Accelerating spatially non-uniform update for sparse target recovery in fluorescence molecular tomography by ordered subsets and momentum methods

Fluorescence molecular tomography (FMT) is a significant preclinical imaging modality that has been actively studied in the past two decades. However, it remains a challenging task to obtain fast and accurate reconstruction of fluorescent probe distribution in small animals due to the large computational burden and the ill-posed nature of the inverse problem. We have recently studied a non-uniform multiplicative updating algorithm, and obtained some further speed gain with the ordered subsets (OS) method. However, increasing the number of OS leads to larger approximation errors and the speed gain from larger number of OS is marginal. In this paper, we propose to further enhance the convergence speed by incorporating the first order momentum method that uses previous iterations to achieve a quadratic convergence rate. Using cubic phantom experiment, we have shown that the proposed method indeed leads to a much faster convergence.