Enhanced Radon Domain Beamforming Using Deep-Learning-Based Plane Wave Compounding

Gino Jansen, Navchetan Awasthi, Hans Martin Schwab, Richard Lopata

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

6 Citaten (Scopus)


In recent years, ultrafast ultrasound imaging has received a lot of attention. However, ultrafast imaging requires large data transfers in short periods of time. Therefore, methods to reduce this data load, while maintaining image quality, are of crucial importance. In the present study, a neural net (NN) is developed that processes ultrasound data in the Radon domain (RD). By using RD data as input, the NN infers an RD pixel-wise weight mask. As such, the NN makes an informed decision on which values it negates to enhance images. The NN is trained to approximate an image of 51 compounded plane waves (PWs) from a 3 PW input. This study shows that the proposed method can match the gCNR of a 51 PW compounded image, using only 3 PWs. This method can be employed in ultrasound systems to reduce data transfer rates in ultrafast imaging and enhance image quality.

Originele taal-2Engels
Titel2021 IEEE International Ultrasonics Symposium (IUS)
UitgeverijInstitute of Electrical and Electronics Engineers
StatusGepubliceerd - 15 nov. 2021
Evenement2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, Xi'an, China
Duur: 11 sep. 201116 sep. 2011


Congres2021 IEEE International Ultrasonics Symposium, IUS 2021
Verkorte titelIUS 2021

Bibliografische nota

Publisher Copyright:
© 2021 IEEE.


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