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opencc-by-4.0Sep 2021View details →

Dataset for "Effective super-resolution method for paired electron microscopic images"

This is the image set used in the paper, Qian, Xu, Drummy, and Ding, 2020, “Effective super-resolution method for paired electron microscopic images,” IEEE Transactions on Image Processing, Vol. 29, pp. 7317–7330.

opencc-by-4.0Nov 2022View details →

North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"

North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/".

openmit-licenseMay 2023View details →

Human STING is a proton channel (Live-cell MGAT Super-Resolution Experiment)

hTERT-immortalized BJ1 cells (ATCC CRL-2522) expressing SEP-mRuby3 targeted to cis/medial Golgi (MGAT) were transduced with pXPR023 (lentiCRISPRv2) expressing an sgRNA targeting STING and selected with 0.1 µg/mL puromycin for 5 days. Cells were then transduced with blasticidin-STING-miRFP680 and selected using 10 µg/mL blasticidin HCl for 5 days. Cells were plated in 96-well glass-bottom plates (Greiner Bio-One) at 6,000 cells/well. After 48 hours, cells were incubated in Fluorobrite DMEM (Thermo Fisher Scientific, cat. #A1896701) medium supplemented with 10% FBS, 1% Pen-strep, and 1x GlutaMAX (Thermo Fisher Scientific, cat. #35050061) and stimulated with 1 µM diABZI (Invivogen, #tlrl-diabzi). All images were acquired using an LSM980 with Airyscan2 (Zeiss) with 37°C with 5% CO2 incubation. 8 z-stacks were acquired with 0.15 µm z-step.  Images were acquired using a 63X 1.40 NA DIC M27 objective with Immersol 518F 37°C oil. Acquired images were Airyscan processed and then analyzed as described in the image analysis section. Each frame represens one timepoint imaged every 5 minutes post diABZI treatment. Channels are: SEP (super-ecliptic pHluorin), mRuby3, and STING-miRFP680.

opencc-by-4.0May 2023View details →

High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components

Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm2 and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.

opencc-by-4.0Jun 2024View details →

FireSR: A Dataset for Super-Resolution and Segmentation of Burned Areas

# FireSR Dataset ## Overview **FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques. ## Dataset Structure The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring: - **S2**: Contains Sentinel-2 images.   - **pre**: Pre-fire Sentinel-2 images (high resolution).   - **post**: Post-fire Sentinel-2 images (high resolution). - **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.   - **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.   - **post**: Burned area labels corresponding to post-fire conditions. - **MODIS**: Contains post-fire MODIS images (lower resolution). - **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023). - **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries. ### File Naming Convention Each GeoTIFF (.tif) file is named according to the format: `CA_<year>_<province>_<id>.tif`, where: - `CA` stands for Canada. - `<year>` is the year of the wildfire event. - `<province>` is the province code (e.g., AB for Alberta, BC for British Columbia). - `<id>` is a unique identifier for the wildfire event. ### Directory Structure The dataset is organized as follows: ``` FireSR/ │ ├── dataset/ │   ├── S2/ │   │   ├── post/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   │   ├── pre/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   ├── mask/ │   │   ├── post/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   │   ├── pre/ │   │   │   ├── CA_2017_AB_204.tif │   │   │   ├── CA_2017_AB_2418.tif │   │   │   └── ... │   ├── MODIS/ │   │   ├── CA_2017_AB_204.tif │   │   ├── CA_2017_AB_2418.tif │   │   └── ... │   ├── LULC/ │   │   ├── CA_2017_AB_204.tif │   │   ├── CA_2017_AB_2418.tif │   │   └── ... │   ├── Daymet/ │   │   ├── CA_2017_AB_204.tif │   │   ├── CA_2017_AB_2418.tif │   │   └── ... ``` ### Spatial Resolution and Channels - **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4) - **MODIS Images**: 250 meters (Bands: B7, B2, B1) - **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned) - **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp) - **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland) **Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions: | Name | Units | Min | Max | Description | |------|-------|-----|-----|-------------| | dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. | | prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. | | srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. | | swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. | | tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. | | tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. | | vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. | **ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year. | Class Value | Land Cover Class | |-------------|------------------| | 1 | Water | | 2 | Trees | | 4 | Flooded Vegetation | | 5 | Crops | | 7 | Built Area | | 8 | Bare Ground | | 9 | Snow/Ice | | 10 | Clouds | | 11 | Rangeland | ## Usage Tutorial To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow: ### Step 1: Extract FireSR.tar.gz ```bash tar -xvf FireSR.tar.gz ``` ### Step 2: Tiling the GeoTIFF Files The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images: ```python import rasterio from rasterio.windows import Window import os def tile_image(image_path, output_dir, tile_size=128):     with rasterio.open(image_path) as src:         for i in range(0, src.height, tile_size):             for j in range(0, src.width, tile_size):                 window = Window(j, i, tile_size, tile_size)                 transform = src.window_transform(window)                 outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")                 with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:                     dst.write(src.read(window=window)) # Example usage tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/') ``` ### Step 3: Loading Data into a Machine Learning Model After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch: ```python import torch from torch.utils.data import Dataset from torchvision import transforms import rasterio class FireSRDataset(Dataset):     def __init__(self, image_dir, transform=None):         self.image_dir = image_dir         self.transform = transform         self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]     def __len__(self):         return len(self.image_paths)     def __getitem__(self, idx):         image_path = self.image_paths[idx]         with rasterio.open(image_path) as src:             image = src.read()         if self.transform:             image = self.transform(image)         return image # Example usage dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor()) dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True) ``` ## License This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given. ## Contact For any questions or further information, please contact: - Name: Eric Brune - Email: ebrune@kth.se

