| --- |
| license: cc-by-4.0 |
| viewer: false |
| --- |
| |
| <h1 align="center" style="font-size: 2.5em; font-weight: 800; letter-spacing: -0.035em; line-height: 1.05; margin-bottom: 0.3em;"> |
| <img src="assets/logo-glow-transparent.svg" alt="" height="100" style="display: inline; height: 1.5em; margin: 0; vertical-align: -0.3em;"> OpenH-RF |
| </h1> |
|
|
| <p align="center"><em>The largest, openly licensed dataset of medical ultrasound channel capture data</em></p> |
|
|
|  |
|
|
| ℹ️ This repository is currently available in early access to project contributors. |
|
|
| ## Dataset Description |
|
|
| OpenH-RF is a community-driven dataset initiative building the open, shared foundation needed to train and evaluate AI models built on pre-beamformed (channel capture) medical ultrasound measurements. |
|
|
| This dataset is a collection of RF samples and metadata in the [`zea` file format](https://github.com/tue-bmd/zea) from a variety of tasks and applications, including ultrasound localization microscopy, ultrasound computer tomography, b-mode, flow imaging and more. |
|
|
| Each subdirectory here holds a data card and a processing pipeline. The matching reconstruction scripts, one runnable reference reconstruction per subset, are in the companion repository, [github.com/open-h/OpenH-RF](https://github.com/open-h/OpenH-RF). |
|
|
| This dataset is ready for commercial or non-commercial uses. |
|
|
| ## Dataset Owner |
|
|
| NVIDIA Corporation |
|
|
| ## Contributing Organizations |
|
|
| NVIDIA, Stanford University, Eindhoven University of Technology, Tel Aviv University, Siemens Healthineers, Resolve Stroke, University of British Columbia, KAIST, Barreleye Inc., Seoul National University Bundang Hospital, us4us Ltd., University of Colorado Boulder, Vanderbilt University, University of North Carolina at Chapel Hill, Weizmann Institute of Science, University of Oslo, Technical University of Munich, Politecnico di Torino, University of Twente, Weill Cornell Medicine, Worcester Polytechnic Institute, IHU Strasbourg, University of Basel, Concordia University, Mosaic Intelligence, Technion - Israel Institute of Technology, University of Waterloo, Dartmouth College, Polytechnique Montréal |
|
|
| ## Dataset Stewardship |
|
|
| OpenH-RF community & Steering Group |
|
|
| ## Dataset Creation Date |
|
|
| September 2026 |
|
|
| ## Versioning |
|
|
| v1.0.0 |
|
|
| **Previous Version(s):** no previous version |
|
|
| ## License/Terms of Use |
|
|
| CC-BY-4.0 |
|
|
| ## Intended Usage |
|
|
| Researchers and builders interested in training ultrasound reconstruction models, RF world foundation models, or RF language models. |
|
|
| ## Dataset Characterization |
|
|
| ### Data Collection Method |
|
|
| Hybrid: Manually-Collected, Automatic/Sensors, Synthetic |
|
|
| ### Labeling Method |
|
|
| Hybrid: Manually-Labeled, Automatic/Sensors, Synthetic |
|
|
| ## Dataset Format |
|
|
| `zea` HDF5 format >= 0.1.4. This release contains files with `zea_version` 0.1.4, 0.1.5, and 0.1.6. |
|
|
| Channel capture data and related meta data are saved in HDF5 data files using a specification defined for the `zea` library. |
|
|
| ## Dataset Quantification |
|
|
| 19,471 files, 39.07 TB, 33 dataset subdirectories. |
|
|
| ## Reference(s) |
|
|
| https://github.com/open-h/OpenH-RF |
|
|
| ## Ethical Considerations |
|
|
| NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse. |
|
|
| Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
|
|
