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5,531 results for “ultrasound”
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Closed Scanning Paths)
<p><strong>Elastic Wave Simulations - Closed Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Circle Paths</strong> - The acoustic source was scanned at a constant linear speed along a circular trajectories with two different diameters - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The linear scanning speed ranged from 2 to 20 m/s and is designated in the filename.</p> <p><strong>Square Paths</strong> - The acoustic source was scanned at a constant speed along square trajectories with two different edge lengths - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The scan speed ranged from 2 m/s to 10 m/s and is designated in the filename.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep</p> <p><strong>nCycles</strong> - Number of pattern repetitions</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Human Hand: Wave Patterns and Perception
<p><strong>Human Hand: Wave Patterns and Perception</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>).</p> <p>This dataset contains the in vivo response of a single participant's hand to focused ultrasound (UHEV1, Ultrahaptics) scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa). The data is provided as .mat files. The files are separated via longitudinal scanning speed, <em>v<sub>l</sub></em> = 1, 2, 4, 7, 11 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>mod</sub></em> of +-2.5 m/s yielding a zigzag path (2 cm path width). The longitudinal speed is designated in the filename. The direction of scanning - either from the wrist to the distal end of digit 2 (Distal direction) or from the distal end of digit 2 to the wrist (Proximal direction) - is also designated in the filename. Written, informed consent was gathered from the participant in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>IMPORTANT - The data is the unprocessed output from a laser doppler vibrometer (PSV-500, Polytec). The data is NOT time-aligned and must be reconstructed using the reference signal and the map of the measurement locations.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>y</strong> (NxMx2) - 3D array containing the skin velocity normal to the laser doppler vibrometer (in m/s) at N measurement locations for M timepoints and 2 repetitions</p> <p><strong>ref</strong> (NxMx2) - 3D array containing a reference voltage signal taken from the ultrasound phased array. The beginning of the reference signal can be used to time-align each of the measurements and repetitions</p> <p><strong>fs</strong> - Laser doppler vibrometer sampling rate (in Hz)</p> <p><strong>measurementLocations</strong> (Nx3) - 3D locations on the hand (x,y,z; in m) for each of N measurement locations<br> <br> </p> <p> </p> <p><strong>BehavioralDataset.zip</strong></p> <p>Contains the responses from three different perception experiments on tactile motion direction discrimination. The experiments are provided in three separate files; the results are provided as a MATLAB table. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>In the first experiment, SSW_PrimaryDataset.mat, participants (N=12) identified the direction of the focused ultrasound as either moving from the wrist to the end of digit 2 (Distal direction) or from the end of digit 2 to the wrist (Proximal direction).</p> <p>The second experiment, SSW_SecondaryDataset-Zigzag.mat, was nearly identical to the first experiment, except we cyclically repeated the stimuli such that the total integrated time in which the stimulus was applied to the skin was approximately constant between all of the different scan speeds. The participants (N=3) identified the motion direction of the focused ultrasound as either "Distal" or "Proximal" under two conditions - one in which there was no delay between our cyclical repeats (No Delay condition) and a second in which there was a 500 ms time delay between subsequent repetitions (With Delay condition).</p> <p>The third file, SSW_SecondaryDataset-Circle.mat, presents the pilot results (N=1) of a similar tactile motion experiment, except with circular trajectories (radius 2.8 cm) drawn on the palm of the hand in either a clockwise or counterclockwise direction. The stimuli were also repeated cyclically (with and without delay between repetitions), similar to experiment two.</p> <p> </p> <p> </p> <p><strong>Table Fields - SSW_PrimaryDataset.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed</strong> - Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response</strong> - Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction</strong> - True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect</strong> - Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition</strong> - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Plays</strong> - Number of times the participant felt the stimulus before selecting a response</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Zigzag.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed </strong>- Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Circle.mat</strong></p> <p><strong>Participant </strong>- Participant label</p> <p><strong>Speed </strong>- Linear speed of the focused ultrasound stimulus along the circular trajectory (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Counterclockwise" or "Clockwise"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Counterclockwise" or "Clockwise"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p>
