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. ### Competing Interest Statement This work would not have been possible without the financial support of the Qualcomm Innovation Fellowship Award, India. We are indebted to the developer of Stride, Mr. Carlos Cueto from Imperial College London, for his feedback and support. ### Funding Statement This study was funded by the Qualcomm Innovation Fellowship Award, India. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used (or will use) ONLY openly available human data that were originally located at: Cancer imaging archive. https://www.cancerimagingarchive.net/. Accessed: 2021-10-24. Computing the mass attenuation coefficients of the elemental composition of the tissues. https://www.nist.gov/pml/xcom-photon-cross- sections-database. Accessed: 2021-10-24. Element composition of the body tissues. https://itis.swiss/virtual-population/tissue- properties/database/elements/. Accessed: 2021- 10-24. ITIS Foundation. https://itis.swiss/virtual- population/tissue-properties/database/elements/. Accessed: 2021-10-24 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at: Cancer imaging archive. https://www.cancerimagingarchive.net/. Accessed: 2021-10-24. Computing the mass attenuation coefficients of the elemental composition of the tissues. https://www.nist.gov/pml/xcom-photon-cross- sections-database. Accessed: 2021-10-24. Element composition of the body tissues. https://itis.swiss/virtual-population/tissue- properties/database/elements/. Accessed: 2021- 10-24. ITIS Foundation. https://itis.swiss/virtual- population/tissue-properties/database/elements/. Accessed: 2021-10-24