Cited by 316Ultrasound Imaging and ElastographyPhotoacoustic and Ultrasonic ImagingUltrasonics and Acoustic Wave Propagation

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Deep Learning in Ultrasound Imaging

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van Sloun, Ruud J. G., Cohen, Regev, Eldar, Yonina C.·2020·Proceedings of the IEEE

DOI 10.1109/jproc.2019.2932116

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Abstract

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)
ORCID: van Sloun, Ruud J. G.
Cohen, Regev
Eldar, Yonina C.
Venue: Proceedings of the IEEE
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-01
Volume: 108
Issue: 1
Pages: 11-29
ISSN: 0018-9219 (ISSN-L)
1558-2256
DOI: 10.1109/jproc.2019.2932116
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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