Deep learning in ultrasound imaging

Ruud J.G. van Sloun (Corresponding author), Regev Cohen, Yonina C. Eldar

Research output: Contribution to journalArticleAcademicpeer-review

138 Citations (Scopus)
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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.

Original languageEnglish
Article number8808885
Pages (from-to)11-29
Number of pages19
JournalProceedings of the IEEE
Issue number1
Publication statusPublished - Jan 2020


  • Beamforming
  • compression
  • deep learning
  • deep unfolding
  • Doppler
  • image reconstruction
  • super resolution
  • ultrasound imaging


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