  Automatic learning algorithms for improving the image quality of diagnostic
B-mode ultrasound (US) images have been gaining popularity in the recent past.
In this work, a novel convolutional neural network (CNN) is trained using time
of flight corrected in-vivo receiver data of plane wave transmit to produce
corresponding high-quality minimum variance distortion less response (MVDR)
beamformed image. A comprehensive performance comparison in terms of
qualitative and quantitative measures for fully connected neural network
(FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the
dataset from Plane wave Imaging Challenge in Ultrasound (PICMUS) is also
reported in this work. The CNN architecture could leverage the spatial
information and will be more region adaptive during the beamforming process.
This is evident from the improvement seen over the baseline FCNN approach and
conventional MVDR beamformer, both in resolution and contrast with an
improvement of 6 dB in CNR using only zero-angle transmission over the
baseline. With the observed reduction in the requirement of number of angles to
produce similar image metrics would prove advantageous in providing a
possibility for higher frame rates.
