Optimizing X-ray CT with Virtual Tomography: Highlights from Rigaku’s Webinar

Novi-Sim enables researchers to create highly realistic synthetic CT and radiograph datasets by combining structural information from a sample with detailed scanner properties such as source spectrum, detector configuration, and geometry. This allows users to virtually test and refine acquisition parameters before stepping into the lab—significantly reducing trial-and-error on real equipment.

Beyond parameter optimisation, Novi-Sim opens new possibilities in training machine learning models, validating algorithms, and designing experiments more efficiently. By generating diverse, physics-based datasets, Novi-Sim bridges the gap between theoretical design and experimental practice, empowering R&D teams to shorten development cycles while improving imaging quality.

The webinar underscored how simulation is becoming a cornerstone of modern tomography workflows. With tools like Novi-Sim, engineers and scientists can push the boundaries of materials characterization while saving valuable time and resources.