Towards improving breast cancer detection through multi-modal image generation

Ultrasound (US) imaging is real-time, less expensive, and more portable, compared to mammography, which makes it better suited for screening in resource-constrained settings and intra-operative imaging. However, US has lower spatial resolution and more artifacts compared to mammograms. This research aims to address these limitations by providing surgeons with mammogram-like image quality in real-time from US images. Previous approaches to US enhancement have discarded the artifacts created by interaction pattern between ultrasound and tissue by treating them as noise. By contrast, we recognize the value of the artifacts as wave interference patterns (WIP) that capture important tissue characteristics. In particular, we utilize the Stride software to numerically solve the forward model by generating US images from mammograms by solving wave-equations and add the high-frequency components separately to produce realistic US images. This forward generation itself is of clinical value because sometimes US acts as a complementary imaging modality to disambiguate cases that are difficult to diagnose using mammograms alone. Then, we train a generative adversarial network (GAN) for the obtaining mammogram-quality images from US. The resultant images have considerably more discernible details than the original US images. With further improvements, both forward and backward image generation can help simulate complementary modality on-the-fly to aid better breast cancer diagnosis in a cost-effective and real-time manner.