumatic, osteoarthritis, quantitative MRI, relaxation times</p> <p>The file "Pony_AllData.mat" contains all the data of the study. The main struct variable in the file ("all_data") contains several fields storing all the data per sample. In the following, brief descriptions for the main subfields are given:</p> <ul> <li>name: contains generic name of the sample</li> <li>qMRI_Data: contains qMRI raw data, the corresponding relaxation time maps and normalized cartilage full-thickness ROI profiles</li> <li>calcROI_Data: contains ROIs used to calculate the relaxation time maps</li> <li>analysisROI_Data: ROIs used for the analysis</li> <li>DD_prof and interpDD_prof: Original and interpolated profiles for proteoglycan content (Optical Density)</li> <li>PLM_prof and interpPLM_prof: Original and interpolated profiles for collagen fiber orientation (Polarized Light Microscopy)</li> <li>E_eq and E_dyn: Equilibrium and dynamic moduli of articular cartilage.</li> </ul> <p>The example script, "Analysis_script.m" contains a short demonstration on how the data in the struct can be accessed. The script assumes that Aedes (http://aedes.uef.fi) analysis software is available for matlab.</p> <p>(Abdul Wahed Kajabi, 17 Jun 2020)</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>
Quantitative Non-Invasive Brain Imaging Using QUTE-CE MRI
ClinicalTrials.gov study NCT03266848. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Quantitative MRI Imaging in Diffuse Liver Diseases
ClinicalTrials.gov study NCT04626492. IPD Sharing: NO. Countries: 1. Publications: 15.
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.