Half Thresholding Pursuit Algorithm for Fluorescence Molecular Tomography

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 5; pp. 1468 - 1476
• Main Authors: He, Xuelei, Yu, Jingjing, Wang, Xiaodong, Yi, Huangjian, Chen, Yanrong, Song, Xiaolei, He, Xiaowei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Biological system modeling; Biomedical optical imaging; Computer Simulation; Fluorescence; fluorescence molecular tomography (FMT); Fluorescent Dyes; Heart - diagnostic imaging; Image Interpretation, Computer-Assisted - methods; Image reconstruction; Inverse problem; Inverse problems; Iterative methods; Lung - diagnostic imaging; Mathematical model; Mathematical models; Mice; Mice, Inbred BALB C; Optical Imaging - methods; Optimization; Parameter robustness; Reconstruction; Regularization; Tomography; Tomography - methods
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2018.2874699

Objective: Fluorescence Molecular Tomography (FMT) is a promising optical tool for small animal imaging. The 11/2-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 11/2-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.