  Beamforming in plane-wave imaging (PWI) is an essential step in creating
images with optimal quality. Adaptive methods estimate the apodization weights
from echo traces acquired by several transducer elements. Herein, we formulate
plane-wave beamforming as a blind source separation problem. The output of each
transducer element is considered as a non-independent observation of the field.
As such, beamforming can be formulated as the estimation of an independent
component out of the observations. We then adapt the independent component
analysis (ICA) algorithm to solve this problem and reconstruct the final image.
The proposed method is evaluated on a set of simulation, real phantom, and in
vivo data available from the PWI challenge in medical ultrasound. The
performance of the proposed beamforming approach is also evaluated in different
imaging settings. The proposed algorithm improves lateral resolution by as much
as $36.5\%$ and contrast by $10\%$ as compared to the classical delay and sum.
Moreover, results are compared with other well-known adaptive methods. Finally,
the robustness of the proposed method to noise is investigated.
