A hybrid-cascaded framework for MLEM based image reconstruction

Abstract

This paper proposed a hybrid-cascaded framework for image reconstruction. This framework consists of breaking the reconstruction process into two parts viz. primary and secondary. The primary part consists of simple algebraic iterative technique using Simultaneous Algebraic Reconstruction Technique (SART) for image reconstruction. The task of primary reconstruction will be to provide an enhanced image to secondary part to be used as an initial estimate for reconstruction process. The secondary part is a hybrid combination of two parts namely the reconstruction part and the prior part. The reconstruction is done using Maximum Likelihood Expectation Maximization (MLEM) while median anisotropic diffusion (MedAD) filter is used as prior to deal with ill-posedness. Using primary and secondary reconstruction steps in a cascaded manner, yields significant improvements in reconstructed image quality. It also accelerates the convergence and provides enhanced results using the projection data. The obtained results justify the applicability of the proposed method.