A priori fluorophore distribution estimation in fluorescence imaging through application of a segmentation process and a data fitting technique

During the last few years a quite large number of fluorescence imaging applications have been reported in the literature, as one of the most challenging problems in medical imaging is to “see” a tumor embedded in tissue, which is a turbid medium. This problem has not been fully encountered yet, due to the non-linear nature of the inverse problem. In this paper, a novel method for processing the forward solver outcomes is presented. Through this technique the comparison between the simulated and the acquired data can be performed only at the region-of-interest, minimizing time-consuming pixel-to-pixel comparison. With this modus operandi a-priori information about the initial fluorophore distribution becomes available, leading to a more feasible inverse problem solution.