ProTCT: Projection quantification and fidelity constraint integrated deep reconstruction for Tangential CT

TL;DR

ProTCT introduces a novel deep reconstruction method for Tangential CT that enhances image quality by analyzing sampling conditions and integrating artifact suppression with fidelity constraints, applicable to large-diameter sample imaging.

Contribution

This work presents a new deep reconstruction framework that combines projection quantification and fidelity constraints, improving Tangential CT image quality and guiding system design.

Findings

Effective artifact removal and detail preservation demonstrated on simulated data.

Improved slice quality and structure restoration shown on real datasets.

Provides practical guidelines for TCT system design based on sampling analysis.

Abstract

Tangential computed tomography (TCT) is a useful tool for imaging the large-diameter samples, such as oil pipelines and rockets. However, TCT projections are truncated along the detector direction, resulting in degraded slices with radial artifacts. Meanwhile, existing methods fail to reconstruct decent images because of the ill-defined sampling condition in the projection domain and oversmoothing in the cross-section domain. In this paper, we propose a projection quantification and fidelity constraint integrated deep TCT reconstruction method (ProTCT) to improve the slice quality. Specifically, the sampling conditions for reconstruction are analysed, offering practical guidelines for TCT system design. Besides, a deep artifact-suppression network together with a fidelity-constraint module that operates across both projection and cross-section domains to remove artifacts and restore edge details. Demonstrated on simulated and real datasets, the ProTCT shows good performance in structure restoration and detail retention. This work contributes to exploring the sampling condition and improving the slice quality of TCT, further promoting the application of large view field CT imaging.