  Deep learning has been used extensively for medical image analysis
applications, assuming the training and test data adhere to the same
probability distributions. However, a common challenge arises when dealing with
medical images generated by different systems or even the same system with
varying parameter settings. Such images often contain diverse textures and
noise patterns, violating the assumption. Consequently, models trained on data
from one machine or setting usually struggle to perform effectively on data
from another. To address this issue in ultrasound images, we proposed a
Generative Adversarial Network (GAN) based model in this paper. We formulated
image harmonization and denoising tasks as an image-to-image translation task,
wherein we modified the texture pattern and reduced noise in Carotid ultrasound
images while keeping the image content (the anatomy) unchanged. The performance
was evaluated using feature distribution and pixel-space similarity metrics. In
addition, blood-to-tissue contrast and influence on computed risk markers (Gray
scale median, GSM) were evaluated. The results showed that domain adaptation
was achieved in both tasks (histogram correlation 0.920 and 0.844), as compared
to no adaptation (0.890 and 0.707), and that the anatomy of the images was
retained (structure similarity index measure of the arterial wall 0.71 and
0.80). In addition, the image noise level (contrast) did not change in the
image harmonization task (-34.1 vs 35.2 dB) but was improved in the noise
reduction task (-23.5 vs -46.7 dB). The model outperformed the CycleGAN in both
tasks. Finally, the risk marker GSM increased by 7.6 (p<0.001) in task 1 but
not in task 2. We conclude that domain translation models are powerful tools
for ultrasound image improvement while retaining the underlying anatomy but
that downstream calculations of risk markers may be affected.
