  A common issue in exploiting simulated ultrasound data for training neural
networks is the domain shift problem, where the trained models on synthetic
data are not generalizable to clinical data. Recently, Fourier Domain
Adaptation (FDA) has been proposed in the field of computer vision to tackle
the domain shift problem by replacing the magnitude of the low-frequency
spectrum of a synthetic sample (source) with a real sample (target). This
method is attractive in ultrasound imaging given that two important differences
between synthetic and real ultrasound data are caused by unknown values of
attenuation and speed of sound (SOS) in real tissues. Attenuation leads to slow
variations in the amplitude of the B-mode image, and SOS mismatch creates
aberration and subsequent blurring. As such, both domain shifts cause
differences in the low-frequency components of the envelope data, which are
replaced in the proposed method. We demonstrate that applying the FDA method to
the synthetic data, simulated by Field II, obtains an 3.5\% higher Dice
similarity coefficient for a breast lesion segmentation task.
