Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angular-averaged measurement data and internal angular-averaged measurement data. Simulation results demonstrate that in most of the test cases the reconstructions with sparsity regularization are both qualitatively and quantitatively more reliable than those with standard L2 regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existing L1 algorithms.

+More

l2 diffusion equation da light propagation radiative transfer equation sparsity reconstruction approach l1 algorithms diffuse optical tomography inverse biological media forward model molecular imaging technology dot image reconstruction boundary angularaveraged measurement data split bregman algorithm

Li Li,Bo Han,Jinping Tang,Weimin Han,Bo Bi,.Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation. 2015 (),.

Li Li,Bo Han,Jinping Tang,Weimin Han,Bo Bi,"Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation" 2015

Li Li,Bo Han,Jinping Tang,Weimin Han,Bo Bi,"Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation"