Half Thresholding Pursuit Algorithm for Fluorescence Molecular Tomography

Objective: Fluorescence Molecular Tomography (FMT) is a promising optical tool for small animal imaging. The l(1/2)-norm regularization has attracted attention in the field of FMT due to its ability in enhancing sparsity of solution and coping with the high ill-posedness of the inverse problem. However, efficient algorithm for solving the nonconvex regularized model deserve to explore. Method: A Half Thresholding Pursuit Algorithm (HTPA) combined with parameter optimization is proposed in this paper to efficiently solve the nonconvex optimization model. Specifically, the half thresholding iteration method is utilized to solve l(1/2)-norm model, pursuit strategy is used to accelerate the process of iteration, and the parameter optimization scheme is designed to obtain robust parameter. Results: Analysis and assessment on simulated and experimental data demonstrate that the proposed HTPA performs better in location accuracy and reconstructed fluorescent yield in less time cost, compared with the state-of-the-art reconstruction algorithms. Conclusion: The proposed HTPA combined with the parameter optimization scheme is an efficient and robust reconstruction approach to FMT.