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

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 beam -forming 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 ex-periments. 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.

Automated summary

A newly proposed adaptive beamforming method based on deep neural network and minimum variance is able to enhance image quality and accelerate beamforming process in ultrafast ultrasound imaging. Compared with traditional approaches, this method shows superior resolution and contrast performance, along with lower computational complexity and faster frame rate. It has the potential to be deployed in ultrafast ultrasound imaging systems.