title: Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization.
abstract: Optical molecular imaging is a promising technique and has been widely used in physiology, and pathology at cellular and molecular levels, which includes different modalities such as bioluminescence tomography, fluorescence molecular tomography and Cerenkov luminescence tomography. The inverse problem is ill-posed for the above modalities, which cause a nonunique solution. In this paper, we propose an effective reconstruction method based on the linearized Bregman iterative algorithm with sparse regularization (LBSR) for reconstruction. Considering the sparsity characteristics of the reconstructed sources, the sparsity can be regarded as a kind of a priori information and sparse regularization is incorporated, which can accurately locate the position of the source. The linearized Bregman iteration method is exploited to minimize the sparse regularization problem so as to further achieve fast and accurate reconstruction results. Experimental results in a numerical simulation and in vivo mouse demonstrate the effectiveness and potential of the proposed method.
authors.affiliation: Key Laboratory of Nondestructive Testing of Ministry of Education, School of Mathematics and Information Sciences, Nanchang Hangkong University, Nanchang 330063, China ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Chengcai
authors.initials: C
authors.lastname: Leng
authors.affiliation: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Yu
authors.affiliation: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China.
authors.forename: Shuang
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: An
authors.affiliation: School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Yifang
authors.initials: Y
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D016014:Linear Models; D051379:Mice; D008819:Mice, Nude; D015233:Models, Statistical; D041622:Tomography, Optical / Q000706:statistics & numerical data*; D055114:X-Ray Microtomography / Q000706:statistics & numerical data