Robust Reconstruction of Fluorescence Molecular Tomography With An Optimized Illumination Pattern
Yan Liu, ETH

Meeting • 04 March 2020

Abstract
Fluorescence molecular tomography (FMT) is an emerging powerful tool for biomedical research. There are two factors that influence FMT reconstruction most effectively. The first one is regularization techniques. For this, we replace traditional Tikhonov regularization with sparse regularization to improve reconstruction quality. The second one is the illumination pattern. We take advantage of the discrete formulation of the forward problem to define an illumination pattern and the admissible set of patterns. Then we add restrictions in the admissible set as different types of regularizers to a discrepancy functional with the illumination pattern as unknown and the reconstruction result as prior information, generating another inverse problem. The computed optimal illumination pattern is then used for the next round of reconstruction. To sum, we combine reconstruction with illumination pattern optimization to form a two-step approach which improves the quality of the reconstructed image in phantom simulations