A hybrid clustering algorithm for multiple‐source resolving in bioluminescence tomography.

Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple‐source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and <italic>K</italic>‐means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple‐source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1‐luc2 mouse model were conducted to assess the performance of the proposed method in multiple‐source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications. [ABSTRACT FROM AUTHOR]