On the efficiency of OSEM algorithm for tomographic lung CT images reconstruction

Abstract

Sometimes, computed tomography (CT) examinations need to be repeated. This may generate adverse effects on patients. To avoid it, an efficient reconstruction technique should be applied. This paper presents a qualitative and quantitative comparative study of four iterative algorithms (Algebraic Reconstruction Technique (ART), Maximum Likelihood Expectation Maximization (MLEM), Ordered-subsets expectation maximization (OSEM) and Simultaneous algebraic reconstruction technique (SART)). The four techniques are applied on a ‘dicom’ lung computed tomography image. Qualitatively, we can not differentiate between the reconstructed images. They are almost the same for all the methods. But, qualitatively, the best performance was observed with OSEM algorithm which provides the best quality image according to practically all the computed evaluation criteria. Moreover, OSEM insures this best performance in shorter processing time ranging from two to three times less compared to the other methods.