Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects

TL;DR

This paper introduces a novel TV regularization method in cylindrical coordinates for improving tomographic reconstructions of cylindrically symmetric objects from sparse X-ray data, enhancing image quality in high-speed dynamic experiments.

Contribution

It presents a new TV regularization approach tailored for cylindrical symmetry, outperforming existing methods in reconstructing sparse-view CT images of symmetric objects.

Findings

The proposed method yields higher quality reconstructions than competing approaches.

It effectively exploits cylindrical symmetry to improve image accuracy.

Demonstrates robustness with sparse and limited-angle data.

Abstract

Flash X-ray computed tomography (CT) is an important imaging modality for characterization of high-speed dynamic events, such as Kolsky bar impact experiments for the study of mechanical properties of materials subjected to impulsive forces. Due to experimental constraints, the number of X-ray views that can be obtained is typically very sparse in both space and time, requiring strong priors in order to enable a CT reconstruction. In this paper, we propose an effective method for exploiting the cylindrical symmetry inherent in the experiment via a variant of total variation (TV) regularization that operates in cylindrical coordinates, and demonstrate that it outperforms competing approaches.