LAUSR

In this study, we developed a detector's eye view (DEV)-based ordered subsets expectation maximization (OSEM) algorithm for more accurate reconstruction of benchtop x-ray fluorescence computed tomography (XFCT) images. The proposed approach was tested using two sets of benchtop XFCT imaging data derived from a newly performed gold nanoparticle (GNP)-containing phantom imaging study and a previously published postmortem benchtop XFCT imaging study of a tumor-bearing mouse injected with GNPs. DEV-based OSEM resulted in higher spatial resolution (up to ~20% decrease in the full width at half maximum values of the regions of interest), compared with filtered back-projection (FBP) and traditional OSEM. It also resulted in up to an order of magnitude smaller background noise in the reconstructed images than FBP, while producing consistently less background noise than traditional OSEM.