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.
258
datasets available to search
ShareScore release 0.7.1
Dataset results
258 results for “Phantom”
DBS Phantom Recordings
Open the record for dataset details and reuse information.
Social Processes Initiative in Neurobiology of the Schizophrenia(s) Traveling Human Phantoms
Open the record for dataset details and reuse information.
Hyperspectral X-ray CT datasets of an aluminium phantom containing three metal-based powders
<p><strong>General Data description:</strong></p> <p>This is a set of two hyperspectral (energy-resolved) X-ray CT projection datasets of a multi-phase phantom. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, following two hyperspectral scans of a metal, multi-phase phantom. The phantom consists of an external aluminium cylinder, with three holes, each filled with a different metal-based powder (CeO<sub>2</sub>, ZnO, Fe). Each powder provides a unique attenuation signal, with CeO<sub>2</sub> in particular producing a distinct spectral marker which can be measured by an energy-sensitive detector. Two identical scans were acquired, with only the exposure time per projection changed.</p> <p>Note: Zenodo Version 2 of this dataset contains the incorrect version of the 180s, 180 projection phantom dataset, if wishing to analyse the dataset used in the associated hyperspectral paper. This version (Version 3) contains the correct dataset from the paper.</p> <p><strong>File descriptions:</strong></p> <p>Contained is an image (.jpg) of the sample, along with five MATLAB (.mat) data files, as well as a single text (.txt) file. Where necessary, the files have been named to match the dataset they belong to, based on the different exposure times used for each dataset.</p> <p>Phantom_design_measurements.jpg contains a photograph of the physical phantom, combined with a diagram showing full sample measurements.</p> <p>Powder_phantom_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections for both scans.</p> <p>Powder_phantom_30s_30Proj_sinogram.mat contains the 4D sinogram constructed following flatfield normalisation of the raw projection data, where an exposure time of 30 s was used for each projection. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired during scanning. The total number of channels in the file is 200.</p> <p>Powder_phantom_180s_180Proj_sinogram.mat is the 4D sinogram for the dataset, when exposure times of 180 s were used for each projection, following flatfield normalisation. A discontinuity occurs at projection 137 due to an interruption in the scan procedure. The total number of channels in the file is 200.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning. This is the same for both datasets.</p> <p>FF_30s.mat contains the 4D flatfield data acquired when no sample was present, in the case of 30 s exposure times. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 200 channels are included.</p> <p>FF_180s.mat contains the 4D flatfield data for the dataset where 180 s exposure times were used. The first 200 channels are included.</p>
Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)
<p>This dataset gathers synthetic T2-weighted magnetic resonance (MR) images generated using FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation.<br> This dataset is associated with the following paper:<br> Lajous H. et al. (2022) A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports. https://doi.org/10.1038/s41598-022-10335-4</p> <p>This dataset provides images simulated by FaBiAN based on the specific implementation of FSE sequences by two MR vendors (Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE), Siemens Healthcare, and Single-Shot Fast Spin Echo (SS-FSE), GE Healthcare) at 1.5 T or 3 T.<br> Automated brain tissue annotations of the low-resolution series and super-resolution (SR) reconstructions are also included.</p> <p>Works using any of these data should cite the following references:<br> - Lajous, H. et al. A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports (2022). https://doi.org/10.1038/s41598-022-10335-4<br> - Lajous, H., Roy, C. W., Yerly, J. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v1.2 (1.2). Zenodo (2022). https://doi.org/10.5281/zenodo.5471094<br> - Lajous, H. et al. Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Zenodo (2022). https://doi.org/10.5281/zenodo.6477946</p> <p><br> 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. 2022.</p>
Hyperspectral X-ray CT datasets of three chemical phantoms
