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258 results for “phantom”
Phantom measurement data for 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Iyyakkunnel et al. (2022)
<p>This dataset contains the phantom measurement data used in the published article Iyyakkunnel et al., 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Magn Reson Med. 2022;87:1250–1260 (doi: 10.1002/mrm.29020). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using the body coil for transmission and the a 20-channel head and neck coil for reception. Phase images from multichannel coil data were reconstructed using the manufacturer’s “adaptive coil combine” method. Magnitude measurements for B1 reconstruction using actucal flip angle imaging (AFI) is also included. Phantom scans were also performed with a 1H/23Na transmit/receive birdcage coil (RAPID Biomedical, Rimpar, Germany) for the Supporting Information Figure S1. The data includes the magnitude and phase measurements for the suggested Carr Purcell spin echo sequence with 10 echoes (in dicom (.dcm) format). For further measurement details, please refer to the mentioned original article.</p>
Multi-Site Fat-Water Phantom MRI Data
<p><strong>Dataset description:</strong> Accompanying data for manuscript “Multi-Site,</p> <p>Multi-Vendor Validation of the Accuracy and Reproducibility of</p> <p>Proton-Density Fat-Fraction Quantification at 1.5T and 3T using a</p> <p>Fat-Water Phantom”, submitted for publication in Magnetic Resonance in</p> <p>Medicine in January 2016.</p> <p> </p> <p><strong>Details:</strong> The 'datasets' folder contains multiple MATLAB MAT-files</p> <p>files with chemical shift-encoded (CSE) MRI data for validation of fat</p> <p>quantification techniques in a fat-water phantom. Data was acquired at</p> <p>six sites with different MRI vendors, two field strengths (1.5T and</p> <p>3T) per site, and two protocols per field strength. The goal of these</p> <p>techniques is to measure proton-density fat-fraction (PDFF) accurately</p> <p>and reproducibly. Data from site 1 was acquired both at the beginning</p> <p>(October 2014) and at the end (December 2015) of this study, in order</p> <p>to evaluate the integrity of the phantom. </p> <p> </p> <p>Details on the vendors, platforms and protocols are described in the</p> <p>file 'vendors_platforms_protocols.pdf' in this same folder.</p> <p> </p> <p>The phantom consists of 11 vials with different fat concentrations</p> <p>(PDFF = 0%, 2.6%, 5.3%, 7.9%, 10.5%, 15.7%, 20.9%, 31.2%, 20.9%,</p> <p>31.2%, 41.3%, 51.4% and 100%, respectively). At each magnet, the vials</p> <p>were arranged horizontally along the B0 field and scanned with axial</p> <p>slices using a 3D multi-echo spoiled gradient echo pulse sequence.</p> <p> </p> <p>Each of the MAT-files contains the acquired complex-valued images over</p> <p>the three central slices within the phantom vials. The structure</p> <p>'imDataAll' contains the acquired data, with the following fields:</p> <p>'TE' (echo times), 'FieldStrength' (in Tesla), 'PrecessionIsClockwise'</p> <p>(describing whether water has higher resonance frequency than fat</p> <p>according to the reconstruction convention), 'echo_polarity' (relative</p> <p>polarity of the acquired echoes, ie: monopolar vs bipolar readouts),</p> <p>'images' (five-dimensional array containing the complex valued images,</p> <p>coil-combined in datasets received with multiple channels, with</p> <p>dimensions X x Y x Slices x Coils x Echoes).</p> <p> </p> <p>Additionally, the MAT-files contain reconstruction results as</p> <p>described in the manuscript, in the arrays 'fwmc_*' of size X x Y x</p> <p>Slices. Note that the nomenclature 'fwmc' stands for 'Fat-Water</p> <p>separated with Magnitude fitting (performed after complex fitting in</p> <p>order to obtain full 0-100% range of PDFF while avoiding errors</p> <p>related to phase shifts in the data), and a Common initial phase for</p> <p>the water and fat signals. The specific arrays are 'fwmc_ff' (PDFF</p> <p>map), 'fwmc_r2star' (R2*=1/T2* decay rate), 'fwmc_w' (water image),</p> <p>'fwmc_f' (fat image).</p>
Dataset related to article "Phantom‑based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM"
<p>The dataset comprises information from several DICOM tags extracted from digital mammography (DM), digital breast tomosynthesis (DBT), and contrast-enhanced mammography (CEM) images acquired in a phantom study aimed at characterizing the automatic exposure control (AEC) behavior of diverse mammography equipment. The final ten columns of the datasets encompass signal (mena pixel values, MPV) and noise (standard deviation, SD) measurements derived from phantom images. These measurements are used to compute several image quality metrics, including contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), CNR relative difference in comparison to the 45 mm reference thickness, and a figure of merit (FOM) obtained by diving the squared CNR by the mean glandular dose (MGD).</p>
