Hybrid prior method for positron emission tomographic image reconstruction

Maximum a-posteriori (MAP) estimation has the advantage of incorporating prior knowledge in the image reconstruction procedure which makes it a superior estimation technique compared to convolution back-projection (CBP), maximum likelihood (ML) etc. The inclusion of prior knowledge greatly improves the image quality. However excess smoothening occurs as the MAP-iterations are continued. In biomedical imaging sharp reconstruction is of potential use. To meet these requirements a new prior is proposed which is capable of enhancing the edges by recognizing the correlated neighbors while restoring homogeneity in the uniform regions of the reconstruction. The proposed prior serves as a post-processing technique in Bayesian domain, once an approximate smooth reconstruction is generated by MAP-algorithm. Simulated experiments show improved sharp reconstruction with the proposed post-processing technique.