Dataset provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>(New ! published in June 2024) Raw data :</strong> <a href="../records/11395562">https://zenodo.org/records/11395562</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Freehand ultrasound without external trackers
<blockquote> <p><strong>We have collected a new large freehand ultrasound dataset and are organising a MICCAI2024&2025 Challenges (<a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC Challenge</a>). Check <a href="../records/11178509" target="_blank" rel="noopener">Part 1</a> and <a href="../records/11180795" target="_blank" rel="noopener">Part 2</a> of the training dataset for TUS-REC2024, and <a href="https://zenodo.org/records/15224704" target="_blank" rel="noopener">Train Data</a> for TUS-REC2025. </strong></p> </blockquote> <p>Freehand US scans were acquired on both left and right forearms from 19 volunteers, using Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40), tracked by an NDI Polaris Vicra (Northern Digital Inc., Canada). On each forearm, the US probe was moved, for the study purpose, in a straight line, a ‘C’ shape and a ‘S’ shape, in a distal-to-proximal direction. These three scans were repeated, with the curvilinear transducer held (thus the US planes) perpendicular of and parallel to the forearm. B-mode images with median level of speckle reduction were recorded at ~20 fps. Each scan included frames between 36 and 430 with a size of 480×640 pixels, equivalent to a probe travel distance approximately between 100 and 200 mm. </p> <p>A total of 12 scans were acquired from each volunteer, recorded in a single ‘*.mha’ file, with the filename indicating the acquisition time. For example, “LH_Ver_S_20220425_141454.mha” means a scan acquired on 14:14:54 April 25th, 2022. The ‘valid_frames.csv’ file contains the 6 “protocols” with each arm from each volunteer: 1) RH_Par_L (right arm, straight line shape with the probe parallel to the forearm); 2) RH_Par_C (right arm, ‘C’ shape with the probe parallel to the forearm); 3) RH_Par_S (right arm, ‘S’ shape with the probe parallel to the forearm); 4) RH_Ver_L (right arm, straight line shape with the probe perpendicular of the forearm); 5) RH_Ver_C (right arm, ‘C’ shape with the probe perpendicular of the forearm); 6) RH_Ver_S (right arm, ‘S’ shape with the probe perpendicular of the forearm); 7) LH_Par_L (left arm, straight line shape with the probe parallel to the forearm); 8) LH_Par_C (left arm, ‘C’ shape with the probe parallel to the forearm); 9) LH_Par_S (left arm, ‘S’ shape with the probe parallel to the forearm); 10) LH_Ver_L (left arm, straight line shape with the probe perpendicular of the forearm); 11) LH_Ver_C (left arm, ‘C’ shape with the probe perpendicular of the forearm); 12) LH_Ver_S (left arm, ‘S’ shape with the probe perpendicular of the forearm). The ‘start’ and ‘end’ denote the start and end frame indices of a scan, respectively, in each ‘*.mha’ file. </p> <p>US images, transformation matrix obtained from the tracker, and corresponding csv file, for each scan can be found in Freehand_US_data.zip. In the “calib_matrix.csv” file, we provide a calibration matrix and a time difference in sec, obtained from our calibration experiments. </p> <p>A baseline code is provided in this <a href="https://github.com/ucl-candi/freehand" target="_blank" rel="noopener">repo</a>.</p> <p>If you find this data set useful for your research, please consider citing some of the following works: </p> <ul> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773</a></li> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14</a></li> </ul>
Dataset for 'Weld map tomography for determining local grain orientations from ultrasound'
<p>This dataset contains data files and Jupyter notebooks used to produce figures in the manuscript 'Weld map tomography for determining local grain orientations from ultrasound'.</p>
Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</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>
Ground truth data for ultrasound assessment of thoracolumbar fascia deformation/shearing
