title: An efficient global inexact Newton method regularized by a dynamic sparse term for in vivo tomographic bioluminescence imaging.
abstract: Tomographic bioluminescence imaging suffers from poor imaging robustness, since it is severely affected by selection of regularization parameter (RP) and initialization for source distribution. In this paper, a global-inexact-Newton based reconstruction methodology, which is regularized by a dynamic sparse term, is demonstrated for in vivo tomographic imaging. The novel method can enhance higher imaging reliability and efficiency. Mouse experimental reconstructions were performed to validate the proposed method. These results demonstrate that the source distribution can be accurately located over a wide range of values of RP and iteration initializations. Moreover, the reconstruction efficiency was also studied.
authors.affiliation: Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Liu
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.forename: Chenghu
authors.initials: C
authors.lastname: Qin
authors.forename: Shouping
authors.initials: S
authors.lastname: Zhu
authors.forename: Dong
authors.initials: D
authors.lastname: Han
authors.forename: Xibo
authors.initials: X
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D015233:Models, Statistical; D015203:Reproducibility of Results; D012984:Software; D014018:Tissue Distribution; D041622:Tomography, Optical / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods