Markov Random Fields Prior Guided PET Reconstruction And Kinetic Parameter Estimate

Positron emission computed tomography (PET) is the effective medical imaging technique that provides functional information of physiological activity by displaying the concentration distribution of radioisotope labeled tracer (chemical compounds or biological molecular) which pre-injected into the human body before imaging process. PET is able to represent heart and brain metabolism and functions on molecule level by imaging techniques, and has shown great performance in oncology, cardiopathy, neurology and new medicine studies. The purpose of PET reconstruction is to get physiological parameters about the region of interest (ROI) through tracer activity distribution. So, it is the direction of development that PET activity image reconstruction and parameters estimate simultaneously through the relationship between physiological parameters and tracer activity distribution. There are two main research directions in PET reconstruction study: 1, accurate reconstruction static activity images of the radioactive tracer spatial distribution; 2, dynamic PET activity image reconstruction and precise physiological parameters estimate.But positron emission tomography is an ill-posed inverse problem because the observed projection data are contaminated by noise due to low count rate and physical effects. Though needing less computation cost, traditional filter back projection (FBP) method often reconstruct noisy images of low quality. Better expressing system models of physical effects and modeling the statistical poisson character of the data, the famous maximum-likelihood expectation-maximization (ML-EM) approach outperforms the FBP method with regard to image quality. However, pure traditional ML-EM approach suffers slow convergence and the reconstructed activity images always start deteriorating to produce "checkerboard effect" as the iteration proceeds.In recently years, Bayesian methods or equivalently MAP (Maximum A Posteriori) methods has been widely used in image reconstruction. Bayesian methods incorporate MRF prior information of objective tracer concentration distribution data into the ML-EM algorithm through regularization or prior terms and have been proved theoretically correct and practically effective compared to other methods. Compared to traditional ML-EM algorithm, Bayesian reconstruction shows a better performance in both improving convergence behavior and producing more appealing images. Bayesian reconstruction can greatly improve reconstruction by incorporating image prior information. However we also find that, heavily relied on the information within a limited neighborhood, conventional Bayesian methods can only contribute limit spatial local prior information to reconstruction.And at persent PET srudy, space and time are divided into two problems. In conventional indirected parameter estimate, such as Weighted Least Square Method (WLS), the changing activity of the injected radiotracer is conventionally measured through multiple consecutive PET image reconstructions. The image of the radioactivity distribution in each frame is reconstructed independently and the whole set of frames is then used to estimate the distribution of the physiological parameter of interest by the application of an appropriate pharmacokinetic model to the time radioactivity curve either of appropriately selected functional regions or of each image element. However, the noisy PET reconstruction image will influence the accuracy of parameters estimate.Our work on PET reconstruction is based on the two mian research direction in PET study: how to further improve the quality for activity image reconstructions and how to further improve accuracy of parameters estimate. We have done following work:1, proposing a noval Markov Quadratic Hybrid Multi-Order Priors which has the effects of QM prior, QP prior and QTO prior adaptively according to different properties of different positions in objective image effectively. The new MRF hybrid prior outperforms unitary QM prior and unitary QP prior.2, studing how to incorporate more prior knowledge to guide PET image reconstruction, a great quantity of experiments were carried out with common prior with different size and and non-local a priori a priori. From the analyses and experiments presented in this paper, we can see that just enlarging the sizes of neighborhood can not effectively incorporate more large-scale knowledge into Bayesian reconstruction. On the other hand, the nonlocal prior, which is devised to exploit the large-scale or global connectivity and continuity knowledge in the image, demonstrates a more effective and robust regularization for emission reconstruction than the conventional local QM prior and Huber prior.3, proposing a noval spatio-temporal prior based on two-tissue compartmental model and neighborhood information. Time and space factors are general take into account in the new spatio-temporal prior. Based on spatio-temporal prior, kinetic parameters and PET activity images could be estimated and reconstructed synchronous. And in the simulation experiment, activity reconstruction image and parameter estimation based on spatio-temporal prior has better quality than that with the conventional method.