<div> <h2>Provided data/ultrasound videos</h2> </div> <div> <p>Here we provide ground truth data to validate ultrasound (US) measurement methods that assess shear or deformation of the thoracolumbar fascia (TLF). Studies that have done so are in the instance:</p> </div> <div> <ul> <li><strong>Langevin et al. (2011).</strong> Reduced thoracolumbar fascia shear strain in human chronic low back pain. BMC Musculoskeletal Disorders, 12, 203. <a href="https://doi.org/10.1186/1471-2474-12-203">https://doi.org/10.1186/1471-2474-12-203</a></li> <li><strong>Weber et al. (2022).</strong> The Influence of a Single Instrument-Assisted Manual Therapy (IAMT) for the Lower Back on the Structural and Functional Properties of the Dorsal Myofascial Chain in Female Soccer Players: A Randomised, Placebo-Controlled Trial. Journal of Clinical Medicine, 11(23), 7110. <a href="https://doi.org/10.3390/jcm11237110">https://doi.org/10.3390/jcm11237110</a></li> <li><strong>Brandl et al. (2023).</strong> Thoracolumbar fascia deformation during deadlifting and trunk extension in individuals with and without back pain. Frontiers in Medicine, 10, 1177146. <a href="https://doi.org/10.3389/fmed.2023.1177146">https://doi.org/10.3389/fmed.2023.1177146</a></li> <li><strong>Brandl et al. (2024).</strong> Quantifying thoracolumbar fascia deformation to discriminate acute low back pain patients and healthy individuals using ultrasound. Scientific Reports, 14(1), 20044. <a href="https://doi.org/10.1038/s41598-024-70982-7">https://doi.org/10.1038/s41598-024-70982-7</a></li> </ul> </div> <div> <p>The data contains ground truth of different velocities and distances from dynamic US measurements of the gel pad simulated the erector spinae muscle and upper tissue layers. For details regarding the gel pads, see <strong>Bartsch et al. (2023).</strong> Assessing reliability and validity of different stiffness measurement tools on a multi-layered phantom tissue model. Scientific Reports, 13(1), Article 1. <a href="https://doi.org/10.1038/s41598-023-27742-w">https://doi.org/10.1038/s41598-023-27742-w</a><br>For this reason, we have developed a customised device based on a linear guide driven by a stepper motor, with which the mimicked tissue layers can be moved with high precision. An explanatory video with detailed information on device setup can be found in the file: <strong>Device setup.mp4</strong></p> </div> <div> <p>Measurements for demographics were taken according to <strong>Brandl (2024).</strong> Ultrasound measurement of thoracolumbar fascia deformation. <a href="http://Protocols.io">Protocols.io</a>. <a href="https://dx.doi.org/10.17504/protocols.io.eq2lyjbmwlx9/v1">https://dx.doi.org/10.17504/protocols.io.eq2lyjbmwlx9/v1</a></p> </div> <div> <p>Measurement uncertainty for distance: <strong>+/- 0.002 mm k = 2 (95% confidence interval)</strong> and for speed: <strong>+/- 0.001 mm k = 2 (95% confidence interval)</strong> according to <strong>ISO (1993).</strong> Guide to the expression of uncertainty in measurement. International Organization for Standardization, Geneva.</p> </div> <div> <p>We further provide an example MATLAB script that demonstrate the use of the ground truth data with the open source image processing software Kinovea, <strong>Charmant, J., & contributors. (2023).</strong> Kinovea (Version 2023.1.1) Computer software. <a href="https://www.kinovea.org">https://www.kinovea.org</a></p> </div> <div> <p>A step-by-step validation protocol to facilitate the application of the ground truth data is provided along with a video showing the setup of the device in the root directory of the data.</p> <h2>Data structure</h2> </div> <div> <div> <table> <tbody> <tr> <th>Folder</th> <th>ODS Table</th> <th>Sheets</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>TLFD_demographics</td> <td>TLFD_demographics</td> <td>demographics</td> <td>contains the data</td> </tr> <tr> <td> </td> <td> </td> <td>abbreviations</td> <td>abbreviations and units used in the table</td> </tr> <tr> <td>TLFD_GT\TLFD_GT_distances</td> <td>TLFD_GT_distances</td> <td>distances</td> <td>contains ground truth distance data and filenames of ultrasound</td> </tr> <tr> <td> </td> <td> </td> <td>abbreviations</td> <td>abbreviations and units used in the table</td> </tr> <tr> <td>TLFD_GT\TLFD_GT_speeds</td> <td>TLFD_GT_speeds</td> <td>speeds</td> <td>contains ground truth speed data and filenames of ultrasound</td> </tr> <tr> <td> </td> <td> </td> <td>abbreviations</td> <td>abbreviations and units used in the table</td> </tr> <tr> <td>MATLAB</td> <td> </td> <td> </td> <td>complete demo files for speckle tracking analysis with Kinovea</td> </tr> <tr> <td>MATLAB\Readme</td> <td> </td> <td> </td> <td>detailed instructions for using the MATLAB script</td> </tr> </tbody> </table> </div> </div> <p> </p>
Off-Grid Ultrasound Imaging by Stochastic Optimization
