Ultrasound deep beamforming using a multiconstrained hybrid generative adversarial network

•To increase the ease-of-use of the MC-HGAN beamformer, we establish an end-to-end mapping between RF data and the output image through a hybrid GAN model.•The hybrid GAN model is composed of an intrinsic learning module, a perceptual learning module, and a fusion module, which are embedded as an in...

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Vydané v:	Medical image analysis Ročník 71; s. 102086
Hlavní autori:	Zhou, Zixia, Guo, Yi, Wang, Yuanyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.07.2021 Elsevier BV
Predmet:	Algorithms Beamforming Computer applications Datasets Deep learning Emission analysis Feature extraction Generative adversarial networks High-quality ultrasound imaging Hybrid generative adversarial networks Image quality Image reconstruction Intermediates Machine learning Multiconstrained training strategy One-shot mapping Plane waves Radio frequency Radio frequency signal Radio signals Spatial discrimination Spatial resolution Training Ultrasonic imaging Ultrasound High-quality ultrasound imaging One-shot mapping Hybrid generative adversarial networks Multiconstrained training strategy Radio frequency signal
ISSN:	1361-8415, 1361-8423, 1361-8423
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