Dataset related to article "Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study"
<p><span>The dataset provided here contains the raw data used in the study discussed in this article. The primary objective of the study was to conduct a comparative analysis of the performance of dual energy subtraction (DES) images acquired using contrast-enhanced mammography (CEM) systems produced by three different manufacturers. The comparison, facilitated by a CEM-specific phantom, focused on the assessment of radiation dose and image quality.</span></p> <p><span>Composed of three separate CSV files, the dataset is structured as follows:</span></p> <p><span>1) "Dose-related data": This file provides exposure parameters (including A/F combination, tube voltage and exposure) and the corresponding mean glandular dose (MGD for low-energy (LE) and high-energy (HE) images. Data are given for each CEM system and automatic exposure mode (AEC).</span></p> <p><span>2) "CNR-related data": Encapsulated in this file are data extracted from the phantom DES images. These include measurements of the mean pixel value (MPV) for each iodinated contrast detail, as well as the MPV and standard deviation (SD) for the surrounding background. These data were used to calculate the contrast-to-noise ratio (CNR) for the iodinated contrast details.</span></p> <p><span>3) “Residual CNR data”: This file includes MPV and SD data extracted from phantom DES images, which were useful for calculating the residual CNR after cancellation of the normal background tissue by the DES algorithm.</span></p>
Dataset related to aticle "Additive Fabrication of a Vascular 3D Phantom for Stereotactic Radiosurgery of Arteriovenous Malformations"The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.
<p><em>The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.</em></p>
Reflection Ultrasound Computed Tomography (RUCT) Phantom Data
<p>Test Data for Reflection Ultrasound Computed Tomography (RUCT) Delay and Sum Algorithm</p> <p>This data is shared for "pyruct" package tests. "pyruct" package can be found in "https://github.com/berkanlafci/pyruct"</p> <p>If you use this data in your research, please cite the following paper:</p> <p>B. Lafci, J. Robin, X. L. Deán-Ben and D. Razansky, "Expediting Image Acquisition in Reflection Ultrasound Computed Tomography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, doi: <a href="https://ieeexplore.ieee.org/document/9768674">10.1109/TUFFC.2022.3172713</a>.</p>
NEMA IQ phantom raw data GE 4-ring DMI
<p><strong>NEMA IQ phantom raw data of the 4-ring acquired on a GE DMI PET/CT at three different count levels</strong></p> <ul> <li>raw and pre-processed sinograms of NEMA IQ phantom acquisition with 3e6, 1e7 and 1e8 prompt counts</li> <li>for each count (noise) level we provide 5 data sets that were unlisted from a very long listmode file with 1e9 prompt counts</li> <li>each unlisted data set (5 for each count level) contains:<br> - subfolder "raw" with the original unlisted emission sinogram + dicom header<br> - subfolder "duetto_workdir" with the pre-processed emission and correction sinograms<br> - subfolder "duetto_workdir/offline3D" example TOF-OSEM reconstruction in dicom format</li> <li>attenuation CT "CTAC" in dicom format</li> <li>high resolution CT "CT_detail" in dicom format</li> <li>the ratio of the activity concentrations of the "hot" spheres and the background was 4.82</li> </ul> <p> </p>
Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research
<p>Dataset related to the publication: "Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research"</p>
Digital Pediatric Image Quality Phantoms and Simulations
<p>Pediatric IQ Phantoms is a dataset of virtual phantoms and their computed tomography (CT) images for assessing the image quality of image-based CT denoising products in pediatric patients. The phantoms in the dataset enable assessment of <a title="IEC Standard 61223-5-3" href="https://webstore.iec.ch/publication/59789" target="_blank" rel="noopener">standard measures of image quality</a> – CT number accuracy, noise magnitude, uniformity, contrast dependent spatial resolution, and low contrast detectability. These phantoms are designed to span the range of pediatric effective waist diameters (Table 1) and thus can be used to assess pediatric image quality performance.</p> Table 1: Pediatric subgroups investigated defined by <table><tbody> <tr> <td><strong>Subgroup</strong></td> <td><strong>Age Range</strong></td> <td><strong>Waist Diameter Range</strong></td> </tr> <tr> <td>Newborn</td> <td>≤ 1 mo</td> <td>≤ 11.5 cm</td> </tr> <tr> <td>Infant</td> <td>> 1 mo & < 2 yrs</td> <td>> 11.5 cm & ≤ 16.8 cm</td> </tr> <tr> <td>Child</td> <td>> 2 yrs & ≤ 12 yrs</td> <td>> 16.8 cm & ≤ 23.2 cm</td> </tr> <tr> <td>Adolescent</td> <td>> 12 yrs & < 21 yrs</td> <td>> 23.2 cm & < 34 cm</td> </tr> <tr> <td>Adult</td> <td>≥ 22 yrs</td> <td>≥ 34 cm</td> </tr> </tbody> </table> <p>These phantoms include:</p> <ul> <li>CTP404 multi-contrast phantom: for assessing CT number accuracy and contrast-dependent spatial resolution. <ul> <li>CTP404 is a modified version of the sensitometry module CTP404 from the Catphan 600 phantom (The Phantom Laboratory, Salem, NY).