An Efficient Hybrid-Cascaded Framework for Emission Computed Tomography Using OSEM Image Reconstruction Algorithm

Abstract

In the literature, it is well established that the ordered subset expectation maximization (OSEM) algorithm, is an accelerated version of original maximum likelihood expectation maximization (MLEM) method, which performs better than MLEM in terms of processing speed. But, the quality of the reconstructed image with OSEM still remains same as MLEM and it also suffers from the problem of initialization and ill-posedness. To address these issues, in this paper a hybrid-cascaded framework of OSEM is proposed. This framework consists of breaking the reconstruction process into two parts, viz., primary and secondary. In the primary part, simultaneous algebraic reconstruction technique (SART) is applied to overcome the problems of slow convergence and initialization. SART provides fast convergence and produce good reconstruction results with lesser number of iterations than other iterative methods. The task of primary part is 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 OSEM algorithm while anisotropic diffusion (AD) is used prior to deal with ill-posedness. A comparative analysis of the proposed model with some other standard methods in the literature is presented both qualitatively and quantitatively for a phantom test data and standard medical image. The proposed model yields significant improvements in reconstruction quality from the projection data.