Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography

•Firstly, based on clustering analysis of iterative reconstruction results, we build individual permissible region for each source with subspace optimization. It is worth mentioning that, both the spatial coordinate and the corresponding energy intensity are considered in the clustering analysis. For automatically determine cluster number, a SSBL method is designed to combined with K-Means to obtain multiple subspaces. In this process, the SDO leverages the relationship between energy distribution and mesh node coordinates to achieve reliable target identification and separation.•Secondly, the SDO incorporates subspace contraction optimization, which not only ensures convergence and continuity of the results, but also preserves the morphology of the source distribution.•Thirdly, SDO uses source sparsity and smooth transitions or discrete energy distributions of noise artifacts to enhance the reconstruction results. Finally, SDO does not rely on a specific reconstruction algorithm, providing strong generalization and applicability. Bioluminescence tomography (BLT) is a noninvasive optical imaging technique that provides qualitative and quantitative information on the spatial distribution of tumors in living animals. Researchers have proposed a list of algorithms and strategies for BLT reconstruction to improve its reconstruction quality. However, multi-target BLT reconstruction remains challenging in practical clinical applications due to the mutual interference of optical signals and difficulty in source separation. To solve this problem, this study proposes the subspace decision optimization (SDO) approach based on the traditional iterative permissible region strategy. The SDO approach transforms a single permissible region into multiple subspaces by clustering analysis. These subspaces are shrunk based on subspace shrinking optimization to achieve spatial continuity of the permissible regions. In addition, these subspaces are merged to construct a new permissible region and then the next iteration of reconstruction is carried out to ensure the stability of the results. Finally, all the iterative results are optimized based on the normal distribution model and the distribution properties of the targets to ensure the sparsity of each target and the non-biasing of the overall results. Experimental results show that the SDO approach can automatically identify and separate different targets, ensuring the accuracy and quality of multi-target BLT reconstruction