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3,309 results for “MRI”
QUIQI II Dataset - Statistical analyses of motion-corrupted MRI relaxometry data
<p>QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.</p> <p>The complete support package for the QUIQI method includes:</p> <ol> <li>A copy of the original analysis code used to compile the results presented in the original scientific publication (doi: 10.5281/zenodo.7612032) </li> <li>A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data.</li> </ol> <p><br> The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.<br> <strong>The material provided here only concerns part 2. of the QUIQI support package described above - subset of the data used in the original publication.</strong></p>
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research [V1.6]
<p>V1.6 release notes:</p> <ul> <li>The test subset is released, which contains T1w (6 repetitions/subject), T2w (6 repetitions/subject), and FLAIR (4 repetitions/subject) data from 25 new subjects. These data have passed motion inspection, but one should note that due to the doubled repetition numbers, the average inter-contrast motions are around twice larger than those in the training and validation subsets. To facilitate users, we release the ground truth images as well, but please do not use them during hyperparameter tuning.</li> </ul> <p>V1.5 release notes:</p> <ul> <li>T1w Gradient echo (GRE) data for all 183 subjects are released. Note that the phase encoding direction for GRE data is in the AP direction, different from other contrasts. These GRE data were not checked for motions.</li> <li>A few incorrect records of patient_id were corrected in the H5 files.</li> <li>Scans 2022062708 and 2022062709 were removed due to duplication. Two new scans were added to replace them.</li> </ul> <p>V1.1 release notes:</p> <ul> <li>Please refer to https://www.nature.com/articles/s41597-023-02181-4 for details.</li> </ul>
MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus
<p>MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.<br> <br> Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; <a href="https://sammba-mri.github.io/">https://sammba-mri.github.io/</a>). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.</p> <p>if used for publication please cite: </p> <p><strong>A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy</strong><br> Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq<br> Jan 2019<br> <strong>NeuroImage</strong> 185, 85-95<br> DOI: 10.1016/J.NEUROIMAGE.2018.10.010<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811918319694">https://www.sciencedirect.com/science/article/pii/S1053811918319694</a></p>
InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification.
Open the record for dataset details and reuse information.
CAMRI Mouse Brain MRI Data
Open the record for dataset details and reuse information.
CAMRI Rat Brain MRI Data
Open the record for dataset details and reuse information.
MRI and unbiased averages of wild muskrats (Ondatra zibethicus) and red squirrels (Tamiasciurus hudsonicus)
<p>Preprocessed data from:</p> <p>Amuno, S., Rudko, D.A., Gallino, D., Tuznik, M., Shekh, K., Kodzhahinchev, V., Niyogi, S., Chakravarty, M.M., Devenyi, G.A., 2020. Altered neurotransmission and neuroimaging biomarkers of chronic arsenic poisoning in wild muskrats (Ondatra zibethicus) and red squirrels (Tamiasciurus hudsonicus) breeding near the City of Yellowknife, Northwest Territories (Canada). Sci. Total Environ. 707, 135556. https://doi.org/10.1016/j.scitotenv.2019.135556</p> <p> </p> <p>As well as averages and brain masks generated during DBM processing. Files in both MINC2 and NIFTI format.</p>
Free Breathing Lung MRI Dataset at 3T
<p> This is a free breathing lung MRI dataset. Data is acquired with 3D ultra-short te (UTE) radial sequence.</p>
Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI
<p>MRI raw data set of a volunteer examination for the project "Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI". The upload includes the measured k-space for 8 different breathing phases, the respective image reconstructions and the coil sensitivity maps required for the Conjugate Gradient SENSE reconstruction.</p> <p>C++ source code for image reconstruction can be downloaded at <a href="https://github.com/expRad/4d_lung">https://github.com/expRad/4d_lung</a>.</p> <p> </p>
Data for: Cardiac and Respiratory Self-Gating in Radial MRI using an Adapted Singular Spectrum Analysis (SSA-FARY)
<p>Magnetic Resonance Imaging measurement data used in our paper about self-gating with SSA-FARY (DOI: <a href="https://doi.org/10.1109/TMI.2020.2985994">10.1109/TMI.2020.2985994</a>). Cardiac data was obtained from eight volunteers with no known illness using single-slice radial (SS), simultaneous multi-slice radial (SMS), and stack-of-stars (SoS) FLASH and bSSFP sequences and is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p>
Open data repository, Boehm-Sturm et al., Phenotyping placental oxygenation in Lgals1 deficient mice using 19F MRI
<p>Open data repository of journal article "Phenotyping placental oxygenation in Lgals1 deficient mice using <sup>19</sup>F MRI"</p>
