A Two-stage Reconstruction of Fluorescence Molecular Tomography Based on Sparse Regularization

Fluorescence molecular tomography (FMT) is a promising imaging modality that offers the possibilities to monitor cellular and molecular function in vivo. However, accurate and stable reconstruction of fluorescence-labeled targets remains a challenging problem. In this contribution, a two-stage reconstruction algorithm that combines sparse regularization with adaptive finite element method is proposed, and two different inversion algorithms are employed separately on the initial coarse mesh and the second refined one. Numerical experiment results with a digital mouse model demonstrate the stability and computational efficiency of the proposed method for FMT.