2D shape-based fluorescence molecular tomography through hybrid genetic algorithm based optimization

Fluorescence molecular tomography (FMT) aims at tomographically resolving the fluorescent targets deeply inside small animal based on transmission boundary measurements. The image reconstruction of FMT is known to be highly ill-posed, due to the highly scattering nature of biological tissue. Hence, prior information is usually required for successful reconstruction. In this article, a novel reconstruction method incorporating shape priors is proposed for 2D FMT. The fluorescent targets are assumed of round shape, which is practically appropriate for approximating various shapes inside diffusive medium. Compared to the traditional pixel-based reconstruction, the number of unknowns is greatly reduced to a few control parameters of round shapes. A hybrid genetic algorithm is proposed to recover the shape parameters. The numerical experiments show that the proposed method significantly improves the imaging accuracy, offering clearer targets boundaries and better resolution. Comparison results also demonstrate that the hybridization of genetic algorithm and Newton-type search is pivotal and important for robustly finding the globally optimal shape parameters.