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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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Østvik, A., Smistad, E., Espeland, T., Berg, E. A. R., & Løvstakken, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_35 · Cited by 28
Complex principal components for robust motion estimation
Mauldin, F. W., Viola, F., & Walker, W. F. (2010) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2010.1710 · Cited by 27
Bayat, M., & Fatemi, M. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461638 · Cited by 27
Learning beamforming in ultrasound imagingPreprint
Vedula, S., Senouf, O. Y., Zurakhov, G., Bronstein, A. M., Michailovich, O., & Zibulevsky, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.08043 · Cited by 27
Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging
Vishnevskiy, V., Sanabria, S. J., & Göksel, O. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00129-2_14 · Cited by 25
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Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks
Goudarzi, S., Asif, A. A., & Rivaz, H. J. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759216 · Cited by 17
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Deep Algorithm Unrolling for Blind Image DeblurringPreprint
Li, Y., Tofighi, M., Geng, J., Monga, V., & Eldar, Y. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.03493 · Cited by 15
Self-citationEnd-to-End Learning-Based Ultrasound ReconstructionPreprint
Simson, W. A., Göbl, R., Paschali, M., Krönke, M., Scheidhauer, K., Weber, W., & Navab, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04696 · Cited by 15
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Universal Deep Beamformer for Variable Rate Ultrasound ImagingPreprint
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Learning Doppler with Deep Neural Networks and its Application to Intra-Cardiac Echography
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Deep learning for automated detection of B-lines in lung ultrasonography
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Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed ImagingPreprint
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Front-end electronics for cable reduction in Intracardiac Echocardiography (ICE) catheters
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FoCUS: Fourier-Based Coded Ultrasound
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Self-citationCompressive Multiplexing of Ultrasound Signals
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An Algorithm Unrolling Approach to Deep Image DeblurringPreprint
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Deep learning for automated detection of B-lines in lung ultrasonography
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Towards sub-Nyquist tissue Doppler imaging using non-uniformly spaced stream of pulses
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Self-citationDual stage beamforming in the absence of front-end receive focusing
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Flagged first, then by citations
Research works printed but not linked: 3
- goodfellow, 2016, Deep Learning
- hamilton, 1998, Nonlinear Acoustics, 237
- 10.1038/nature16066 · DOI
Cited by
Sorted by citations
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
Monga, V., Li, Y., & Eldar, Y. C. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.3016905 · Cited by 1,589
Self-citationKumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040
RetractedRoy, S., Menapace, W., Oei, S. P., Luijten, B., Fini, E., Saltori, C., Huijben, I. A. M., Chennakeshava, N., Mento, F., Sentelli, A., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2994459 · Cited by 597
Communication-efficient federated learning
Chen, M., Shlezinger, N., Poor, H. V., Eldar, Y. C., & Cui, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024789118 · Cited by 397
Self-citationShlezinger, N., Whang, J., Eldar, Y. C., & Dimakis, A. G. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3247480 · Cited by 470
Self-citation
Metadata
- 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
- 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
About these checks
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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