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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)
2 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
Titel2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's1333-1337
Aantal pagina's5
ISBN van elektronische versie978-1-4799-8131-1
DOI's
StatusGepubliceerd - 1 mei 2019
Evenement44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, Verenigd Koninkrijk
Duur: 12 mei 201917 mei 2019

Congres

Congres44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
LandVerenigd Koninkrijk
StadBrighton
Periode12/05/1917/05/19

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  • Citeer dit

    Luijten, B., Cohen, R., de Bruijn, F. J., Schmeitz, H. A. W., Mischi, M., Eldar, Y. C., & van Sloun, R. J. G. (2019). Deep learning for fast adaptive beamforming. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (blz. 1333-1337). [8683478] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2019.8683478