publicNov 2024View details →

Data for: High-throughput expansion microscopy enables scalable super-resolution imaging

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publicApr 2019View details →

Quantification of endogenous and exogenous protein expressions of Na,K-ATPase with super-resolution PALM/STORM imaging

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publicSep 2020View details →

Comparing Lifeact and Phalloidin for super-resolution imaging of actin in fixed cells

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publicJul 2025View details →

Electrochemically controlled switching of dyes for enhanced super-resolution optical fluctuation imaging (EC-SOFI)

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opencc-by-4.0Jul 2020View details →

Implementation of a 4Pi-SMS super-resolution microscope - Example data

4Pi-SMS image of indirectly immunostained ɑ-tubulin labelled with CF660C in a COS7 cell in water-based imaging buffer.

opencc-by-4.0Oct 2020View details →

Raw data accompanying the manuscript "Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope"

Raw data sets (.tif files) of data utilized to demonstrate 2D SIM with an unmodified multi photon intravital fluorescence microscope.

opencc-by-nc-sa-4.0Oct 2023View details →

Spatially Variant Super-Resolution (SVSR) benchmarking dataset

The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 real low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding real noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers. The associated paper can be found here: <a title="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf" href="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf">https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf</a>

live cell super-resolution data_dual_color_cell_line_SCR_deletion

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live cell super-resolution data_dual_color_cell_line_E15_deletion

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live cell super-resolution data_dual_color_cell_line_original

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live cell super-resolution data_dual_color_cell_line_Cohesin_depletion

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openother-ncMay 2022View details →

SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms

1 Description SEN2VENµS is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters. Changelog with respect to version 1.0.0 (https://zenodo.org/records/6514159) <ul> <li>All patches are now stored in indivual geoTiFF files with proper geo-referencing, regrouped in zip files per site and per category,</li> <li>The dataset now includes 20 meter resolution SWIR bands B11 and B12 from Sentinel-2 (L2A from Theia). Note that there is no HR reference for those bands, since the VENµS sensor has no SWIR band.</li> </ul> 2 Files organization The dataset is composed of separate sub-datasets embedded in separate zip files, one for each site, as described in table <a href="#org5e17b56">1</a>. Note that there might be slight variations in number of patches and number of pairs with respect to version 1.0.0, due do incorrect count of samples in previous version (an empty tensor was still accounted for). Table 1: Number of patches and pairs for each site, along with VENµS viewing zenith angle <table> <tbody> <tr> <th>Site</th> <th>Number of patches</th> <th>Number of pairs</th> <th>VENµS Zenith Angle</th> </tr> </tbody> <tbody> <tr> <td>FR-LQ1</td> <td>4888</td> <td>18</td> <td>1.795402</td> </tr> <tr> <td>NARYN</td> <td>3813</td> <td>24</td> <td>5.010906</td> </tr> <tr> <td>FGMANAUS</td> <td>129</td> <td>4</td> <td>7.232127</td> </tr> <tr> <td>MAD-AMBO</td> <td>1442</td> <td>18</td> <td>14.788115</td> </tr> <tr> <td>ARM</td> <td>15859</td> <td>39</td> <td>15.160683</td> </tr> <tr> <td>BAMBENW2</td> <td>9018</td> <td>34</td> <td>17.766533</td> </tr> <tr> <td>ES-IC3XG</td> <td>8822</td> <td>34</td> <td>18.807686</td> </tr> <tr> <td>ANJI</td> <td>2312</td> <td>14</td> <td>19.310494</td> </tr> <tr> <td>ATTO</td> <td>2258</td> <td>9</td> <td>22.048651</td> </tr> <tr> <td>ESGISB-3</td> <td>6057</td> <td>19</td> <td>23.683871</td> </tr> <tr> <td>ESGISB-1</td> <td>2891</td> <td>12</td> <td>24.561609</td> </tr> <tr> <td>FR-BIL</td> <td>7105</td> <td>30</td> <td>24.802892</td> </tr> <tr> <td>K34-AMAZ</td> <td>1384</td> <td>20</td> <td>24.982675</td> </tr> <tr> <td>ESGISB-2</td> <td>3067</td> <td>13</td> <td>26.209776</td> </tr> <tr> <td>ALSACE</td> <td>2653</td> <td>16</td> <td>26.877071</td> </tr> <tr> <td>LERIDA-1</td> <td>2281</td> <td>5</td> <td>28.524780</td> </tr> <tr> <td>ESTUAMAR</td> <td>911</td> <td>12</td> <td>28.871947</td> </tr> <tr> <td>SUDOUE-5</td> <td>2176</td> <td>20</td> <td>29.170244</td> </tr> <tr> <td>KUDALIAR</td> <td>7269</td> <td>20</td> <td>29.180855</td> </tr> <tr> <td>SUDOUE-6</td> <td>2435</td> <td>14</td> <td>29.192055</td> </tr> <tr> <td>SUDOUE-4</td> <td>935</td> <td>7</td> <td>29.516127</td> </tr> <tr> <td>SUDOUE-3</td> <td>5363</td> <td>14</td> <td>29.998115</td> </tr> <tr> <td>SO1</td> <td>12018</td> <td>36</td> <td>30.255978</td> </tr> <tr> <td>SUDOUE-2</td> <td>9700</td> <td>27</td> <td>31.295256</td> </tr> <tr> <td>ES-LTERA</td> <td>1701</td> <td>19</td> <td>31.971764</td> </tr> <tr> <td>FR-LAM</td> <td>7299</td> <td>22</td> <td>32.054056</td> </tr> <tr> <td>SO2</td> <td>738</td> <td>22</td> <td>32.218481</td> </tr> <tr> <td>BENGA</td> <td>5857</td> <td>28</td> <td>32.587334</td> </tr> <tr> <td>JAM2018</td> <td>2564</td> <td>18</td> <td>33.718953</td> </tr> </tbody> </table>   Each site zip file contains a subfolder with the site name. This subfolder contains secondary zip files for each date, following this naming convention as the pair <code>id</code>: <code>{site_name}_{acquisition_date}_{mgrs_tile}</code>. For each date, 5 zip files are available, as shown in table <a href="#org504e2aa">2</a>.Each zip file contain subfolder <code>{bands}/{resolution}/</code> in which one GeoTiFF file per patch is stored, with the following naming convention: <code>{site_name}_{idx}_{acquisition_date}_{mgr_tile}_{bands}_{resolution}.tif</code>. Pixel values are encoded as 16 bits signed integers and should be converted back to floating point surface reflectance by dividing each and every value by 10 000 upon reading. Table 2: Naming convention for zip files associated to each date. <table> <tbody> <tr> <th>File</th> <th>Content</th> </tr> </tbody> <tbody> <tr> <td><code>{id}_05m_b2b3b4b8.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B2, B3, B4 and B8 (from VENµS)</td> </tr> <tr> <td><code>{id}_10m_b2b3b4b8.zip</code></td> <td>10m patches (\(128\times128\) pix.) for S2 B2, B3, B4 and B8 (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_05m_b5b6b7b8a.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B5, B6, B7 and B8A (from VENµS)</td> </tr> <tr> <td><code>{id}_20m_b5b6b7b8a.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B5, B6, B7 and B8A (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_20m_b11b12.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B11 and B12 (from Sentinel-2)</td> </tr> </tbody> </table>   Each file comes with a master <code>index.csv</code> CSV (Comma Separated Values) file, with one row for each pair sampled in the given site. Columns are named after the <code>{bands}_{resolution}</code> pattern, and contains the full path to the corresponding GeoTiFF wihin the corresponding zip file: <code>{site}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.zip/{bands}/{resolution}/{site}_{idx}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.tif</code> 3 Licencing 3.1 Sentinel-2 patches 3.1.1 Copyright Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Note: Copernicus Sentinel-2 Level 1C data is subject to this license: <a href="https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf">https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf</a> 3.1.2 Licence Files <code>*_b2b3b4b8_10m.tif</code>, <code>*_b5b6b7b8a_20m.tif</code> and <code>*_b11b12_20m.tif</code> are distributed under the the original licence of the Sentinel-2 Theia L2A products, which is the Etalab Open Licence Version 2.0 <a href="#fn.2">2</a>. 3.2 VENµS patches 3.2.1 Copyright Value-added data processed by CNES for the Theia data centre www.theia-land.fr using VENµS satellite imagery from CNES and Israeli Space Agency. The processing uses algorithms developed by Theia's Scientific Expertise Centres. 3.2.2 Licence Files <code>*_b2b3b4b8_05m.tif</code> and <code>*_b5b6b7b8a_05m.tif</code> are distributed under the original licence of the VENµS products, which is Creative Commons BY-NC 4.0 <a href="#fn.3">3</a>. 3.3 Remaining files All remaining files are distributed under the Creative Commons BY 4.0 <a href="#fn.4">4</a> licence. 4 Note to users Note that even if the VenµS2 dataset is sorted by sites and by pairs, we strongly encourage users to apply the full set of machine learning best practices when using it : random keeping separate pairs (or even sites) for testing purpose, and randomization of patches accross sites and pairs in the training and validation sets. 5 Citing Please cite the following data paper (preprint, submitted to MDPI Data) and zenodo link when publishing work derived from this dataset: Michel, J.; Vinasco-Salinas, J.; Inglada, J.; Hagolle, O. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms. Data 2022, 7, 96. https://doi.org/10.3390/data7070096 <a href="https://zenodo.org/deposit/6514159">10.5281/zenodo.14603764</a> Footnotes: <a href="#fnr.1">1</a> <a href="https://pytorch.org/">https://pytorch.org/</a> <a href="#fnr.2">2</a> <a href="https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf">https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf</a> <a href="#fnr.3">3</a> <a href="https://creativecommons.org/licenses/by-nc/4.0/">https://creativecommons.org/licenses/by-nc/4.0/</a> <a href="#fnr.4">4</a> <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>

