title: A fast bioluminescent source localization method based on generalized graph cuts with mouse model validations.
abstract: Bioluminescence imaging (BLI) makes it possible to elucidate molecular and cellular signatures to better understand the effects of human disease in small animal models in vivo. The unambiguous three-dimensional bioluminescent source information obtained by bioluminescence tomography (BLT) could further facilitate its applications in biomedicine. However, to the best of our knowledge, the existing gradient-type reconstruction methods in BLT are inefficient, and often require a relatively small volume of interest (VOI) for feasible results. In this paper, a fast generalized graph cuts based reconstruction method for BLT is presented, which is to localize the bioluminescent source in heterogeneous mouse tissues via max-flow/min-cut algorithm. Since the original graph cuts theory can only handle graph-representable problem, the quadratic pseudo-boolean optimization is incorporated to make the graph representable and tractable, which is called generalized graph cuts (GGC). The internal light source can be reconstructed from the whole domain, so a priori knowledge of VOI can be avoided in this method. In the simulation validations, the proposed method was validated in a heterogeneous mouse atlas, and the source can be localized reliably and efficiently by GGC; and compared with gradient-type method, the proposed method is about 25-50 times faster. Moreover, the experiments for sensitivity to the measurement errors of tissue optical properties demonstrate that, the reconstruction quality is not much affected by mismatch of parameters. In what follows, in vivo mouse BLT reconstructions further demonstrated the potential and effectiveness of the generalized graph cut based reconstruction method.
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: Yujie
authors.initials: Y
authors.lastname: Lu
authors.forename: Chenghu
authors.initials: C
authors.lastname: Qin
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.forename: Shouping
authors.initials: S
authors.lastname: Zhu
authors.forename: Xing
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000818:Animals; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008163:Luminescent Measurements / Q000379:methods*; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods; D041622:Tomography, Optical / Q000379:methods*; D051598:Whole Body Imaging / Q000379:methods*