Beneath the SURFace: An MRI-like View into the Life of a 21st Century Datacenter
<p>This is a trace archive of metrics collected from the Lisa cluster at SURFsara associated with the article that will be published in USENIX;login: in July 2020.</p> <p>Github repository which contains documentation as well as scripts required to replicate the work from the login paper: <a href="https://github.com/sara-nl/SURFace">https://github.com/sara-nl/SURFace</a></p> <p>Real-world data can be instrumental in answering detailed questions: How do we know which assumptions regarding large-scale systems are realistic? How do we know that the systems we build are practical? How do we know which metrics are important to assess when analyzing performance? To answer such questions, we need to collect and share operational traces containing real-world, detailed data. Not only is the presence of low-level metrics significant, but they also help avoid biases through their variety. To address variety, there exist several types of archives, such as the Parallel Workloads Archive, the Grid Workloads Archive, and the Google or Microsoft logs (the Appendix gives a multi-decade overview). However, such traces mostly focus on higher-level scheduling decisions and high-level, job-based resource utilization (e.g., consumed CPU and memory). Thus, they do not provide vital information to system administrators or researchers analyzing the full-stack or the OS-level operation of datacenters. </p> <p><br> The traces we are sharing have the finest granularity of all other open-source traces published so far. In addition to scheduler-level logs, they contain over <em>100 low-level, server-based metrics, going to the granularity of page-faults or bytes transferred through a NIC</em>.</p> <p> </p> <p><strong>The SURF archive</strong></p> <p>Datacenters already exhibit unprecedented scale and are becoming increasingly more complex. Moreover, such computer systems have begun having a significant impact on the environment, for example, training some machine learning models has sizable carbon footprints. As our recent work on modern datacenter networks shows, low-level data is key to understanding full-stack operation, including high-level application behavior. We advocate it is time to start using such data more systematically, unlocking its potential in helping us understand how to make (datacenter) systems more efficient. We advocate that our data can contribute to a more holistic approach, looking at how the multitude of these systems work together in a large-scale datacenter. </p> <p> </p> <p>This archive contains data from the Dutch National Infrastructure, Lisa.</p> <p> </p> <p><a href="https://userinfo.surfsara.nl/systems/lisa/description">Description of the Lisa system</a></p> <p> </p> <p><a href="https://userinfo.surfsara.nl/systems/cartesius/description">Description of the Cartesius system</a></p> <p> </p> <p>We gather metrics, at 15-second intervals, from several data sources:</p> <ul> <li> <p><strong>Slurm:</strong> all job, task, and scheduler related data, such as running time, queueing time, failures, servers involved in the execution, organization in partitions, and scheduling policies.</p> </li> <li> <p><strong>NVIDIA Management Library (NVML): </strong>per GPU, data such as power metrics, temperature, fan speed, or used memory.</p> </li> <li> <p><strong>IPMI: </strong>per server, data such as power metrics and temperature.</p> </li> <li> <p><strong>OS-level: </strong>from either <em>procfs</em>, <em>sockstat,</em> or <em>netstat</em> data: low-level OS metrics, regarding the state of each server, including CPU, disk, memory, network utilization, context switches, and interrupts. </p> </li> </ul> <p> </p> <p>We also release other kinds of novel information, related to datacenter topology and organization.</p> <p> </p> <p>The audience we envision using these metrics is composed of systems researchers, infrastructure developers and designers, system administrators, and software developers for large-scale infrastructure. The frequency of collecting data is uniquely high for open-source data, which could allow these experts unprecedented views into the operation of a real datacenter.</p> <p><br> * Note: For the GPU metrics a number of nodes were introduced to the system in late Feb/start of March and as such these specific nodes have no data available in January and February which may cause irregularities. The github will contain code snippets that will show how to filter this data such that this is not a problem and how to graph the parquet data (this is pending update in the next few days.</p>
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</p>
Seven Tesla MRI of the pelvis of 11 volunteers
<p>MRI dataset of the pelvis of 11 young volunteers.</p> <p>Each volunteer set contains two anonymized dicom 3D image series:</p> <p>1. Anatomical overview of the pelvic area with a 3D dataset of 256 images, 'lipid excitation'</p> <p>2. Multi gradiënt echo dataset of 5 gradiënt echoes, 3D datset of 256 images per echo (1280 images), 'water excitation'.</p> <p>See the following PlosOne publication for details on acquisition parameters:</p> <p><strong>Magnetic resonance imaging at ultra-high magnetic field strength: an in vivo assessment of number, size and distribution of pelvic lymph nodes</strong></p> <p>Ansje S. Fortuin, Bart W.J. Philips, Marloes M.G. van der Leest, Mark E. Ladd, Stephan Orzada, Marnix C. Maas, Tom W.J. Scheenen</p>