</li> <li>This cylindrical phantom has eight unique contrast inserts ranging from -1000 to +900 HU in a uniform background of 0 HU. In its standard size, CTP404 has a diameter of 150 mm with 12 mm diameter inserts. Due to the sharp intersection between the phantom background and multi-contrast inserts, this module was used to evaluate contrast-dependent image sharpness using the contrast-dependent modulation transfer function.2</li> </ul> </li> <li>MITA LCD phantom: for assessing low contrast detectability. <ul> <li>The MITA-LCD phantom is a cylindrical phantom filled with water-equivalent attenuation material and contains four low-contrast disk inserts of different size and contrast combinations: 3mm-14HU, 5mm-7HU, 7mm-5HU, 10mm-3HU.3,4</li> </ul> </li> <li>Uniform water phantom: for assessing noise and noise texture. <ul> <li>The uniform water phantom is a cylindrical phantom filled with water-equivalent attenuation material.</li> </ul> </li> </ul>
Binary data file needed for the Shen et al. (2011) equation of state implemented in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is downloaded automatically from this repository on running Phantom **</p> <p>Contains information needed to load the <a href="http://adsabs.harvard.edu/abs/2011PhRvC..83c5802S">Shen, Horowitz & Teige (2011)</a> equation of state for nuclear matter in Phantom simulations</p> <p>The data file is a binary data file that enables a fast read of the information listed in the ascii tables given in the supplementary material of the Shen et al paper. The original ascii data files can be found here:</p> <p><a href="https://journals.aps.org/prc/supplemental/10.1103/PhysRevC.83.035802">https://journals.aps.org/prc/supplemental/10.1103/PhysRevC.83.035802</a></p> <p>For information on how to read this file, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_shen.f90">eos_shen.f90</a>)</p>
Turbulence pattern files used for star cluster formation in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are automatically downloaded by Phantom on running the code **</p> <p>The files here are sample cubes containing turbulent driving patterns for the velocity field (vx, vy and vz) used to initiate star cluster formation simulations in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code</p> <p>These can be used to set up initial conditions for a set of simulations similar to those shown in <a href="http://adsabs.harvard.edu/abs/2003MNRAS.339..577B">Bate, Bonnell & Bromm (2003)</a>. The files here are not the original driving patterns used in the BBB03 simulations, but have the same structure, and give a default driving pattern that can be used without having to re-generate the files. A similar set of files was used for the simulations published in <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..105L">Liptai et al. (2017)</a>.</p> <p>The files were generated with a piece of code written by Volker Bromm, which was originally part of Matthew Bate's sphNG simulation code.</p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/velfield_fromcubes.f90">src/setup/velfield_fromcubes.f90</a>)</p>
Data files for the tabulated MESA equation of state in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<div> <div> <div> <p>** These tables are automatically downloaded from this repository when running phantom **<br><br>This tabulated equation of state in PHANTOM is adapted from the logPgas − Temperature equation of state tables provided with the open source package Modules for Experiments in Stellar Astrophysics MESA (Paxton et al. 2011). Details of the data, originally compiled from blends of equations of state from Saumon, Chabrier, & van Horn (1995) (SCVH), Timmes & Swesty (2000), Rogers & Nayfonov (2002, also the 2005 update), Potekhin & Chabrier (2010) and for an ideal gas, are outlined by Paxton et al. (2011).</p> <p>Code to read these tables is available as part of phantom (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_mesa_microphysics.f90">src/main/eos_mesa.f90</a>). The original version of these tables and the module to read them was contributed by Tom Constantino from the MUSIC code (<a href="https://ui.adsabs.harvard.edu/abs/2017A&A...600A...7Ga">Goffrey et al. 2017</a>), and the phantom implementation described in <a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. (2018)</a>.The current tables were created by Tom Reichardt for the paper Reichardt et al. (2020):<br><br><a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5333R/abstract">https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5333R/abstract</a></p> </div> </div> </div> <p>Figure 1 in Reichardt et al. (2020) shows the pressure, temperature, Gamma and P/Pideal shown as a function of internal energy and density from these tables</p>
