High Quality PET Image Reconstruction Based On Different Prior Acquisitions

As an effective functional imaging method for medical images, positron emission tomography (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. 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 compution 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 is still notorious for two drawbacks: 1, the reconstructed images always start deteriorating to produce "checkerboard effect" as the iteration proceeds; 2, ML-EM approach suffers slow convergence, and a large number of iterations are needed to be performed before obtaining acceptable reconstructions.Many methods have been proposed to overcome above two frawbacks in the past twenty years. On one hand, Bayesian methods or equivalently MAP (Maximum A Posteriori) methods, which incorporate MRF prior information of objective isotope density data into the ML-EM algorithm through regularization or prior terms, 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. On the other hand, as to the drawback of slow convergence for ML-EM algorithm, some effective solutions and algorithms have also been proposed. In 1994, H. Malcolm Houdson and Richard S. Larkin proposed segementing the original whole sonogram data into several ordered data by OS (Ordered Subsets) method to lower the compution cost of each iteration step; J. A. Fessler and his group proposed fast convergent SAGE (Space-Alternating Generalized EM) algorithm and PSCD (Paraboloidal Surrogate Coordinate Ascent) algorithm for PET. And Erkan (?) Mumcuoglu, David S. Lalush and Fessler also applied CG (Conjugate Gradient) in PET reconsruction algorithm to obtain fast convergence rate.As to the problem of reconstruction rate, the iterative methods which incorporate the statistical characters of scaned data, although able to reconstruct better images than conventional FBP approaches, their applications are also hindered by the slow reconstruction rate. As to the problem of reconstructed image quality, it is found that the large mount of noise in the detected sonogram data always impose a negative effect upon reconstruction, and such a negative effect might run through the whole iterative process. Bayesian reconstruction can greatly improve reconstruction by incorporating image prior information. But we also find that, heavily relied on the information within a limited neighborhood, conventional Bayesian methods can only contribuite limit local prior information to reconstruction. On one hand, the smoothing QM (Quadratic Membrane) smoothing prior tends to produce an unfavorable oversmoothing effect, and on the other hand the edge-preserving nonquadratic prior might also bring staircase edge artifact to reconstruction.Our work on PET reconstruction algorithm is based on how to further accelerate the convergence rate of reconstruction algorithm and how to further improve the quality for reconstructions.We have done following work on PET reconstruction algorithms:1, proposeing a new fast OSCG (Ordered Subsets Conjugate Gradient) algorithm, which combines the idea of OS (Odered Subset) and CG method to further accelerate the rate for PET reconstruction.2, proposing a novel coupled feedback (CF) iterative model and the relevant reconstruction algorithm. The new methods can relieve the negative effect of the noise in detected sinogram data on PET reconstruction by an iterative sinogram-correcting method.3, proposing a quadratic hybrid multi-order (QHM) prior model that combine both QM prior and QP prior effectively, the QHM prior is capable of facilitating an adaptive utilization of QM prior and QP prior in PET reconstruction.4, proposing a novel nonlocal Markov Random Fields (MRF) prior with quadratic prior energy form, which is able to exploit global image prior information and provides more effective regularization for PET reconstruction.In experiments, we apply above four approaches in emission tomography and transmission tomography in PET. Relevant experimentations and analyses show that the four new algorithms, which are all based on MRF and optimation theory, are all able to improve currently PET reconstruction to different extents, respectively...