Sparsity-Promoting Fluorescence Molecular Tomography with Iteratively Reweighted Regularization

Fluorescence molecular tomography has become a promising technique for in vivo small animal imaging, and has many potential applications. Due to the ill-posed and the ill-conditioned nature of the problem, Tikhonov regularization is generally adopted to stabilize the solution. However, the result is usually over-smoothed. In this study, the sparsity of the fluorescent source is used as a priori information. We replace Tikhonov method with an iteratively reweighted scheme. By dynamically updating the weight matrix, L0- or L1-norm regularization can be approximated which can promote the sparsity of the solution. Simulation study shows that this method can preserve the sparsity of the fluorescent source within heterogeneous medium, even with very limited measurement data.