Deep learning for fast adaptive beamforming

Ben Luijten, Regev Cohen, Frederik J. de Bruijn, Harold A.W. Schmeitz, Massimo Mischi, Yonina C. Eldar, Ruud J.G. van Sloun

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

45 Citations (Scopus)
5 Downloads (Pure)


The real-time nature that makes diagnostic ultrasonography so appealing to clinicians imposes strong constraints on the computational complexity of image reconstruction algorithms. As such, these typically rely on traditional delay-and-sum beamforming, a low-complexity approach that unfortunately comes at the cost of reduced image quality as compared to more advanced and content-adaptive beamformers. Here, we propose a model-aware deep learning strategy to ultrasound image reconstruction, which leverages knowledge of minimum variance beamforming while exploiting the efficiency of deep neural networks. Our approach yields high quality images with strong contrast at real-time reconstruction rates. The neural network is trained using in vivo and simulated radio frequency channel data of a single plane wave transmit, and corresponding high-quality minimum-variance beamformed reconstructions. Performance is benchmarked using simulated acquisitions from the PICMUS [1] dataset, demonstrating the convincing generalizability and image quality of the proposed beamformer.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
Publication statusPublished - 1 May 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) - Brighton Conference Centre, Brighton, United Kingdom
Duration: 12 May 201917 May 2019


Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)
Abbreviated titleICASSP 2019
Country/TerritoryUnited Kingdom
Internet address


  • Adaptive Beamforming
  • Deep Learning
  • Plane Wave Imaging
  • Ultrasound


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