This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.

The purpose of the work described in this paper was the development of a framework for the creation of a realistic positron emission tomography (PET) simulated database incorporating patient-specific variability. The ground truth used was therefore based on clinical PET/computed tomography (CT) data of oncology patients. In the first step, the NURBS-based cardiac-torso phantom was adapted to the patient's CT acquisitions to reproduce their specific anatomy while the corresponding PET acquisitions were used to derive the activity distribution of each organ of interest. Secondly, realistic tumor shapes with homogeneous or heterogeneous activity distributions were modeled based on segmentation of the PET tumor volume and incorporated in the patient-specific models obtained at the first step. Lastly, patient-specific respiratory motion was also modeled. The derived patient-specific models were subsequently combined with the PET SORTEO Monte Carlo simulation tool for the simulation of the whole-body PET acquisition process. The accuracy of the simulated datasets was assessed in comparison to the original clinical patient images. In addition, a couple of applications for such simulated images were also demonstrated. Future work will focus on the creation of a comprehensive database of simulated raw data and reconstructed whole-body images, facilitating the rigorous evaluation of image-processing algorithms in PET for oncology applications.