Reconstruction of synthetic ultrasound images to address data deficiency and domain adaptation

High-quality datasets are essential for effective deep learning training. However, collecting consistent ultrasound image data is challenging due to privacy concerns, patients' physical characteristics, and equipment variability. To address this, we propose a CycleGAN-based method to transform real ultrasound data to resemble data generated by the Field II simulation program. This method creates synthetic data that preserves the image structure of real data while incorporating the detailed characteristics of the simulation, facilitating the model's application to real datasets. By bridging the domain gap, the synthetic data enhances feature learning from both real and simulated datasets. Consequently, experiments using synthetic data show higher performance compared to those using only real data or data augmented with simulation alone.