  In the recent past, there have been many efforts to accelerate adaptive
beamforming for ultrasound (US) imaging using neural networks (NNs). However,
most of these efforts are based on static models, i.e., they are trained to
learn a single adaptive beamforming approach (e.g., minimum variance
distortionless response (MVDR)) assuming that they result in the best image
quality. Moreover, the training of such NNs is initiated only after acquiring a
large set of data that consumes several gigabytes (GBs) of storage space. In
this study, an active learning framework for beamforming is described for the
first time in the context of NNs. The best quality image chosen by the user
serves as the ground truth for the proposed technique, which trains the NN
concurrently with data acqusition. On average, the active learning approach
takes 0.5 seconds to complete a single iteration of training.
