Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT

TL;DR

This paper presents an iterative total variation minimization algorithm for accurate image reconstruction in divergent-beam CT from limited and few-view data, improving rapid and low-dose imaging.

Contribution

It introduces a novel TV-based iterative reconstruction method tailored for divergent-beam CT with under-sampled data, applicable to various tomographic modalities.

Findings

Effective in reconstructing images from limited-angle data

Demonstrated success in fan-beam CT simulations

Potential for generalization to cone-beam CT

Abstract

In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.