Deep learning in ultrasound imaging
Open accessAbstract
In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.
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- Authors
- van Sloun, R. J. G., Cohen, R., & Eldar, Y. C.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Weizmann Institute of Science (IL)
- Venue
- Proceedings of the IEEE
- Published
- 2020-01
- Volume
- 108
- Issue
- 1
- Pages
- 11-29
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2955895861
- Cited by
- 316
- References
- 127
- Citations by year
- 2026: 25 · 2025: 48 · 2024: 44 · 2023: 43 · 2022: 51 · 2021: 75 · 2020: 26 · 2019: 4
- FWCI
- 25.78
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- TU/e Research Portal (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Physical Sciences › Engineering › Biomedical Engineering › Photoacoustic and Ultrasonic Imaging
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Keywords
- deep learning, ultrasonography, adaptive beamforming, color Doppler ultrasound, clutter suppression, super-resolution ultrasound imaging, radiofrequency data, sparsity
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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