Pixel-weighted Multi-pose Fusion for Metal Artifact Reduction in X-ray Computed Tomography

TL;DR

This paper introduces a novel pixel-weighted multi-pose fusion technique using Multi-Agent Consensus Equilibrium to effectively reduce metal artifacts in X-ray CT reconstructions by integrating complementary data from multiple poses.

Contribution

It presents a new multi-pose fusion method with pixel-weighted integration within the MACE framework for improved metal artifact reduction in CT imaging.

Findings

Significantly reduces metal artifacts compared to single-pose reconstructions.

Demonstrates effectiveness on real CT data with metal inserts.

Outperforms existing methods in artifact suppression.

Abstract

X-ray computed tomography (CT) reconstructs the internal morphology of a three dimensional object from a collection of projection images, most commonly using a single rotation axis. However, for objects containing dense materials like metal, the use of a single rotation axis may leave some regions of the object obscured by the metal, even though projections from other rotation axes (or poses) might contain complementary information that would better resolve these obscured regions. In this paper, we propose pixel-weighted Multi-pose Fusion to reduce metal artifacts by fusing the information from complementary measurement poses into a single reconstruction. Our method uses Multi-Agent Consensus Equilibrium (MACE), an extension of Plug-and-Play, as a framework for integrating projection data from different poses. A primary novelty of the proposed method is that the output of different MACE agents are fused in a pixel-weighted manner to minimize the effects of metal throughout the reconstruction. Using real CT data on an object with and without metal inserts, we demonstrate that the proposed pixel-weighted Multi-pose Fusion method significantly reduces metal artifacts relative to single-pose reconstructions.