A unified deep network for beamforming and speckle reduction in plane wave imaging: A simulation study

Abstract

Plane Wave Imaging is a fast imaging method used in ultrasound, which allows a high frame rate, but with compromised image quality when a single wave is used. In this work a learning-based approach was used to obtain improved image quality. The entire process of beamforming and speckle reduction was embedded in a single deep convolutional network, and trained with two types of simulated data. The network architecture was designed based on traditional physical considerations of the ultrasonic image formation pipe. As such, it includes beamforming with spatial matched filters, envelope detection, and a speckle reduction stage done in log-signal representation, with all stages containing trainable parameters. The approach was tested on the publicly available PICMUS datasets, achieving axial and lateral full-width-half-maximum (FWHM) resolution values of 0.22 mm and 0.35 mm respectively, and a Contrast to Noise Ratio (CNR) metric of 16.75 on the experimental datasets.