Research On Meshless-based Methods For Fluorescence Molecular Tomography

Molecular imaging,emerging as an indispensable research tool,enhances the understanding of the physiological and pathological changes in organisms.This non-invasive technology is capable of visualizing the dynamic molecular and cellular processes taking place in vivo over a period of time,which has facilitated preclinical and clinical applications in biomedical in vivo studies,such as tumor diagnosis and treatment,stem cell imaging,and drug efficacy evaluation.Fluorescence molecular tomography(FMT)could exploit the distribution of fluorescent biomarkers that target tumors accurately and effectively,which enables noninvasive visualization of the fluorescence target inside small animals,and it could get the three-dimensional(3D)visualization as well as quantitative analysis of small tumors in small animal studies in vivo at real-time.The research of this dissertation focuses on the major topic to improve the performance of FMT,which relies on the researches into the forward problem,inverse problem and image enhancement of FMT.The main contributions are listed as follows:1.Currently,photon propagation of FMT is conventionally described by diffusion equation(DE),which is mainly solved by the Finite Element Method(FEM),and it can obtain acceptable image quality.However,there are still some inherent inadequacies in FEM,such as time consuming,discretization error and inflexibility in mesh generation,which partly limit its imaging accuracy.To further improve the solving accuracy of photon propagation model(PPM),we propose a novel meshless method(MM)to implement the PPM of FMT,which is based on the introduction of compactly supported radial basis functions(CSRBFs).We introduced a series of independent nodes and continuous CSRBFs to interpolate the PPM,which can avoid complicated mesh generation.To analyze the performance of the proposed MM,we carried out numerical heterogeneous mouse simulation to validate the simulated surface fluorescent measurement,and in vivo experiment to observe the tomographic reconstruction.The experimental results confirmed that our proposed MM could obtain more similar surface fluorescence measurement with the golden standard(Monte-Carlo method),and more accurate reconstruction result was achieved via MM in in vivo application.2.Conventional reconstruction methods of FMT based on FEM have achieved acceptable stability and efficiency.However,some inherent shortcomings in FEM meshes,such as time consumption in mesh generation and a large discretization error limit further biomedical application.For the improvement of the reconstruction,we propose a meshless reconstruction method for FMT(MM-FMT)using CSRBFs.With CSRBFs,the image domain can be accurately expressed by continuous CSRBFs,avoiding the discretization error to a certain degree.After direct collocation with CSRBFs,the conventional optimization techniques including Tikhonov,L1-norm iteration shrinkage(L1-IS)and sparsity adaptive matching pursuit(SAMP)were adopted to solve the meshless reconstruction.To evaluate the performance of the proposed MM-FMT,we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments.The results suggest that MM-FMT can reduce the position error of the reconstruction result to smaller than 0.4mm for the double-source simulation and in vivo experiment,which is a significant improvement for FMT.3.Due to the difficulties of reconstruction,more practical and efficient approaches to accurately obtain the characteristics of fluorescent regions inside biological tissues keep to be introduced continuously in recent years.In this dissertation,we propose a region reconstruction method for FMT,which is defined as an L1-norm regularization piecewise constant level set(L1-PCLS)approach.The proposed approach adopts a priori information including the sparsity of the fluorescent sources and the distinction of fluorescence intensity between the target region and non-target region.When the intensity of different fluorescent sources ranges in same level,our approach can simultaneously solve the detection and characterization problems for the reconstruction of FMT.To evaluate the performance of the region reconstruction method,numerical phantom experiments and in vivo bead-implanted mouse experiments were performed.The results suggested that the proposed region reconstruction method was able to reconstruct the features of the fluorescent regions accurately and effectively,and the proposed method was able to be feasibly adopted in in vivo application.