Simulating Ultrasound images from CT Scans

Anatomical information in ultrasound (US) imaging has not been exploited fully because its wave interference pattern (WIP) has been viewed as speckle noise. We tested the idea that more information can be retrieved by disentangling the WIP rather than discarding it as noise. We numerically solved the forward model of generating US images from computed tomography (CT) images by solving wave-equations using the Stride library. By doing so, we have paved the way for using deep neural networks to be trained on the data generated by the forward model to simulate the solution of the inverse problem, which is generating the CT-style and CT-quality images from a real US image. We demonstrate qualitative features of the generated images that are rich in anatomical details and realism.