Data-driven Method for 3D Axis-symmetric Object Reconstruction from Single Cone-beam Projection Data

TL;DR

This paper introduces a novel regularization-based method using adaptive tight frames for 3D axis-symmetric object reconstruction from a single cone-beam projection, enabling instant deformation analysis in materials.

Contribution

It presents a new regularization approach with adaptive tight frames and a numerical algorithm for efficient 3D reconstruction from minimal data, addressing limitations of traditional layer-by-layer methods.

Findings

Effective reconstruction demonstrated on simulation data.

Outperforms traditional methods in capturing instant deformation.

Algorithm efficiently computes imaging matrix.

Abstract

In this paper we consider 3D axis-symmetric (AS) object reconstruction from single cone-beam x-ray projection data. Traditional x-ray CT fails to capture fleeting state of material due to the long time for data acquisition at all angles. Therefore, AS object is devised to investigate the instant deformation of material under pulse change of environment because single projection data is enough to reconstruct its inner structure. Previous reconstruction methods are layer by layer, and ignore the longitudinal tilt of x-ray paths. We propose a regularization method using adaptive tight frame to reconstruct the 3D AS object structure simultaneously. Alternating direction method is adopted to solve the proposed model. More importantly, a numerical algorithm is developed to compute imaging matrix. Experiments on simulation data verify the effectiveness of our method