title: A fast reconstruction algorithm for fluorescence molecular tomography with sparsity regularization.
abstract: Through the reconstruction of the fluorescent probe distributions, fluorescence molecular tomography (FMT) can three-dimensionally resolve the molecular processes in small animals in vivo. In this paper, we propose an FMT reconstruction algorithm based on the iterated shrinkage method. By incorporating a surrogate function, the original optimization problem can be decoupled, which enables us to use the general sparsity regularization. Due to the sparsity characteristic of the fluorescent sources, the performance of this method can be greatly enhanced, which leads to a fast reconstruction algorithm. Numerical simulations and physical experiments were conducted. Compared to Newton method with Tikhonov regularization, the iterated shrinkage based algorithm can obtain more accurate results, even with very limited measurement data.
authors.affiliation: Medical Image Processing Group, Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Han
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
authors.forename: Shouping
authors.initials: S
authors.lastname: Zhu
authors.forename: Jinchao
authors.initials: J
authors.lastname: Feng
authors.forename: Chenghu
authors.initials: C
authors.lastname: Qin
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D005453:Fluorescence; D007091:Image Processing, Computer-Assisted; D051379:Mice; D019047:Phantoms, Imaging; D014054:Tomography / Q000295:instrumentation*