PsPM-SC2F: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS, performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 18 healthy unmedicated participants (9 females and 9 males, age range: 18 - 34 years, mean age: 24.8 +/- 4.2) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tone (4 s), and up or down frequency sweep sounds, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less.</p>
Dataset related to publication "Heating of hip joint implants in MRI: the combined effect of radiofrequency and switched-gradient fields"
<p>The datasets reported in the figures of the article "Heating of hip joint implants in MRI: the combined effect of radiofrequency and switched-gradient fields" by Alessandro Arduino, Umberto Zanovello, Jeff Hand, Luca Zilberti, Rüdiger Brühl, Mario Chiampi and Oriano Bottauscio, accepted for publication to Magnetic Resonance Imaging on Dec. 9th, 2020. A link to the article will be given after its final publication.</p>
24-shell diffusion MRI data
<p>24-shell diffusion MRI used in <em>Huynh, K.M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.H., Lin, W., Shen, D. and Yap, P.T., 2020. Probing tissue microarchitecture of the baby brain via spherical mean spectrum imaging. IEEE Transactions on Medical Imaging, 39(11), pp.3607-3618.</em></p>
Replication Data for: Continuously moving table MRI with golden angle radial sampling
<p>Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.</p>
Annotated MRI and ultrasound volume images of the prostate
<p><strong>Introduction</strong></p> <p>The <em>Surgical Planning Laboratory (SPL) </em>and the <em>National Center for Image Guided Therapy (NCIGT) </em>are making this dataset available as a resource to aid in the development of algorithms and tools for deformable registration, segmentation and analysis of prostate magnetic resonance imaging (MRI) and ultrasound (US) images. </p> <p><strong>Description</strong></p> <p>This dataset contains anonymized images of the human prostate (N=3 patients) collected during two sessions for each patient:</p> <ol> <li>MRI examination of the prostate for the purposes of disease staging.</li> <li>US examination of the prostate for the purposes of volumetric examination in preparation to the brachytherapy implant.</li> </ol> <p>These are three-dimensional (multi-slice) scalar images.</p> <p>Image files are stored using NRRD file format (files with .nrrd extension), see details at http://teem.sourceforge.net/nrrd/format.html. Each image file includes a code for the case number (internal numbering at the research site) and the modality (US or MR).</p> <p>Image annotations were prepared by Dr. Fedorov (no professional training in radiology) and Dr. Tuncali (10+ in prostate imaging interpretation). Annotations include</p> <ol> <li>Manual contouring (segmentation) of the whole prostate gland, performed in 3D Slicer software. These segmentation images are coded in the same fashion as the image files, and saved in NRRD format, with "-label" suffix.</li> <li>Manually placed points (fiducials) corresponding to the location of urethra entry into the prostate at base (coded as UB), verumontanum (VM), urethra entry into the prostate at apex (UA), as well as centroids of cysts and calcifications. UB, UA and VM locations are annotated both in MR and US for all cases, while cysts and calcifications are annotated when applicable. Fiducial points are stored in comma-separated CSV-style format adopted by 3D Slicer software (.fcsv file extension). There is one row per point in these files, encoding the location of the point in RAS coordinate space relative to the image data, and the name of the point.</li> </ol> <p><strong>Viewing the collection</strong></p> <p>We tested visualization of images, segmentations and fiducials in 3D Slicer software, and thus recommend 3D Slicer as the platform for visualization. 3D Slicer is a free open source platform (see http://slicer.org), with the pre-compiled binaries available for all major operating systems. You can download 3D Slicer at http://download.slicer.org.</p> <p><strong>Acknowledgments </strong></p> <p>Preparation of this data collection was made possible thanks to the funding from the National Institutes of Health (NIH) through grants R01 CA111288 and P41 RR019703.</p> <p>If you use this dataset in a publication, please cite the following manuscript. You can also learn more about this dataset from the publication below.</p> <p>Fedorov, A., Khallaghi, S., Antonio Sánchez, C., Lasso, A., Fels, S., Tuncali, K., Sugar, E. N., Kapur, T., Zhang, C., Wells, W., Nguyen, P. L., Abolmaesumi, P. & Tempany, C. Open-source image registration for MRI–TRUS fusion-guided prostate interventions. <em>Int J CARS</em> <strong>10,</strong> 925–934 (2015). https://pubmed.ncbi.nlm.nih.gov/25847666/</p> <p><strong>Contact</strong></p> <p>Andrey Fedorov, fedorov@bwh.harvard.edu</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>
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.