<p><strong>General Data description:</strong></p> <p>The following are hyperspectral (energy-resolved) X-ray CT datasets for a set of chemical phantom samples, each containing multiple phases of an aqueous contrast agent at different concentrations. All scans were acquired with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction of each dataset. The phantom samples were produced as they each offer a distinct spectral marker which, when measured by an energy-sensitive detector, may be used as a form of calibration for spectral analysis. The phantoms were for the common contrast agents of I<sub>2</sub>KI, BaSO<sub>4</sub> and PTA.</p> <p><strong>File descriptions:</strong></p> <p>Contained are four MATLAB (.mat) data files, as well as three text (.txt) metadata files.</p> <p>Iodine_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the I<sub>2</sub>KI phantom. The concentrations for the iodine phases were 25, 50, 76 and 101 mg/ml of aqueous I<sub>3</sub><sup>-</sup> ions respectively.</p> <p>Barium_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the BaSO<sub>4</sub> phantom. The concentrations for the BaSO<sub>4</sub> phases were 100, 200 and 400 mg/ml of BaSO<sub>4</sub> respectively.</p> <p>Tungsten_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the PTA phantom. The concentrations for the PTA phases were 50, 100 and 200 mg/ml of PTA respectively.</p> <p>Iodine_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the I<sub>2</sub>KI phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Barium_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the BaSO<sub>4</sub> phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Tungsten_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the PTA phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p>
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>
STEMPO - dynamic X-ray tomography phantom
<p>The Spatio-TEmporal Motor-Powered (<strong>STEMPO</strong>) phantom is a physical phantom designed for collecting dynamic X-ray tomography data. The dynamic part of the phantom is computer controlled allowing for wide variety of different measurements and sampling setups to be used. The primary goal is to help mathematical community test and validate novel dynamic tomography reconstruction methods.</p> <p>Detailed documentation of the phantom, the included data (volume 1 only) and some examples can be found on the related publication: <a href="https://doi.org/10.1007/978-981-97-6769-4_1">https://doi.org/10.1007/978-981-97-6769-4_1</a> (available as an arXiv preprint: <a href="http://arxiv.org/abs/2209.12471">http://arxiv.org/abs/2209.12471</a>).</p> <p>This data set can be appended with new data in the future. Current version (<strong>1.2</strong>) includes<br> <strong>Data - vol.1 (v1.0)</strong></p> <ul> <li>stempo_static_2d_b*.mat</li> <li>stempo_static_3d_b*.mat</li> <li>stempo_cont360_2d_b*.mat</li> <li>stempo_cont360_3d_b*.mat</li> <li>stempo_seq8x45_2d_b*.mat</li> <li>stempo_seq8x45_3d_b*.mat</li> <li>stempo_data_geometries.csv</li> </ul> <p> <strong>Data - vol.2 (added in v1.2)</strong></p> <ul> <li>stempo_seq8x180_2d_b*.mat</li> <li>stempo_seq8x180_3d_b*.mat</li> </ul> <p>where b* denotes downsampling or binning of the data by a factor of (4, 8, 16 or 32). These are 2D and 3D data collected from a <em>static</em> object for reference, or from a dynamic target in a <em>continuous</em> 360 projection scan or <em>sequence</em> of 8 rotations, each consisting of 45 or 180 projections (with <strong>seq8x45</strong> and <strong>seq8x180</strong> data respectively). Finally stempo_data_geometries.csv is a simple table containing the key parameters of the measurement geometry in text format. Note that the height of the phantom for volume 2 data is slightly different compared to volume 1, including the static scan (mostly relevant for comparing 3D reconstructions).</p> <p>In addition the data set contains</p> <p> <strong>Additional files</strong></p> <ul> <li>stempo_ground_truth_2d_b4.mat</li> </ul> <p>which is an approximation of the true motion obtained from a single static FBP reconstruction which has been interpolated to match the location of the moving block during the <em>cont360</em> and <em>seq8x45</em> scans. Finally there are</p> <p> <strong>Example algorithms</strong></p> <ul> <li>stempo_fbp_example.m</li> <li>stempo_fdk_example.m</li> <li>stempo_pdfp_wavelet_2d_example.m</li> <li>stempo_LplusS_2d_example.m</li> </ul> <p>which are short example algorithms of well know analytic (<em>FBP</em> and <em>FDK</em>) and iterative methods. <em>stempo_pdfp_wavelet_2d.m<strong> </strong></em>uses variational regularization and wavelet transform of the 2D + time object to reach a suitable solution. The codes are adapted from [<a href="https://doi.org/10.1088/1361-6501/aa9260">1</a>,<a href="https://doi.org/10.1088/1361-6420/ab9c15">2</a>]. <em>stempo_LplusS_2d_example.m</em> attempts to split the reconstruction into low-rank component <em>L</em> and a sparse dynamic component <em>S</em>. This code is adapted from [<a href="https://doi.org/10.1002/mrm.25240">3</a>]. These are meant to give users ideas how the data can be used in different applications to match the requirements of different methods.