The disclosure relates to a multi-spectral bioluminescence tomography (BLT) method and system. This method and system introduces a Log-sum penalty function model into the BLT research. It uses multi-spectral information to increase the effective measurement and reduce the ill-posed characteristic of BLT reconstruction, and establishes a non-convex BLT reconstruction model based on Log-sum regularization. Further, in order to simplify the solution process of the non-convex model and improve the reconstruction performance, this method uses a non-monotonic accelerate proximal gradient (nmAPG) algorithm to solve the established BLT reconstruction model based on Log-sum regularization. The method obtains the reconstruction result quickly and accurately. Acquire image data of an organism Estah1ih a finite element rid hsd on ariiired comnited tomngahy (CT) 1 image data of the organism, use a finite element method to determine a diffusion 101 approximation (DA) model of a radiation transmission equation by combining specific optical parameters of each tissue of the organism, and establish a linear mapping relationship between a light source in the organism and light intensity distribution of different wavelengths on a surface of the organism Map an acquired two-dimensional (2D) multi-spectral image to a surface of 102 the finite element grid to obtain a multi-spectral surface measurement vector Construct a bioluminescence tomography (BLT) reconstruction optimization 103 model based on a Log-sum penalty function according to the multi-spectral surface measurement vector Use a non-monotonic accelerate proximal gradient (nmAPG) algorithm to 104 determine energy distribution of the light source in the organism according to the BLT reconstruction optimization model based on the Log-sum penalty function Generate a reconstruction result of the light source in the organism according 105 to the energy distribution and finite element grid information
Multi-Spectral Bioluminescence Tomography Method and System