  Ultrasonic imaging is being used to obtain information about the acoustic
properties of a medium by emitting waves into it and recording their
interaction using ultrasonic transducer arrays. The Delay-And-Sum (DAS)
algorithm forms images using the main path on which reflected signals travel
back to the transducers. In some applications, different insonification paths
can be considered, for instance by placing the transducers at different
locations or if strong reflectors inside the medium are known a-priori. These
different modes give rise to multiple DAS images reflecting different geometric
information about the scatterers and the challenge is to either fuse them into
one image or to directly extract higher-level information regarding the
materials of the medium, e.g., a segmentation map. Traditional image fusion
techniques typically use ad-hoc combinations of pre-defined image transforms,
pooling operations and thresholding. In this work, we propose a deep neural
network (DNN) architecture that directly maps all available data to a
segmentation map while explicitly incorporating the DAS image formation for the
different insonification paths as network layers. This enables information flow
between data pre-processing and image post-processing DNNs, trained end-to-end.
We compare our proposed method to a traditional image fusion technique using
simulated data experiments, mimicking a non-destructive testing application
with four image modes, i.e., two transducer locations and two internal
reflection boundaries. Using our approach, it is possible to obtain much more
accurate segmentation of defects.
