Optimality of the fully discrete filtered backprojection algorithm for tomographic inversion

On the optimality of the filtered backprojection algorithm.

It is shown that under certain conditions the filtered backprojection algorithm produces a computed tomographic reconstruction for which the statistical accuracy attainable in the amplitude estimation of large-area objects meets the general lower bound derived by Tretiak. In this sense, filtered backprojection is an optimum algorithm.

The Semidiscrete Filtered Backprojection Algorithm Is Optimal for Tomographic Inversion

Introduction to the Mathematics of Computed Tomography

Computed tomography (CT) entails the reconstruction of a function f from line integrals of f . This mathematical problem is encountered in a growing number of diverse settings in medicine, science, and technology. This introductory article is divided into three parts. The first part is concerned with general theory and explores questions of uniqueness, stability and inversion, as well as detect...

A Local and Iterative Neural Reconstruction Algorithm for Cone-Beam Data

This work presents a new neural algorithm designed for the reconstruction of tomographic images from Cone Beam data. The algorithm is iterative and based on a set of neural networks that are working locally and sequentially. The algorithm was compared with the iterative ART algorithm and the filtered backprojection (FBP) method. The results show how the proposed algorithm is much more accurate ...

Accurately approximating algebraic tomographic reconstruction by filtered backprojection

In computed tomography, algebraic reconstruction methods tend to produce reconstructions with higher quality than analytical methods when presented with limited and noisy projection data. The high computational requirements of algebraic methods, however, limit their usefulness in practice. In this paper, we propose a method to approximate the algebraic SIRT method by the computationally efficie...