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Dataset results
12 results for “beamforming”
In vivo rat brain for Ultrasound Localization Microscopy: raw and beamformed data.
<p><strong>Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms.</strong></p> <p><strong>Abstract:</strong></p> <p>Ultrasound Localization Microscopy (<strong>ULM</strong>) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated <em>in vitro</em>, this technique has matured and sees implementation<em> in vivo</em> for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets<em> in silico</em> and<em> in vivo</em> to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits.</p> <p>This article provides all materials and post-processing scripts and functions.</p> <p><strong>Methods:</strong></p> <p>200.000 ultrasound images have been acquired <em>in vivo </em>on a rat brain with skull removal at 1000 Hz with a 15 MHz linear probe.</p> <p>This dataset contains raw radiofrequency data (<strong>RF</strong>) and beamformed images (<strong>IQ</strong>) of the brain vascularization with flowing microbubbles (ultrasound contrast agent).</p> <p><strong>Article to be cited:</strong> Heiles, Chavignon, Hingot, Lopez, Teston and Couture.<br> <a href="http://doi.org/10.1038/s41551-021-00824-8"><em>Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy</em>, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8)</a>.</p> <p><strong>Related processing scripts and codes:</strong> <a href="https://github.com/AChavignon/PALA">github.com/AChavignon/PALA</a></p> <p><strong>Related datasets:</strong> <a href="https://doi.org/10.5281/zenodo.4343435">doi.org/10.5281/zenodo.4343435</a></p> <p><strong>Acknowledgments:</strong></p> <p>We thank Cyrille Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, BB@C, Caen, France) for animals’ preparation and perfusion of contrast agent and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France).</p>
Additional dataset concerning "Observations of mantle seismic anisotropy using array techniques: shear-wave splitting of beamformed SmKS phases"
<p>SplitRacer input and output for beams and single-station splitting measurements for event 201007290731. This dataset was used in "Observations of mantle seismic anisotropy using array techniques: shear-wave splitting of beamformed SmKS phases" by Jonathan Wolf, Daniel A. Frost, Maureen D. Long, Ed Garnero, Adeolu O. Aderoju, Neala Creasy and Ebru Bozdag. The manuscript is available at <a href="https://doi.org/10.1029/2022JB025556">https://doi.org/10.1029/2022JB025556</a>.</p>
DS4. Experimental evaluation of the TERAWAY physical layer system, implementing optical injection locking and optical beamforming, using bulk components
<p>This set comprises experimental results from the performance evaluation of the TERAWAY system in the physical layer, implementing bulk components. This performance evaluation focused on the optical generation of millimeter-wave signals implementing injection locking technique by means of inserting an optical frequency comb into the reference optical laser source, the fiber transmission of the optical signals, as well as the processing of this signal via an optical beamforming network.</p> <p>The data set consists of multiple waveforms that correspond to 16-QAM electrical signals of 500 MBaud symbol rate, at a central frequency of 60 GHz. These waveforms were obtained directly after the photodetection stage that consists of two high-speed photodetectors in order to emulate an optical beamforming scenario. The set is separated into different folders that correspond to the injection-locked and unlocked scenarios, both in back-to-back configuration and after a 25 km transmission of the signal through an optical fiber. Each folder contains the waveforms of the electrical signals, as they were generated at the photodetection stage, under various optical power values.</p>
The known pulsars detected in the GP survey in only beamformed searches.
