Beamspace-domain learning of minimum variance beamformer with fully convolutional network

Abstract In medical ultrasound systems, receiving beamforming is necessary to produce an ultrasonic image. Although minimum variance (MV) beamforming was developed to achieve higher image quality than commonly used delay-and-sum (DAS) beamforming, it is computationally expensive. Therefore, in this...

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Bibliographic Details
Published inJapanese Journal of Applied Physics Vol. 62; no. SJ; p. SJ1050
Main Authors Hiki, Ryuichi, Mozumi, Michiya, Omura, Masaaki, Nagaoka, Ryo, Hasegawa, Hideyuki
Format Journal Article
LanguageEnglish
Published Tokyo IOP Publishing 01.07.2023
Japanese Journal of Applied Physics
Subjects
Beamforming
Deep learning
Image quality
medical ultrasound imaging
minimum variance beamforming
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Summary:Abstract In medical ultrasound systems, receiving beamforming is necessary to produce an ultrasonic image. Although minimum variance (MV) beamforming was developed to achieve higher image quality than commonly used delay-and-sum (DAS) beamforming, it is computationally expensive. Therefore, in this study, we investigated how to convert the beamforming profile of DAS to that of MV using deep learning. The results showed that a fully convolutional network could produce an image with comparable quality to that in MV beamforming in a shorter time than the conventional MV beamformer.
Bibliography:JJAP-S1103133.R1
ISSN:0021-4922
1347-4065
DOI:10.35848/1347-4065/acbda2