Regularization methods for the analytical statistical reconstruction problem in medical computed tomography

Abstract

The main purpose of this paper is to present the properties of our novel statistical model-based iterative approach to the image reconstruction from projections problem regarding its condition number. The reconstruction algorithm based on this concept uses a maximum likelihood estimation with an objective adjusted to the probability distribution of measured signals obtained using x-ray computed tomography. We compare this with some selected methods of regularizing the problem. The concept presented here is fundamental for 3D statistical tailored reconstruction methods designed for x-ray computed tomography.