Adaptive beamforming based on minimum variance (ABF-MV) using deep neural network for ultrafast ultrasound imaging

•Provides a deep neural network guided by minimum variance method for ultrafast ultrasound adaptive beamforming.•Shows that the proposed method’s beneficial effects evaluated by simulations, phantom experiments, and in-vivo experiments.•Reveals that the proposed method has the potential for ultrafas...

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Vydáno v:	Ultrasonics Ročník 126; s. 106823
Hlavní autoři:	Wang, Wenping, He, Qiong, Zhang, Ziyou, Feng, Ziliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.12.2022
Témata:	Adaptive beamforming Deep neural network Low computation complexity Minimum variance beamforming Ultrafast ultrasound imaging Minimum variance beamforming Deep neural network Low computation complexity Ultrafast ultrasound imaging Adaptive beamforming
ISSN:	0041-624X, 1874-9968, 1874-9968
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Popis
Shrnutí:	•Provides a deep neural network guided by minimum variance method for ultrafast ultrasound adaptive beamforming.•Shows that the proposed method’s beneficial effects evaluated by simulations, phantom experiments, and in-vivo experiments.•Reveals that the proposed method has the potential for ultrafast ultrasound imaging in image quality, computational complexity, and real-time frame rate. Ultrafast ultrasound imaging can achieve high frame rate by emitting planewave (PW). However, the image quality is drastically degraded in comparison with traditional scanline focused imaging. Using adaptive beamforming techniques can improve image quality at cost of real-time performance. In this work, an adaptive beamforming based on minimum variance (ABF-MV) with deep neural network (DNN) is proposed to improve the image performance and to speed up the beamforming process of ultrafast ultrasound imaging. In particular, a DNN, with a combination architecture of fully-connected network (FCN) and convolutional autoencoder (CAE), is trained with channel radio-frequency (RF) data as input while minimum variance (MV) beamformed data as ground truth. Conventional delay-and-sum (DAS) beamformer and MV beamformer are utilized for comparison to evaluate the performance of the proposed method with simulations, phantom experiments, and in-vivo experiments. The results show that the proposed method can achieve superior resolution and contrast performance, compared with DAS. Moreover, it is remarkable that both in theoretical analysis and implementation, our proposed method has comparable image quality, lower computational complexity, and faster frame rate, compared with MV. In conclusion, the proposed method has the potential to be deployed in ultrafast ultrasound imaging systems in terms of imaging performance and processing time.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0041-624X 1874-9968 1874-9968
DOI:	10.1016/j.ultras.2022.106823