NEMA image quality phantom acquisition on the Siemens mMR scanner
<p>NEMA image quality (IQ) phantom data acquired on the Siemens Biograph mMR PET/MR scanner. 60 minutes of PET data were acquired. The list mode acquisition and associated files required for reconstruction are provided.</p>
Tomographic X-ray data of time-dependent 3D cross phantom
<p>This is the documentation of the tomographic X-ray data of a dynamic cross phantom made available at http://www.fips.fi/dataset.php. The data can be freely used for scientific purposes with appropriate references to the data and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram with 16 or 30 time frames (depending on resolution) of 2D slices of the cross phantom, made by crossing aluminum and graphite sticks in melted candle wax and (2) the corresponding static and dynamic measurement matrices modeling the linear operation of the X-ray transform. Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.</p> <p>This new version contains added file <a href="https://zenodo.org/api/files/5c00de09-bb57-4c9e-9beb-00c63358de3c/DataStatic_560x60.mat?versionId=1e53d43d-d999-435e-bb1c-8297cf32c984">DataStatic_560x60.mat </a> with 80 time frames and <a href="https://zenodo.org/api/files/5c00de09-bb57-4c9e-9beb-00c63358de3c/DataStatic_1120x60.mat?versionId=afded407-6c88-4880-b427-fe7fd09306f4">DataStatic_1120x60.mat </a> with 230 time frames. You can run these files with the code example 2, which computes a Tikhonov regularized reconstruction using conjugate gradient algorithm.</p>
Testing phantoms & SARAS' arms position - Part 1
<p>The SARAS project will allow the next generation of surgical robots to execute minimally invasive procedures with only the main surgeon, without the need of an assistant. In this video a small team of experts of the SARAS Consortium met in Verona to test progresses of Prostate’s phantom and the positioning of the SARAS arms with respect to the phantom and the Commercial Robot.</p> <p> </p>
Optical liquid phantom characterization
<p>This dataset contains the results of the characterisation of liquid phantoms compounds and recipes that will be used to test hyperspectral systems.</p> <p>We have characterize 4 compounds: Indian Ink, Protoporphyrin IX (PpIX), Blood (human and horse) and Yeast fluorescence.</p> <p>The files contained here are:</p> <ul> <li>RawSpectraInk.zip: raw absorbance spectra of the Indian ink phantoms (Winsor & Newton, Black Indian 951) measured using a commercial spectrophotometer (PerkinElmer LAMBDA 950), between 400 and 900 nm with 0.5-nm resolution at 5 different concentrations (dilution in water) (0.2, 0.3, 0.4, 0.5 and 0.6 µl/ml).</li> <li>Spectrofluorometer_Yeast.zip. raw data of the fluorescence of baker’s yeast (<em>Saccharomyces cerevisiae</em>) diluted at a concentration of 20 mg/mL at different excitation wavelength measured using a commercial luminescence spectrometer (PerkinElmer LS 55). The illumination was sequential from 300 to 700 nm at 20-nm steps. Also contains the reference fluorescence spectra accounting from water and PS contribution without any yeast, illuminated from 300 to 400 nm at 20-nm steps</li> <li>SpectrophotometerSpectra.mat. Absorbance spectra of human blood, horse blood and PpIX.</li> </ul> <p>The horse blood was defibrinated horse blood that can be easily purchased from a chemical supplier (https://uk.vwr.com/store/product/9131472/animal-blood-serum-products-for-microbiology) and the human blood was expired human red blood cells acquired from a blood bank.</p> <p>The data here are the absorption spectra measured using a commercial spectrophotometer (PerkinElmer Lambda 750 S). For the blood, solution of 5% blood in PBS for both blood type was prepared . Then the blood samples where fully oxygenated by bubbling O2, and by monitoring the level of dissolved oxygen (DO2) in these solutions. Once fully oxygenated, the absorbance of the solution was measured in the spectrophotometer. A second sample of deoxygenated blood was measured in the same conditions. In order to induce the deoxygenation of the solution, a small quantity of sodium dithionite was added to the oxygenated solution, and the measurement was taken after the DO2 meter reading had fallen to 0%.</p> <p>The PpIX (solution of 1.2mM of PpIX in dimethyl sulfoxide (DMSO)) absorption spectra was measured with the same spectrophotometer.