An OSEM-based hybrid-cascaded framework for PET/SPECT image reconstruction

Abstract

PET/SPECT reconstruction algorithms often used the data contain a high amount of statistical noise, that have been acquired from a limited angular range, or have a limited number of views. Generally, an iterative reconstruction algorithm suffers from the problem of slow convergence, choice of optimum initial point and ill-posedness. To address these aforementioned issues, in this paper a hybrid-cascaded framework of Ordered Subsets Expectation Maximisation (OSEM) is proposed. This allows us to use more than one algorithm for reconstruction and extract the benefits of each. The proposed model includes two steps: primary and secondary. In the primary step, SART method is used as an initial guess for OSEM to deal with the problem of initialisation and convergence. The task of primary step will be to provide an enhanced image to secondary step to be used as an initial estimate for reconstruction process. The secondary step is a hybrid combination of two parts namely the OSEM reconstruction and anisotropic diffusion (AD) as a prior. By incorporating a suitable prior knowledge the problem of ill-posedness is addressed. A comparative analysis of the proposed model with some other standard methods in literature is presented both qualitatively and quantitatively for phantom test data sets. The obtained result justifies the applicability of the proposed model.