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Ultrasound (US) imaging is better suited for intraoperative settings becauseit is real-time and more portable than other imaging techniques, such asmammography. However, US images are characterized by lower spatial resolutionnoise-like artifacts. This research aims to address these limitations byproviding surgeons with mammogram-like image quality in real-time from noisy USimages. Unlike previous approaches for improving US image quality that aim toreduce artifacts by treating them as (speckle noise), we recognize their valueas informative wave interference pattern (WIP). To achieve this, we utilize theStride software to numerically solve the forward model, generating ultrasoundimages from mammograms images by solving wave-equations. Additionally, weleverage the power of domain adaptation to enhance the realism of the simulatedultrasound images. Then, we utilize generative adversarial networks (GANs) totackle the inverse problem of generating mammogram-quality images fromultrasound images. The resultant images have considerably more discernibledetails than the original US images.

Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosis