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.

翻译：超声成像因其实时性和便携性优于钼靶等成像技术，更适合术中场景。然而，超声图像存在空间分辨率低且伴有类似噪声的伪影。本研究旨在通过为外科医生提供从噪声超声图像中实时生成类钼靶图像质量的解决方案，克服上述局限。不同于以往通过将超声图像伪影视为散斑噪声来提升图像质量的方法，我们认识到这些伪影具有信息性波干涉图样的价值。为此，我们利用Stride软件通过数值求解波动方程，从钼靶图像正向生成超声图像；同时借助域自适应技术增强模拟超声图像的真实性。进而采用生成对抗网络解决从超声图像逆向合成钼靶质量图像的难题。最终所得图像相较于原始超声图像展现出显著更丰富的可辨识细节。