</p> <p>Easiest way to utilize the data is with the <a href="http://www.astra-toolbox.com/">ASTRA Toolbox</a> and the <a href="https://github.com/Diagonalizable/HelTomo">HelTomo Toolbox</a>. Some of the example codes also require <a href="https://www.cs.ubc.ca/labs/scl/spot/">Spot Linear Operator Toolbox</a> (highly recommended) and the Wavelet Toolbox. However none of these are mandatory and any method (including programming languages other than MATLAB) are fine as long as the measurement geometry is respected.</p> <p><br>The author is supported by the Emil Aaltonen Foundation junior researcher grant no. 200029 and the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. The author also acknowledges the support of Academy of Finland through the Finnish Centre of Excellence in Inverse Modelling and Imaging 2018–2025, decision number 312339. Finally the author would like to thank E. Heikkilä, T. Heikkilä, A. Meaney and F.S. Moura for all their technical expertise and help in developing, building and imaging the mechanism.</p> <p>The author also thanks O. Tapaninen for helping measure the data for <strong>vol.2</strong>.</p>
Testing of Medtronic Percept PC with MEG phantom
<p>The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested.</p> <p>In the present study, we tested the most commonly implanted telemetric stimulator – Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system.</p> <p>The dataset and code herein make it possible to reproduce most of the figures in the paper and examine additional conditions not described in detail in the paper. The data can be useful for developing, testing and benchmarking MEG artefact removal methods.</p>
DWI Traveling Human Phantom Study
Open the record for dataset details and reuse information.
4D cone beam computed tomography phantom data set
<p>This data set accompanies the following Medical Physics publication: <a href="https://doi.org/10.1002/mp.14441"><i>Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-5631</i></a><i>.</i></p><p>It comprises 6 time-resolved (4D) cone-beam computed tomography scans with the following scan configurations:</p><ul><li>4D CBCT Scanner: Varian TrueBeam (the detailed scan geometry and further details can be found in Scan.xml included in each scan)</li><li>Phantom: <a href="https://www.cirsinc.com/products/radiation-therapy/dynamic-thorax-motion-phantom/">Dynamic Thorax Phantom: Model 008A</a></li><li>The following motion patterns are included:<ol><li>SI amplitude of insert: ±10mm, pattern: sin, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 7.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 7.5s</li></ol></li></ul>
NatalIA: PBF-US1 (Phantom Blind-sweeps for Fetal Ultrasound Scanning)
<p>NatalIA PBF-US1 is dataset designed to support the development of AI-based tools for detecting relevant fetal planes in ultrasound videos captured by non-trained personnel, such as midwives or nurses.</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>
ACR PET phantom raw data and templates for advanced analysis
<p>A zipped folder containing raw PET data of the ACR phantom, which was acquired first for 30 minutes without any activity outside the axial field of view (FOV), followed by another 30 minutes of acquisition with activity outside the FOV.</p> <p>Each acquisition comes with the UTE mu-map in DICOM format, included in both raw data folders, <raw> and <raw_ofov>.</p> <p>Since the MR-based mu-maps are not of sufficient accuracy, the synthetic mu-map has been included (and also the generated hardware mu-map).</p> <p>The design for the templates for generating the synthetic mu-map, NAC PET image, and sampling VOIs are included in folder <design>.</p> <p> </p>
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
Simulated dMRI images and ground truth of random fiber phantoms in various configurations
