  Ultrasound (US) imaging is better suited for intraoperative settings because
it is real-time and more portable than other imaging techniques, such as
mammography. However, US images are characterized by lower spatial resolution
noise-like artifacts. This research aims to address these limitations by
providing surgeons with mammogram-like image quality in real-time from noisy US
images. Unlike previous approaches for improving US image quality that aim to
reduce artifacts by treating them as (speckle noise), we recognize their value
as informative wave interference pattern (WIP). To achieve this, we utilize the
Stride software to numerically solve the forward model, generating ultrasound
images from mammograms images by solving wave-equations. Additionally, we
leverage the power of domain adaptation to enhance the realism of the simulated
ultrasound images. Then, we utilize generative adversarial networks (GANs) to
tackle the inverse problem of generating mammogram-quality images from
ultrasound images. The resultant images have considerably more discernible
details than the original US images.
