  One of the primary sources of suboptimal image quality in ultrasound imaging
is phase aberration. It is caused by spatial changes in sound speed over a
heterogeneous medium, which disturbs the transmitted waves and prevents
coherent summation of echo signals. Obtaining non-aberrated ground truths in
real-world scenarios can be extremely challenging, if not impossible. This
challenge hinders the performance of deep learning-based techniques due to the
domain shift between simulated and experimental data. Here, for the first time,
we propose a deep learning-based method that does not require ground truth to
correct the phase aberration problem and, as such, can be directly trained on
real data. We train a network wherein both the input and target output are
randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a
conventional loss function such as mean square error is inadequate for training
such a network to achieve optimal performance. Instead, we propose an adaptive
mixed loss function that employs both B-mode and RF data, resulting in more
efficient convergence and enhanced performance. Finally, we publicly release
our dataset, comprising over 180,000 aberrated single plane-wave images (RF
data), wherein phase aberrations are modeled as near-field phase screens.
Although not utilized in the proposed method, each aberrated image is paired
with its corresponding aberration profile and the non-aberrated version, aiming
to mitigate the data scarcity problem in developing deep learning-based
techniques for phase aberration correction.
