Model-based Optical Tomography Reconstruction from Transport Equation
传输方程下基于模型的光学层析图像重建

The model-based reconstruction algorithm is adopted in order to conquer the ill-posedness in optical tomography.An edge-preserving,global smoothing generalized Gaussian Markov random field(GGMRF) is imported into optical tomography reconstruction based on the radiative transfer equation in this paper.The iteration optimization method is used to solve the objective function with the GGMRF model by maximizing the a posterior probability,but the gradient computation of the objective function with respect to optical parameters is difficult.Therefore,a novel gradient computation strategy based on gradient tree is proposed.Experimental results show that this kind of reconstruction technology can improve the image reconstruction quality and decrease the ill-posedness compared to the reconstruction algorithm without priori model.