An alternative reconstruction framework with optimal permission source region for bioluminescence tomography

A predefined permission source region (PSR) is commonly used in bioluminescence tomography (BLT) as a priori information to reduce the ill-posedness of source reconstruction. However, the PSR is usually hard to determine and may miss the actual source region. Simultaneously, a linear or nonlinear based reconstruction method commonly have a relatively poor performance in the terms of low photon rate and strong noise. Here, we propose an alternative statistical-based-reconstruction framework with a cluster-algorithm-optimized PSR for BLT. In the alternative reconstruction framework, a global reconstruction is first conducted overall the solving domain to generate a rough estimation of source distribution by using a global based maximum likelihood expectation maximum (MLEM) method. Subsequently, a k-means cluster method is used to learn and find likely source support regions, which generates the optimal PSR. Finally, a local MLEM based reconstruction is performed to refine the source distribution on the optimal PSR. We first verify the performance of the proposed method with a series of numerical simulations, and then explore the potential by imaging in situ liver cancer in living mouse.