Performance enhancement of fluorescence molecular tomography imaging based on maximally weighted iteration strategy

Fluorescence molecular tomography (FMT) reconstruction represents an ill-posed inverse problem, and the existing solution methods often serve as estimators that tend to introduce reconstruction errors. In this study, we present a maximally weighted iteration (MWI) based FMT imaging framework designed to mitigate the ill-posedness of the inverse problems by strategically controlling the ill-conditioned weighting matrix through iterative weighted decomposition and a weighted correction term. Furthermore, four types of weighting matrices incorporating L1, L2, Frobenius, and L-inf norms are introduced and quantitatively compared with the conventional Tikhonov regularization method through numerical simulations, phantom and in vivo experiments for both single- and double-target scenarios. These results demonstrate that the MWI-based algorithms, particularly the one using the L2-norm, exhibit superior performance in terms of noise robustness, reconstruction accuracy, and spatial resolution. This study provides an effective strategy to enhance the FMT imaging quality.