About¶

zea is a toolbox intended to support research towards cognitive ultrasound imaging, a concept described in Van Sloun [A-1]. The idea is to close the action-perception loop in ultrasound imaging, where acquisition and reconstruction are tightly coupled to tackle some of the persistent challenges in the field of ultrasound imaging.

While the full realization of cognitive ultrasound imaging remains an ongoing effort, we hope this toolbox will help spur further research and development in the field.

High-level overview of an ultrasound perception-action loop implemented in zea.

Note

What’s in a name?

It’s just a name… If we have to give it some meaning: zea is derived from the scientific name for corn, Zea mays, a staple food crop. If you look at the logo, you can see that the kernels of the corn cob have some resemblance with either a sensing matrix or possibly the elements of an array. The high-dimensional and structured nature of the corn cob also reflects the complexity of ultrasound data.

Core maintainers¶

Tristan Stevens, Eindhoven University of Technology, The Netherlands
Wessel van Nierop, Eindhoven University of Technology, The Netherlands
Ben Luijten, Eindhoven University of Technology, The Netherlands

Active contributors¶

A list of active contributors can be found on the GitHub contributors page. If you would like to contribute, please see the Contributing guide.

License¶

This project is licensed under the Apache License 2.0.

Citation¶

Please see the Citation guide for citation information of zea.

Papers¶

The following list contains some of the papers that have been published using zea. If you have used zea in your work, please consider adding it to the list by creating a pull request on GitHub. See the Contributing guide for more information.

[A-1]

Ruud JG Van Sloun. Active inference and deep generative modeling for cognitive ultrasound. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2024.

[A-2]

Vincent van de Schaft, Oisín Nolan, and Ruud JG van Sloun. Off-grid ultrasound imaging by stochastic optimization. arXiv preprint arXiv:2407.02285, 2024.

[A-3]

Tristan SW Stevens, Faik C Meral, Jason Yu, Iason Z Apostolakis, Jean-Luc Robert, and Ruud JG Van Sloun. Dehazing ultrasound using diffusion models. IEEE Transactions on Medical Imaging, 43(10):3546–3558, 2024.

[A-4]

Tristan SW Stevens, Oisín Nolan, Jean-Luc Robert, and Ruud JG Van Sloun. Sequential posterior sampling with diffusion models. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025.

[A-5]

Tristan SW Stevens, Oisín Nolan, Oudom Somphone, Jean-Luc Robert, and Ruud JG van Sloun. High volume rate 3d ultrasound reconstruction with diffusion models. arXiv preprint arXiv:2505.22090, 2025.

[A-6]

Oisín Nolan, Tristan Stevens, Wessel L. van Nierop, and Ruud Van Sloun. Active diffusion subsampling. Transactions on Machine Learning Research, 2025. URL: https://openreview.net/forum?id=OGifiton47.

[A-7]

Simon W. Penninga, Hans van Gorp, and Ruud J.G. van Sloun. Deep sylvester posterior inference for adaptive compressed sensing in ultrasound imaging. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025.