  In recent years, diverging-wave (DW) ultrasound imaging has become a very
promising methodology for cardiovascular imaging due to its high temporal
resolution. However, if they are limited in number, DW transmits provide lower
image quality compared with classical focused schemes. A conventional
reconstruction approach consists in summing series of ultrasound signals
coherently, at the expense of the frame rate. To deal with this limitation, we
propose a convolutional neural networks (CNN) architecture for high-quality
reconstruction of DW ultrasound images using a small number of transmissions.
Given the spatially varying properties of DW images along depth, we adopted the
inception model composed of the concatenation of multi-scale convolutional
kernels. Incorporating inception modules aims at capturing different image
features with multi-scale receptive fields. A mapping between low-quality
images and corresponding high-quality compounded reconstruction was learned by
training the network using in vitro and in vivo samples. The performance of the
proposed approach was evaluated in terms of contrast-to-noise ratio and lateral
resolution, and compared with standard compounding method and conventional CNN
methods. The results demonstrate that our method could produce high-quality
images using only three DWs, yielding an image quality equivalent to the one
obtained with standard compounding of 31 DWs and outperforming more
conventional CNN architectures in terms of complexity, inference time and image
quality.
