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Open-H-RF: A Large-Scale Ultrasound Raw Channel Dataset Initiative

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Venue: Preprint

Authors: Tristan S.W. Stevens*, Simon W. Penninga*, Wessel L. van Nierop*, Oisín Nolan*, Felix Duelmer, Benjamin N. Frey, ... (186 authors from 38 institutions) ..., Jeremy J. Dahl†, Ruud J.G. van Sloun†, Walter Simson† (* equal first authors, † equal senior authors)

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Open-H-RF logo Open-H-RF

Medical ultrasound is a widely accessible, real-time imaging modality, yet progress in data-driven ultrasound research is constrained by the scarcity of large, open collections of raw channel data. Existing public datasets are predominantly based on reconstructed images, while the smaller number of open raw channel datasets is generally specialized to individual reconstruction or imaging tasks, limiting the development of general-purpose methods and foundation models that operate directly on ultrasound signals. While interpretive tasks can be performed on reconstructed images, many of the key technical challenges in ultrasound imaging, such as aberration correction and reverberation reduction, require the raw channel data itself. We introduce Open-H-RF, a community-driven open dataset initiative comprising approximately 40 TB of medical ultrasound raw channel data in 19,332 acquisitions, contributed by 38 institutions across academia and industry, spanning in-vivo, phantom, and simulated data. Each dataset is annotated with a number of supported tasks, ranging from generalized reconstruction, speed-of-sound estimation, and aberration correction to blood flow imaging, motion estimation, quantitative ultrasound, and image interpretation. Open-H-RF provides a unified, self-contained data format that captures raw channel data together with the acquisition, probe, metadata, and reconstruction information required for reproducible processing across scanners and acquisition schemes. The corpus is publicly hosted under a permissive CC BY 4.0 license and can be streamed programmatically and selectively at scale. By bringing heterogeneous ultrasound data into a common representation, backed by modern data infrastructure, Open-H-RF lowers the barrier to working with raw channel data and aims to open medical ultrasound research to new communities.

 

Samples from the Open-H-RF dataset: reconstructed images (top) and corresponding raw channel data (bottom)

Impression of the dataset. Top: reconstructed images from a selection of samples across the contributions. Bottom: the corresponding (demodulated) raw channel data, each showing a single transmit cropped for visualization.

@misc{stevens2026openhrf,
  title         = {Open-{{H-RF}}: {{A Large-Scale Ultrasound Raw
                   Channel Dataset Initiative}}},
  author        = {Stevens, T. S. W. and Penninga, S. W. and
                   van Nierop, W. L. and Nolan, O. and
                   Duelmer, F. and Frey, B. N. and
                   Federici, B. and van Hal, V. and
                   van Harten, L. D. and de Hoop, H. and
                   Lopata, R. and Luijten, B. and
                   van Sambeek, M. and van der Schaft, V. and
                   Schwab, H.-M. and Azampour, M. F. and
                   Navab, N. and Ali, R. and Andrzejek, A. and
                   Baek, J. and Boitnott, B. and Brevett, T. and
                   Hashemi, H. and Hyun, D. and Park, J. H. and
                   Telichko, A. V. and Thomas, C. and
                   Zhuang, L. and Aklilu, Y. and Arora, H. and
                   Barker, J. and Mackanic, A. K. and
                   McDowall, P. and McHugh, L. and
                   Pirozzi, I. and Simon-Trench, S. and
                   Bronstein, A. M. and Senouf, O. and
                   Vedula, S. and Zadok, D. and Rivaz, H. and
                   Sharifzadeh, M. and Chen, S. and
                   Luke, G. P. and Wang, S. and Wu, Yujia and
                   Verde, J. and Göbl, R. and Zettinig, O. and
                   Boctor, E. and Wu, Yixuan and Bae, H.-M. and
                   Kim, M.-G. and Kim, Y.-M. and Lee, H. and
                   Oh, S.-H. and Pinto, P. and
                   Berrada-Sounni, M. and Carley, M. and
                   Fedewa, R. J. and Gavin, S. and
                   Goldsmith, N. and Kassayan, R. and
                   Low, C. and Mehranfar, R. and
                   Bardoscia, G. and Boccardo, M. and
                   Botter, A. and Carbonaro, M. and
                   Cerone, G. L. and Cesti, E. and
                   Meiburger, K. and Seoni, S. and
                   Truscello, F. and Raiteri, B. J. and
                   Caron, L. and Gasse, M. and Ghigo, N. and
                   Gulenko, O. and Leconte, A. and
                   Lee, S. A. and Malescot, A. and
                   Martineau, E. and Porée, J. and
                   Provost, J. and Ramos-Palacios, G. and
                   Rauby, B. and Rungta, R. L. and
                   Sadikot, A. F. and Wu, A. and Xing, P. and
                   Chavignon, A. and Ferlay, C. and
                   Hingot, V. and Reberol, M. and
                   Waelbroeck, A. and Kwon, H.-S. and
                   Bucholz, J. and Guracar, I. and
                   Loftman, R. and Williams, C. and
                   Grutman, T. and Ilovitsh, T. and
                   Aleef, T. A. and Chen, P. B. and Chen, W. and
                   Chen, Y. and Frew, M. and Honarvar, M. and
                   Lee, Y. C. and Li, Z. and Moradi, H. and
                   Rohling, R. and Salcudean, S. E. and
                   Wu, Z. and Zeng, Q. and Cattin, P. C. and
                   Hadramy, S. El and Bottenus, N. and
                   Perez, A. S. and Feng, S. and Feng, Y. and
                   Jin, P. and Lin, Y. and Ostras, O. and
                   Pinton, G. and Santibanez, F. and
                   Sode, M. and Turkbey, B. and Wang, H. and
                   Wood, B. J. and Zhang, L. and Luo, S. and
                   Wiskin, J. and Austeng, A. and
                   Karabiyik, Y. and Näsholm, S. P. and
                   Rindal, O. M. H. and Bustamante, D. and
                   Mehrmohammadi, M. and Raeis, H. and
                   Hameddine, A. and Lajoinie, G. and
                   de Roo, H. and Versluis, M. and
                   Zorgdrager, R. and Au, J. S. and
                   Chee, A. J. Y. and Cohen, J. and Gu, T. and
                   Hsu, J. Y.-H. and Ling, S. and
                   Mourtzakis, M. and Nahas, H. and
                   de la Torre, P. and Xiao, D. and
                   Yu, A. C. H. and Zafiris, E. and
                   Jarosik, P. and Karwat, P. and
                   Klimonda, Z. and Lewandowski, M. and
                   Byram, B. and Khan, C. and Marshall, Z. and
                   Pan, Y.-C. and Liu, S. and Sultan, T. and
                   Hoerig, C. and Mamou, J. and Auerbach, D. and
                   Eldar, Y. C. and Wegerhoff, A. and
                   Fischer, G. S. and Katayose, S. and
                   Ma, X. and Murakami, R. and Tang, Y. and
                   Tsumura, R. and Wang, Y. and Zhang, H. K. and
                   Azizian, M. and Huver, S. and Dahl, J. J. and
                   van Sloun, R. J. G. and Simson, W.},
  year          = {2026},
  doi           = {10.48550/arXiv.2610.13616},
  url           = {https://arxiv.org/abs/2610.13616},
  eprint        = {2610.13616},
  archivePrefix = {arXiv},
}
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Heavy-Tailed Denoising Diffusion for High Frame Rate Ultrasound Coherent Plane-Wave Compounding
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