<p><span>The known pulsars detected in the GP survey in only beamformed </span><span>searches. It shows the names and parameters of the pulsars. Detailed analysis of </span><span>these pulsars can be found in Xue et al. (2017) and Bhat et al. (2023b). As this </span><span>work is mainly focused on the imaging aspect of pulsar searching, these pulsars </span><span>are not included as part of the analysis done for this work.</span></p>
Upper Crustal Structure of the Xinfengjiang Reservoir from Ambient Noise Double Beamforming Tomography and Its Implications for Induced Seismicity
<p>The file "CC.tar.gz" contains the linearly stacked ZZ component cross-correlations for all station pairs.</p> <p>The file "Model.tar.gz" contains the 3-D upper crustal model of the Xinfengjiang Reservoir via ambient noise Double-Beamforming tomograpy.</p>
Beamforming in Noninvasive Brain–Computer Interfaces Dataset
<p>This is the dataset of 10 motor imagery subjects upon which the paper cited here is written. It is saved in the EEGLAB .set format with the digitized electrode positions included. The only issue is that the names for channels 65-128 are missing, and the head model that corresponds to these electrode locations is also poorly specified (see field Headmodel of the EEG struct). When using this data please cite:</p> <p>Grosse-Wentrup, Moritz, et al. "Beamforming in noninvasive brain–computer interfaces." <em>IEEE Transactions on Biomedical Engineering</em> 56.4 (2009): 1209-1219.</p> <p>DOI: <a href="https://doi.org/10.1109/TBME.2008.2009768">10.1109/TBME.2008.2009768</a></p>
Data for the study: Comparison of beamformer implementations for MEG source localization
<p>This data set is a part of the study 'Comparison of beamformers implementations for MEG source localization'. The dataset includes 64 phantom datasets, 2 human datasets, and 50 simulated datasets. The data also include segmented MRI files from FreeSurfer for MEG phantom (Megin Oy, Helsinki, Finland) and a human subject MRI. It also includes the used versions of the four beamforming packages (MNE-Python, FieldTrip, SPM12(DAiSS), and Brainstorm) and codes used for the analysis.</p>
Evaluation of a Binaural Beamformer (StereoZoom) in a Virtual Acoustic Environment and in Real Life
ClinicalTrials.gov study NCT03361527. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Pulsars detected in the GP survey in both imaging and beamforming searches.
<p><span>S</span><span>I</span><span> </span><span>is the mean flux density of the pulsars </span><span>in Stokes I image from this work, S</span><span>V</span><span> </span><span>is the Stokes V for the pulsars that were </span><span>detected in Stokes V image in this work and S</span><span>lit</span><span> </span><span>is the low-frequency flux density </span><span>available in the literature: ’M’ stands for MWA-image detection by Murphy et al.</span><span>(2017), ’X’ stands for MWA-incoherent beam detection by Xue et al. (2017), ’S’ </span><span>stands for MWA SMART survey detections by Bhat et al. (2023b) and ’K’ stands </span><span>for LOFAR detection by Kondratiev et al. (2016).</span><span> </span><span>α</span><span> </span><span>is the spectral index of the </span><span>pulsars.</span><span> </span><span>α</span><span> </span><span>is calculated using the flux densities of the pulsars in MWA Stokes I </span><span>image (154 MHz) and the RACS Stokes I image (888 MHz).</span></p>
This Clinical Investigation Assesses the Safety and Performance of a New Beamformer for MED-EL Cochlear Implant Recipients.
ClinicalTrials.gov study NCT07213505. IPD Sharing: NO. Countries: 1. Publications: 0.
Towards Fast Region Adaptive Ultrasound Beamformer For Plane Wave Imaging Using Convolutional Neural Networks
<p>This dataset is supplementary to the <a href="https://ieeexplore.ieee.org/document/9630930">IEEE EMBC 2021 paper titled "Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks"</a></p> <p>The dataset is in .mat format and has two variables as below:</p> <p>tofc: Time of flight corrected (delay compensated) input data</p> <p>beamformedData: The delay and sum beamformed (pre-envelope) data</p> <p>The data is in int16 format and may need to be converted to double/float for improved results.</p> <p><strong>Dataset Access: </strong>You need to download the agreement in the <a href="https://drive.google.com/file/d/1tei07_xzcOLTdHpEXUtFcgNCwjxoAynW/view?usp=sharing" target="_blank" rel="noopener">link </a>and submit the form along with the agreement in the <a href="https://forms.gle/RaXtPR12wfrhbotf9" target="_blank" rel="noopener">link</a></p>
Supplementary Data for a Feasibility Study on Boulder Detection in the Shallow Sub-Seafloor with Beamforming on Ultra-High Resolution Seismic Data
<p>Ultra high resolution multi channel seismic and multi-beam echo sounder data has been collected during the expedition He525 with R/V Heincke in March 2019 to conduct a feasibility study on boulder detection in the shallow sub-seafloor with beamforming on ultra-high resolution seismic data. The campaign of one week took place in the north of Helgoland in the German North Sea sector.</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.