An in-silico method to predict and quantify the effect of gold nanoparticles in X-ray imaging.

PURPOSE:Over the last few years studies are conducted, highlighting the feasibility of Gold Nanoparticles (GNPs) to be used in clinical CT imaging and as an efficient contrast agent for cancer research. After ensuring that GNPs formulations are appropriate for in vivo or clinical use, the next step is to determine the parameters for an X-ray system's optimal contrast for applications and to extract quantitative information. There is currently a gap and need to exploit new X-ray imaging protocols and processing algorithms, through specific models avoiding trial-and-error procedures and provide an imaging prognosis tool. Such a model can be used to confirm the accumulation of GNPs in target organs before radiotherapy treatments with a system easily available in hospitals, as low energy X-rays. METHODS:In this study a complete, easy-to-use, simulation platform is designed and built, where simple parameters, as the X-ray's specifications and experimentally defined biodistributions of specific GNPs are imported. The induced contrast and images can be exported, and accurate quantification can be performed. This platform is based on the GATE Monte Carlo simulation toolkit, based on the GEANT4 toolkit and the MOBY phantom, a realistic 4D digital mouse. RESULTS:We have validated this simulation platform to predict the contrast induction and minimum detectable concentration of GNPs on any given X-ray system. The study was applied to preclinical studies but is also expandable to clinical studies. CONCLUSIONS:According to our knowledge, no other such validated simulation model currently exists, and this model could help radiology imaging with GNPs to be truly deployed.