<p>These are the data files used in the paper "Off-Grid Ultrasound Imaging by Stochastic Optimization".</p> <p>The code for the paper can be found in the corresponding <a title="github repository" href="https://github.com/vincentvdschaft/off-grid-ultrasound" target="_blank" rel="noopener">github repository</a>.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Description</strong></td> <td><strong>Transmit scheme</strong></td> <td><strong>Transducer</strong></td> </tr> <tr> <td>L11-5v_carotid1.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Verasonics L11-5V</td> </tr> <tr> <td>L11-5v_carotid2.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Verasonics L11-5V</td> </tr> <tr> <td>L11-5v_carotid3.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions.</td> <td>Philips S5-1</td> </tr> <tr> <td>S5-1_cirs.hdf5</td> <td>Acquisition of the CIRS-040 phantom in the low attenuation zone.</td> <td>80 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Philips S5-1</td> </tr> <tr> <td>cirs_simulated.hdf5</td> <td>Simulated data similar to CIRS-040 phantom.</td> <td>3 synthetic aperture transmissions.</td> <td>Similar to Philips S5-1</td> </tr> </tbody> </table> <p>A description and unit for the datasets in these files is provided in the dataset attributes.</p>
Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave models
<p>This dataset contains the skull maps and modeling results associated with the forthcoming publication "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models".</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>
Sebastian+Simmons-High-frequency quantitative ultrasound to assess the acoustic properties of engineered tissues in vitro
<p>This dataset includes raw acquired ultrasound data, processing scripts, and statistical data for acoustic property characterization of cell-free and cell-seeded fibrin hydrogels.</p>
Results of ultrasound measurements of sea-ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019
<p>This dataset details the results of testing sea ice cores collected during the SCALE 2019 Winter Cruise using ultrasound techniques. </p>
Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2024 - Train Dataset (Part 3)
<div> <blockquote> <p><strong>This Challenge will be an open-ended challenge, and we welcome your submission. Please register your team via this <a title="https://forms.office.com/e/dPg47ktV7M" href="https://forms.office.com/e/dPg47ktV7M" target="_blank" rel="noopener">form</a>. You can submit the algorithm via this <a title="https://forms.office.com/e/QChhNkLYiu" href="https://forms.office.com/e/QChhNkLYiu" target="_blank" rel="noopener noreferrer">form</a> for TUS-REC2024 Challenge, and we will test your submitted docker on the test set.</strong></p> <p><strong>We are organising TUS-REC2025 at MICCAI2025. More information is available on the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC2025 challenge website</a> and <a href="https://github.com/QiLi111/TUS-REC2025-Challenge_baseline" target="_blank" rel="noopener">Baseline code repo</a>.</strong></p> </blockquote> <p><strong>This is the third part of the Challenge dataset. <a href="../doi/10.5281/zenodo.11178509" target="_blank" rel="noopener">Link</a> to first part; <a href="../doi/10.5281/zenodo.11180795" target="_blank" rel="noopener">Link</a> to second part. <a href="../doi/10.5281/zenodo.12979481" target="_blank" rel="noopener">Link</a> to validation dataset.</strong></p> <p>For detailed information please refer to the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/TUS-REC2024/" target="_blank" rel="noopener">Challenge website</a>. Baseline code is also provided, which can be found at this <a href="https://github.com/QiLi111/tus-rec-challenge_baseline" target="_blank" rel="noopener">repo</a>.</p> <p>Dataset structure: The dataset contains 50 .h5 files. Each corresponds to one subject, storing coordinates of landmarks for 24 scans of this subject. For each scan, the coordinates are stored in numpy array with shape of [20,3]. The first column is the index of frames; the second and third columns denote the coordinates of landmarks in the image coordinate system. </p> </div> <div> <p> </p> <p><strong>Data Usage Policy:</strong></p> <ul> <li>The training and validation data provided may be utilized within the research scope of this challenge and in subsequent research-related publications. However, commercial use of the training and validation data is prohibited. In cases where the intended use is ambiguous, participants accessing the data are requested to abstain from further distribution or use outside the scope of this challenge.</li> <li>If you use our dataset in your publication, please cite the challenge paper and some of the following optional articles: <ul> <li>Challenge paper: <ul> <li><strong>Qi Li et al. "TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker." <em>arXiv preprint arXiv:<a title="https://arxiv.org/abs/2506.21765" href="https://doi.org/10.48550/arXiv.2506.21765" target="_blank" rel="noopener">2506.21765</a></em> (2025).</strong></li> </ul> </li> <li>Optional articles: <ul> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14.</a></li> </ul> </li> </ul> </li> </ul> </div>
Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2024 - Validation Dataset
<blockquote> <p><strong>This Challenge will be an open-ended challenge, and we welcome your submission. Please register your team via this <a title="https://forms.office.com/e/dPg47ktV7M" href="https://forms.office.com/e/dPg47ktV7M" target="_blank" rel="noopener">form</a>. You can submit the algorithm via this <a title="https://forms.office.com/e/QChhNkLYiu" href="https://forms.office.com/e/QChhNkLYiu" target="_blank" rel="noopener noreferrer">form</a> for TUS-REC2024 Challenge, and we will test your submitted docker on the test set.</strong></p> <p><strong>We are organising TUS-REC2025 at MICCAI2025. More information is available on the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC2025 challenge website</a> and <a href="https://github.com/QiLi111/TUS-REC2025-Challenge_baseline" target="_blank" rel="noopener">Baseline code repo</a>.</strong></p> </blockquote> <p><strong>This is the validation dataset. The training dataset is available at <a href="../doi/10.5281/zenodo.11178508" target="_blank" rel="noopener">Part1</a>, <a href="../doi/10.5281/zenodo.11180795" target="_blank" rel="noopener">Part2</a>, and <a href="../doi/10.5281/zenodo.11355499" target="_blank" rel="noopener">Part3</a>.</strong></p> <p>Acquisition devices and config: The 2D US images were acquired using an Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40). The associated position information of each frame was recorded by an optical tracker (NDI Polaris Vicra, Northern Digital Inc., Canada). The acquired US frames were recorded at 20 fps, with an image size of 480×640, without speckle reduction. The frequency was set at 6MHz with a dynamic range of 83 dB, an overall gain of 48% and a depth of 9 cm. </p> <div> <p>Scanning protocol: Both left and right forearms of volunteers were scanned. For each forearm, the US probe moves in three different trajectories (straight line shape, "C" shape, and "S" shape), in a distal-to-proximal direction followed by a proximal-to-distal direction, with the US plane perpendicular of and parallel to the scanning direction. The validation dataset contains 72 scans in total, 24 scans associated with each subject.</p> <p>For detailed information please refer to the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/TUS-REC2024/" target="_blank" rel="noopener">Challenge website</a>. Baseline code is also provided, which can be found at this <a href="https://github.com/QiLi111/tus-rec-challenge_baseline" target="_blank" rel="noopener">repo</a>.</p> <p>Dataset structure: </p> <ul> <li>Folder <code>frames</code>: contains three folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing image of each frame within this scan. Key-value pair and name of each .h5 file are explained below. <ul> <li>“frames” - All frames in the scan; with a shape of [N,H,W], where N refers to the number of frames in the scan, H and W denote the height and width of a frame. </li> <li>Notations in the name of each .h5 file: “RH”: right arm; “LH”: left arm; “Per”: perpendicular; “Par”: parallel; “L”: straight line shape; “C”: C shape; “S”: S shape; “DtP”: distal-to-proximal direction; “PtD”: proximal-to-distal direction; For example, “RH_Per_L_DtP.h5” denotes a scan on the right forearm, with ultrasound probe perpendicular of the forearm sweeping along straight line, in distal-to-proximal direction.</li> </ul> </li> </ul> </div> <div> <ul> <li>Folder <code>transfs</code>: contains three folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing transformation of each frame within this scan. Key-value pair and name of each .h5 file are explained below. <ul> <li>“tforms” - All transformations in the scan; with a shape of [N,4,4], where N is the number of frames in the scan, and the transformation matrix denotes the transformation from tracker tool space to camera space. </li> <li>Notations in the name of each .h5 file is the same as in folder <code>frames</code>.</li> </ul> </li> <li>Folder <code>landmark</code>: contains three .h5 files. Each corresponds to one subject, storing coordinates of landmarks for 24 scans of this subject. For each scan, the coordinates are stored in numpy array with a shape of [20,3]. The first column is the index of frame; the second and third columns denote the coordinates of landmarks in the image coordinate system.</li> <li><code>calib_matrix.csv</code>: The calibration matrix was obtained using a pinhead-based method. The "scaling_from_pixel_to_mm" and "spatial_calibration_from_image_coordinate_system_to_tracking_tool_coordinate_system" are provided in the “calib_matrix.csv”.