A Critical Survey on Developed Reconstruction Algorithms for Computed Tomography Imaging from a Limited Number of Projections

Abstract

Rapid system and hardware development of X-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. Of the two reconstruction algorithms, analytical and iterative, iterative reconstruction (IR) algorithms have become a clinically viable option in CT imaging. The first CT scanners in the early 1970s used IR algorithms, but lack of computation power prevented their clinical use. In 2009, the first IR algorithms became commercially available and replaced conventionally established analytical algorithms as filtered back projection. Since then, IR has played a vital role in the field of radiology. Although all available IR algorithms share the common mechanism of artifact reduction and/or potential for radiation dose reduction, the magnitude of these effects depends upon specific IR algorithms. IR reconstructs images by iteratively optimizing an objective function. The objective function typically consists of a data integrity term and a regularization term. Therefore, different regularization priors are used in IR algorithms. This paper will briefly look at the overall evolution of CT image reconstruction and the regularization priors used in IR algorithms. Finally, a discussion is presented based on the reality of various reconstruction methodologies at a glance to find the preferred one. Consequently, we will present anticipation towards future advancements in this domain.