Yifang Hu1,2, Jie Liu3,4, Chengcai Leng2,5, Yu An1, Shuang Zhang6, Kun Wang7. 1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China. 2. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Zhongguancun East Road #95, Haidian Dist., Beijing, 100190, People's Republic of China. 3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China. jieliu@bjtu.edu.cn. 4. Department of Biomedical Engineering, Beijing Jiaotong University, School of Computer and Information, Shangyuancun 3#, Beijing, 100044, People's Republic of China. jieliu@bjtu.edu.cn. 5. School of Mathematics and Information Sciences, Nanchang Hangkong University, Nanchang, 330063, China. 6. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110819, China. 7. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Zhongguancun East Road #95, Haidian Dist., Beijing, 100190, People's Republic of China. kun.wang@ia.ac.cn.
Abstract
PURPOSE: Bioluminescence tomography (BLT) is a promising in vivo optical imaging technique in preclinical research at cellular and molecular levels. The problem of BLT reconstruction is quite ill-posed and ill-conditioned. In order to achieve high accuracy and efficiency for its inverse reconstruction, we proposed a novel approach based on L p regularization with the Split Bregman method. PROCEDURES: The diffusion equation was used as the forward model. Then, we defined the objective function of L p regularization and developed a Split Bregman iteration algorithm to optimize this function. After that, we conducted numerical simulations and in vivo experiments to evaluate the accuracy and efficiency of the proposed method. RESULTS: The results of the simulations indicated that compared with the conjugate gradient and iterative shrinkage methods, the proposed method is more accurate and faster for multisource reconstructions. Furthermore, in vivo imaging suggested that it could clearly distinguish the viable and apoptotic tumor regions. CONCLUSIONS: The Split Bregman iteration method is able to minimize the L p regularization problem and achieve fast and accurate reconstruction in BLT.
PURPOSE: Bioluminescence tomography (BLT) is a promising in vivo optical imaging technique in preclinical research at cellular and molecular levels. The problem of BLT reconstruction is quite ill-posed and ill-conditioned. In order to achieve high accuracy and efficiency for its inverse reconstruction, we proposed a novel approach based on L p regularization with the Split Bregman method. PROCEDURES: The diffusion equation was used as the forward model. Then, we defined the objective function of L p regularization and developed a Split Bregman iteration algorithm to optimize this function. After that, we conducted numerical simulations and in vivo experiments to evaluate the accuracy and efficiency of the proposed method. RESULTS: The results of the simulations indicated that compared with the conjugate gradient and iterative shrinkage methods, the proposed method is more accurate and faster for multisource reconstructions. Furthermore, in vivo imaging suggested that it could clearly distinguish the viable and apoptotic tumor regions. CONCLUSIONS: The Split Bregman iteration method is able to minimize the L p regularization problem and achieve fast and accurate reconstruction in BLT.
Entities:
Keywords:
Bioluminescence tomography (BLT); Image reconstruction; L p regularization; Split Bregman method