Quantitative predictions in small-animal X-ray fluorescence tomography.

X-ray fluorescence (XRF) tomography from nanoparticles (NPs) shows promise for high-spatial-resolution molecular imaging in small-animals. Quantitative reconstruction algorithms aim to reconstruct the true distribution of NPs inside the small-animal, but so far there has been no feasible way to predict signal levels or evaluate the accuracy of reconstructions in realistic scenarios. Here we present a GPU-based computational model for small-animal XRF tomography. The unique combination of a highly accelerated Monte Carlo tool combined with an accurate small-animal phantom allows unprecedented realistic full-body simulations. We use this model to simulate our experimental system to evaluate the quantitative performance and accuracy of our reconstruction algorithms on large-scale organs as well as mm-sized tumors. Furthermore, we predict the detection limits for sub-mm tumors at realistic NP concentrations. The computational model will be a valuable tool for optimizing next-generation experimental arrangements and reconstruction algorithms.