</p> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: the absorbance spectra of each compound</p> <p>Name: the name of each compound</p> <p> </p> <p>The rest of the files report measurements performed in reflectance in a diffuse media. The setup used to test the different components was a metallic container, of dimensions 27 × 15 × 16 cm. The sides and bottom of the container were coated with a matt black absorbing paint to prevent any reflections from the boundaries. To ensure precise measurements of the volume, particularly for the large volumes of basal solution required, a gravimetric approach was used, as weighing liquids is a valid and accurate method for determining volume. DO2 levels within the solutions were monitored using a calibrated oxygen probe. Solution pH was concurrently monitored using a pH probe. The entire setup was placed on a hot stirring plate complete with a temperature probe, set to maintain a constant 37°C. Constant stirring at 700 RPM ensured that the solution remained homogeneous.</p> <p>The optical setup was composed of a broadband light (HL-2000 UV-Vis-NIR Halogen Light Source by Ocean Optics) for the source and of a USB4000 spectrometer (Ocean Optics) for the detection. Light was guided from the light source and to the spectrometer via optical fibres with s source detector distance of 1 cm. The compositions of the baseline solution for the two phantoms is of 1400g of PBS solution (at 50mM) and 75g of Intralipid 20%.</p> <p>The files contained here are:</p> <ul> <li>SpectraPpIXalone.mat. Attenuation spectra of the PpIX (at 15uM concentration) in the baseline solution described above.</li> </ul> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: Attenuation spectra of the PpIX in the diffusive media</p> <p>Name: the name of the spectra</p> <ul> <li>DeltaA_Human_Horse_Blood.mat: Change in attenuation between oxygenated and deoxygenated blood for both horse and human blood. 5 mL of blood were added to the baseline solution. The blood was deoxygenated using N<sub>2</sub>.</li> </ul> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: Change in attenuation between deoxygenated and oxygenated blood</p> <p>Name: the name of the spectrum</p> <ul> <li>SpectraBloodPpIX.mat: Attenuation spectra of the blood with and without PpIX (same quantities as above) both for oxygenated and deoxygenated states.</li> </ul> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: the attenuation spectra</p> <p>Name: the name of the spectra</p>
GATE simulated cylindrical PET with NEMA-like phantom
<p>This is a GATE-simulated data using a cylindrical PET scanner (based on the GATE cylindrical PET example) and a NEMA-like phantom. Included are the sinograms created by OMEGA software, for both TOF and non-TOF cases as mat-files as well as normalization correction coefficients. You can open these mat-files in MATLAB, Octave, Python, Julia or in practically any other language. This data can be also used as a testing data for OMEGA. Included is also the ground truth image (i.e. the source image), attenuation image as created by GATE, as well as the original ROOT files. The ROOT file package also contains the original macros that give details on the scanner and the phantom.</p> <p>The sinogram data contains several different sinograms. raw_SinM is the raw sinogram with no modifications, SinM contains normalization and randoms correction precorrected (not available for TOF data), SinDelayed contains delayed coincidences, SinTrues the trues, SinRandoms the true randoms, SinScatter the true scattered photons, appliedCorrections show the corrections applied to SinM and RandProp and ScatterProp show whether variance reduction or smoothing was applied to the delayed coincidences or (not present) scatter estimation data. The attenuation data is already correctly scaled and is saved as a MetaImage file.</p> <p>Also included is the ground truth, or original source, image. This is saved as the variable C. RA is the randoms image while SC is the scatter image. The latter two are in singles mode, i.e. they show the locations of the photons that either were random (two different events) or scattered along the way.</p> <p>The normalization data works for other measurements with the same scanner as long as the sinogram dimensions remain the same. OMEGA will automatically use the normalization data if it's present in the mat-files folder.</p> <p>This new version includes also the detector coordinates for each measurement. This is mainly for OMEGA-example showcasing the use of custom data. See, for example, <a title="https://github.com/villekf/OMEGA/blob/master/main-files/custom_detector_exampleSimple.m" href="https://github.com/villekf/OMEGA/blob/master/main-files/custom_detector_exampleSimple.m" target="_blank" rel="noopener">https://github.com/villekf/OMEGA/blob/master/main-files/custom_detector_exampleSimple.m</a></p>
Mediso SCP PET NEMA IQ phantom
<p>NEMA iQ Phantom™ with Ge68</p> <p>includes hot sphere and cold sphere with internal diameters of 10, 13, 17, 22, 28, and 37 mm</p> <p>acquired with a Mediso AnyScan SCP<br>* 60 mins <br>* 1 bed position</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>
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.