Fluorescence molecular tomography (FMT) is a promising multimodality-fused medical imaging technique, aiming at
noninvasively and dynamically visualizing the interaction processes at the cellular and molecular level. However, due to the intricate correlation among columns in system matrix, the inverse problem of FMT faces high ill-posedness. In this work, we propose a novel regularized doubly orthogonal matching pursuit (RDOMP) method through synergistically integrating Gram-Schmidt (GS) orthogonalization with regularized orthogonal matching pursuit (ROMP) to decorrelate the elements in support set against remaining elements. Experiments based on the numerical mouse with double tumors and in vivo mouse were conducted to validate the enhancement of RDOMP with comparison to orthogonal matching pursuit (OMP), sparsity adaptive subspace pursuit (SASP), and ROMP contrast methods. The simulated experimental results demonstrated a better performance of RDOMP in terms of location accuracy, fluorescent yield reconstruction, relative sparsity, and morphological similarity, which highlighted the pruning capability of the introduced GS orthogonalization. And in vivo experimental results demonstrated the practical application of RDOMP on FMT reconstruction.