A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields

TL;DR

This paper introduces a convex reconstruction model for X-ray tomography that accounts for uncertain flat-field measurements, significantly reducing artifacts in low-quality data scenarios like dynamic CT.

Contribution

The paper presents a novel convex model that explicitly incorporates flat-field uncertainty, improving reconstruction quality over traditional methods.

Findings

Effective in reducing ring artifacts in simulated data

Demonstrates improved reconstructions with real data

Outperforms classical methods under uncertain measurement conditions

Abstract

Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real data sets.