Channel Count Reduction for Plane Wave Ultrasound Through Convolutional Neural Network Interpolation

Plane wave ultrasound imaging has helped to achieve high frame rate ultrasound, however the data required to achieve frames rates over 1000 fps remains challenging to handle, as the transfer of large amounts of data represents a bottleneck for image reconstruction. This paper presents a novel method of using a fully convolutional encoder-decoder deep neural network to interpolate pre-beamformed raw RF data from ultrasound transducer elements. The network is trained on in vivo human carotid data, then tested on both carotid data and a standard ultrasound phantom. The neural network outputs are compared to linear interpolation and the proposed method captures more meaningful patterns in the signal; the output channels are then combined with the non-interpolated channels and beamformed to form an image, showing not only significant improvement in mean-squared error compared to the alternatives, but also 10–15 dB reduction in grating lobe artifacts. The proposed method has implications for current ultrasound research directions, with applications to real-time high frame rate ultrasound and 3D ultrasound imaging.