  Ultrasound imaging (US) often suffers from distinct image artifacts from
various sources. Classic approaches for solving these problems are usually
model-based iterative approaches that have been developed specifically for each
type of artifact, which are often computationally intensive. Recently, deep
learning approaches have been proposed as computationally efficient and high
performance alternatives. Unfortunately, in the current deep learning
approaches, a dedicated neural network should be trained with matched training
data for each specific artifact type. This poses a fundamental limitation in
the practical use of deep learning for US, since large number of models should
be stored to deal with various US image artifacts. Inspired by the recent
success of multi-domain image transfer, here we propose a novel, unsupervised,
deep learning approach in which a single neural network can be used to deal
with different types of US artifacts simply by changing a mask vector that
switches between different target domains. Our algorithm is rigorously derived
using an optimal transport (OT) theory for cascaded probability measures.
Experimental results using phantom and in vivo data demonstrate that the
proposed method can generate high quality image by removing distinct artifacts,
which are comparable to those obtained by separately trained multiple neural
networks.
