title: Adaptive regularized method based on homotopy for sparse fluorescence tomography.
abstract: Determining an appropriate regularization parameter is often challenging work because it has a narrow range and varies with problems, which is likely to lead to large reconstruction errors. In this contribution, an adaptive regularized method based on homotopy is presented for sparse fluorescence tomography reconstruction. Due to the adaptive regularization strategy, the proposed method is always able to reconstruct sources accurately independent of the estimation of the regularization parameter. Moreover, the proposed method is about two orders of magnitude faster than the two contrasting methods. Numerical and in vivo mouse experiments have been employed to validate the robustness and efficiency of the proposed method.
authors.affiliation: Intelligent Medical Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Zhenwen
authors.initials: Z
authors.lastname: Xue
authors.forename: Xibo
authors.initials: X
authors.lastname: Ma
authors.forename: Qian
authors.initials: Q
authors.lastname: Zhang
authors.forename: Ping
authors.initials: P
authors.lastname: Wu
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041622:Tomography, Optical / Q000379:methods*