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

TLDR

Tomographic imaging often suffers from under‑sampling and limited‑angle data, which hinder reconstruction and increase radiation dose, making few‑view CT a desirable but challenging approach. The study develops an iterative total‑variation minimization algorithm for divergent‑beam CT to address these reconstruction challenges. The algorithm iteratively minimizes image total variation and is applicable to divergent‑beam CT, with a formulation that can be extended to cone‑beam and other modalities. Numerical experiments on fan‑beam CT with insufficient data show that the TV algorithm reconstructs images accurately.

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.