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 training of deep learning-based techniques' performance due
to the presence of 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, including 161,701 single plane-wave
images (RF data). This dataset serves to mitigate the data scarcity problem in
the development of deep learning-based techniques for phase aberration
correction.Comment: arXiv admin note: text overlap with arXiv:2303.0574