<p>This archive contains simulated dMRI images of random fiber phantoms in various configurations created with Fiberfox and other tools available in MITK Diffusion (<a href="http://mitk.org/wiki/DiffusionImaging">http://mitk.org/wiki/DiffusionImaging</a>). RandomFibers_Example.png illustrates one of the random fiber configurations used for these phantoms.</p> <p>If you are using any of these datasets or the tools used to generate them, please don't forget to cite the dataset itself as well as other relevant publications.</p> <p>Each subfolder contains the following elements:<br> The simulated dMRI image with b-values and gradient directions: dwi.nii.gz, dwi.bvals, dwi.bvecs<br> The fibers used for simulation: AllBundles.fib (binary vtk format)<br> parameters.ffp: Fiberfox simulation parameters<br> parameters.ffp.bvals: b-value file for Fiberfox simulation<br> parameters.ffp.bvecs: gradient vector file for Fiberfox simulation<br> parameters.ffp_VOLUME1.nii.gz: fiber compartment volume fraction map for Fiberfox simulation<br> The logfile detailing all steps of the generation process of the respective phantom: LOGFILE.json</p> <p>bundles: folder containing the individual fiber bundles (binary vtk format .fib)<br> centroids: folder containing the centerlines of each bundle<br> masks: folder containing the binary envelope of each bundle<br> peaks: folder containing the principal fiber direction image (peaks) of each bundle</p> <p>Each subfolder contains the fibers and dMRI simulations with the following fiber specifications:<br> Phantom 1:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 2:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 3:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 4:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 5:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 6:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 7:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 8:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 9:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 10:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 11:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 12:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 13:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 14:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 15:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 16:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p>
Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)
<p>This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br>The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase.<br>For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder.<br>For further measurement details, please refer to the mentioned original article.</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>
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>
High-Resolution Heterogeneous Digital PET [18F]FDG Brain Phantom based on the BigBrain Atlas
<p>We present the design of a digital phantom that tries to overcome the problems of the current PET digital brain phantoms, particularly for the simulation of simultaneous PET-MRI data sets. We propose a new brain digital brain phantom based on the BigBrain atlas, a free, publicly available tool that provides considerable neuroanatomical insight into the human brain with an ultrahigh-resolution 3D model of a human brain at nearly cellular resolution of 20 micrometers. We used the histology maps, the classified tissue maps and the MRI image of the BigBrain atlas, as well as the Hammersmith atlas and a PET [18F]FDG template as inputs to create an instance of this ultra high-resolution heterogeneous PET-MRI phantom.</p> <p>Full details of this phantom in Medical Physics: "Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas", <a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218.</a></p> <p>You can find codes examples for reading the data at https://github.com/mabelzunce/PETBrainPhantoms </p> <p>Please cite this paper if you use this phantom in your work:</p> <p>Belzunce, M.A. and Reader, A.J. (2020), Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas. Med. Phys., 47: 3356-3362. doi:<a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218</a></p>
Phantom measurement data for 'Configuration-based electrical properties tomography', Iyyakkunnel et al. (2021)
<p>This dataset contains the phantom bSSFP measurement data used in the published article Iyyakkunnel et al., 'Configuration-based electrical properties tomography', Magn Reson Med. 2021;85:1855–1864 (doi: 10.1002/mrm.28542). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br> The data includes the magnitude and phase measurements for eight phase-cycled scans (in dicom (.dcm) format). The RF phase increment for the phase cycled scans corresponds to 0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°. For further measurement details, please refer to the mentioned original article.</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.