Key Techniques For Multi-target Dynamic Fluorescence Molecular Imaging

Dynamic fluorescence molecular imaging(D-FMI)is an imaging technique that continuously monitors living objects and uses the dynamic differences of the distribution of fluorescent molecular probes in different organs over time.As an important branch of molecular imaging,D-FMI has unique advantages in pharmacokinetics,efficacy evaluation,physiological and pathological process monitoring.Aiming at the problems of existing in multi-target D-FMI imaging system,the accuracy of multi-target D-FMI has been improved by improving the registration and reconstruction algorithms.D-FMI has an important application in drug research.In this study,the pharmacokinetic model was established by using the reconstructed multi-target fluorescent sources,and the parameters of the model were estimated by using the obtained dynamic fluorescence reconstruction results.The main work of this paper is as follows:(1)The theoretical basis of D-FMI is studied,including the construction of forward photon model,dual-modality image mapping and basic methods of inverse reconstruction.(2)The dual-modality images mapping algorithm is studied.Aiming at the problem of energy mapping between two-dimensional fluorescence images and three-dimensional computed tomography(CT)structure images,an optical-CT dual-modality images mapping algorithm based on digitally reconstructed radiograph image registration is proposed,which maps multi-angle two-dimensional optical data collected by charge coupled device camera to three-dimensional body surface of CT data,and reconstructs three-dimensional energy distribution of the object surface.The effectiveness of the proposed method was verified by experiments in phantom and tumor mouse.(3)In order to solve the problem of insufficient sparsity and low localization accuracy in the reconstruction based on norm optimization,a multi-objective dynamic fluorescence molecular reconstruction method based on block sparse Bayesian learning is proposed.This method can effectively reduce the ill-condition in the reconstruction by utilizing the characteristics of multi-observation dynamic fluorescence signals sharing the same sparse structure.The validity of the algorithm is verified by the simulation experiments of two light sources and three light sources.(4)A dynamic fluorescence molecular imaging system was built,and the multi-target dynamic fluorescence molecular imaging algorithm was validated by pork tissue experiment.A pharmacokinetic model was established for pork tissue experiment,and the pharmacokinetic parameters were estimated based on the results of dynamic fluorescence molecular reconstruction using block sparse Bayesian learning based on multiple measurement vector model.