opencc-by-nc-4.0May 2024View details →

Transthoracic contrast echocardiography data of a patient for super-resolution ultrasound localisation microscopy

We provide sample contrast echocardiography datasets used for generation of super-resolution ultrasound localisation microscopy (ULM) images as reported in the following paper: <div> <div>Jipeng Yan, Biao Huang, Johanna Tonko, Matthieu Toulemonde, Joseph Hansen-Shearer, Qingyuan Tan, Kai Riemer, Konstantinos Ntagiantas, Rasheda A Chowdhury, Pier D Lambiase, Roxy Senior, Meng-Xing Tang. Transthoracic Ultrasound Localization Microscopy of Myocardial Vasculature in Patients, Nature Biomedical Engineering, 2024. DOI:  (<a href="https://www.nature.com/articles/s41551-024-01206-6" target="_blank" rel="noopener noreferrer">10.1038/s41551-024-01206-6</a>).</div> <div> </div> <div>The sample datasets include:</div> <div> </div> <div>"LogCompressedCEUS.mp4' is a video of CEUS images gated within one cardiac cycle after motion correction and log compression (acquistion time: 0.36s).</div> <div> </div> <div>"LinearScaleCEUS.mat" is a Matlab data file containing CEUS images after motion correction.</div> <div> </div> <div>"LinearScaleCEUSAfterNoiseReduction.mat' is a Matlab data file containing above CEUS images with noise reduced, which can be processed with our SRUS software (<a href="https://github.com/JipengYan1995/SRUSSoftware">JipengYan1995/SRUSSoftware</a>) for localisation and tracking (A brief tutorial can be found in "Usage of sample data in SRUS Software.docx").</div> <div> </div> <div>"RcvDataSample.mat' is a Matlab data file containing RF data in channels;</div> <div> </div> <div>"BFInformation.mat' is a Matlab data file containing parameters for beamforming;</div> <div> </div> <div>"Data Description.txt" contains more detailed descriptions of above data.</div> <div> </div> </div> Data from all the 10 cardiac cycles of this patient will be available in the future. If you have any questions, please contact Meng-Xing Tang (email: mengxing.tang@imperial.ac.uk).

opencc-by-4.0May 2024View details →

Dataset supporting the submission to the journal "Ocean Dynamic" and titled "Hybrid covariance super-resolution data assimilation"

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