Reconstruction Algorithm Of Bioluminescent Tomography Based On Adaptive Hp-FEM

Molecular imaging has rapidly developed over the past few years because of its ability to observe the molecular and cellular information in vivo. Compared with the traditional imaging techniques like positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound, X-ray computed tomography (CT), optical imaging modality, especially fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT) methods, has high molecular specificity, nonionizing radiation and high cost-effectiveness.Light transport can be described by the steady-state diffusion equation, which is the theoretical basis of BLT. BLT mainly concerns about two problems, the forward problem and the inverse problem. The forward problem is to gain the radiative flux on the surface of small animals, according to the information of bioluminescent sources inside tissues and optical parameters of tissues. And the inverse problem aims to find the information of bioluminescent sources inside tissues based on the given radiative flux on the surface of small animals and optical parameters of tissues. The algorithm of BLT inverse problem and source reconstruction in mice model are studied in this paper.In this paper, a new algorithm is presented for BLT source reconstruction based on adaptive hp-finite element method (hp-FEM). Using adaptive mesh refinement strategy and intelligent permissible source region, the robustness, stability and efficiency of reconstruction algorithm are improved, and the ill-posed of BLT is debased.Numerical simulations and physical experiment were both conducted to verify the performance of the proposed algorithm, where the optical data on phantom surface were obtained via Monte Carlo simulation and CCD camera detection, respectively. The results show that more accurate information about the location and density of reconstructed sources are obtained. At last, an experiment in mouse model was implemented, and the reconstructed result represents the merits and potential of our algorithm for BLT source reconstruction in practical application.