Improved quantification of fluorescence in 3-D In a realistic mouse phantom

Advanced imaging systems and theoretical models have been developed to quantify fluorescence, and this theoretical framework involves numerical-based solutions of a set of coupled diffusion equations. One key to advancing this modality is the extension of the imaging into realistic tissue geometries, which can be dynamically updated from data from other high resolution modalities. Here we explore the quantification of fluorescence in a three-dimensional (3-D) mouse phantom tagged with heterogeneous optical properties. A finite element model for the diffusion equation was used to approximate light propagation along with Newton's method for image reconstruction, to recover 3-D images of fluorescent yield. Using measurements generated on a brain tumor in a mouse with 2% noise, our results show that only 11.4% of the expected fluorescent yield could be recovered without any prior knowledge about the spatial structure of the domain. Using a parameter reduction scheme based upon prior spatial information of the location and size of the tumor, 100% of the expected value could be estimated. These preliminary results indicate that image guided fluorescence spectroscopy has the ability to provide accurate fluorescence recovery, whereas diffuse imaging based recovery is limited in the ability to quantify.