An extensive generative framework and multiphasic verification strategy for synthesising CT images of diverse anatomies: towards the utilisation for <i>in silico</i> clinical trials

The conventional approach of testing the safety and efficacy of medical devices/drugs is done through clinical trials. These trials are time-consuming, expensive and pose ethical issues. In Silico Clinical Trials (ISCTs) leverage computer simulations to assess medical devices or drugs by replicating physiological processes. Acquiring large datasets for ISCTs is challenging due to legal and ethical constraints, but synthetic data generation offers a solution. In this study, we present a widely applicable generative framework which has the capability to synthesise CT images of visceral organs and hard tissues, thereby demonstrating its extensiveness across various anatomical structures. In addition to data synthesis, we show that synthesised images are comparable to real images in terms of quality metrics and expert evaluations (data-level verification). Hence, this research shows the feasibility to use generative frameworks' for clinically realistic data synthesis. Furthermore, we introduce a multiphasic verification strategy with an additional verification step (model-level verification) for prospective utilisation for ISCTs.