</li> <li><code>dataset_keys.h5</code>: stores the paths to all the scans of the data set. Keys in “dataset_keys.h5” denotes all the available scans in validation set, in a format of “sub%03d__%s” where %03d denotes folder name, and %s denotes the scan name. For example, “sub050__LH_Par_C_DtP” means the scan in folder “050”, with file name of “LH_Par_C_DtP.h5”</li> </ul> <div> <p><strong>Data Usage Policy:</strong></p> <ul> <li>The training and validation data provided may be utilized within the research scope of this challenge and in subsequent research-related publications. However, commercial use of the training and validation data is prohibited. In cases where the intended use is ambiguous, participants accessing the data are requested to abstain from further distribution or use outside the scope of this challenge.</li> <li><span>If you use our dataset in your publication, </span>please cite the challenge paper and some of the following optional articles: <ul> <li>Challenge paper: <ul> <li><strong>Qi Li et al. "TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker." <em>arXiv preprint arXiv:<a title="https://arxiv.org/abs/2506.21765" href="https://doi.org/10.48550/arXiv.2506.21765" target="_blank" rel="noopener">2506.21765</a></em> (2025).</strong></li> </ul> </li> <li>Optional articles:<br> <ul> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14.</a></li> </ul> </li> </ul> </li> </ul> </div> </div>
Ultrasound scans of wind turbine bearings with white etching crack damage
<p>A set of ultrasound images of wind turbine bearings with white etching crack subsurface damage. Attenuation levels above -10 dB indicate subsurface damage. Five tests were run under constant loading in a laboratory test rig, with two bearings in parallel. Tests were stopped at the indicated times (in hours) when vibration levels went above a certain threshold. This typically means that one of the two bearings has failed, although this is somewhat inconclusive for the fifth test where both bearings seem to be close to failure. For each bearing two sides were scanned (indicated by A and H).</p> <p>The experiment was performed in summer-autumn 2017 by DTU Wind Energy, as part of the FP7 Integrated Research Programme in the field of Wind Energy (IRPWIND), 2nd Round of Joint Experiments.</p> <p>For more information about this dataset please contact Dr Hilmar Danielsen at DTU Wind Energy.<br> </p> <p> </p>
Ultrasound Vevo 2100 data on ascending and abdominal aneurysms in ApoE-deficient mice - baseline and early stage
<p>This dataset contains raw data of ultrasound measurements taken of the ascending and abdominal aorta of Ang II-infused mice. Data were taken at baseline (prior to pump implantation) and at an early stage of disease development. Data can be openend with the Vevo 2100 analysis software provided by Fujifilm.</p> <p> </p> <p> </p>
Quantification of Fatty Acids in Hemp Seeds (Cannabis sativa L.) and Yield Prediction Using Machine Learning for Soxhlet and Ultrasound Extraction Methods
<p>This study focuses on the quantification of fatty acids present in hemp seeds (Cannabis sativa L.) cultivated in the Ecuadorian Andes using Soxhlet and ultrasound extraction methods. The aim is to evaluate and compare the extraction efficiency of these two techniques. Furthermore, machine learning models are applied to predict extraction yields based on experimental conditions. Using locally cultivated seeds provides valuable insights into the influence of regional agro-climatic conditions on the chemical composition. The integration of predictive algorithms offers a novel approach to optimizing the extraction process, enhancing both precision and efficiency. The findings could contribute to developing sustainable extraction methods for high-value bioactive compounds in the food and pharmaceutical industries.</p>
Validation experiments of Ultrasound Image Velocimetry (UIV) applied in cohesive sediments (fluid mud)
<p>A standard ultrasound imaging transducer was towed trough mud at known velocities. During this movement ultrasound images were acquired at high frequency (+/- 350 Hz). Based on these images the relative velocity between the transducer and the mud was deduced using the OpenPIV script (Python version). The output was compared to the imposed velocity of the transducer to validate the accurcy of the UIV technique applied in mud. This was done for various velocities ranging from 500 mm/s to 1750 mm/s in increments of 250 mm/s. The density of the mud was fixed at 1.15 g/cm³ and the ultrasound frequency to 3.5 MHz. The combination of PIV algorithm applied to ultrasound images is referred to as Ultrasound Image Velocimetry (UIV).</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.