CNN-BASED ULTRASOUND IMAGE RECONSTRUCTION FOR ULTRAFAST DISPLACEMENT TRACKING

Abstract

Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultrahigh
frame rates. Recently, a wide variety of studies based on deep learning have sought to improve ultrafast
ultrasound imaging. Most of these approaches have been performed on radio frequency (RF) signals. However,
in-phase/quadrature (I/Q) digital beamformers are now widely used as low-cost strategies. In this work, we used
complex convolutional neural networks for reconstruction of ultrasound images from I/Q signals. We recently
described a convolutional neural network architecture called ID-Net, which exploited an inception layer
designed for reconstruction of RF diverging-wave ultrasound images. In the present study, we derive the
complex equivalent of this network; i.e., the Complex-valued Inception for Diverging-wave Network (CID-Net)
that operates on I/Q data. We provide experimental evidence that CID-Net provides the same image quality as
that obtained from RF-trained convolutional neural networks; i.e., using only three I/Q images, the CID-Net
produces high-quality images that can compete with those obtained by coherently compounding 31 RF images.
Moreover, we show that CIDNet outperforms the straightforward architecture that consists of processing the real
and imaginary parts of the I/Q signal separately, which thereby indicates the importance of consistently
processing the I/Q signals using a network that exploits the complex nature of such signals