Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling

2026-08-13

AI Summary

This study addresses the severe beam-hardening artifacts in cone-beam computed tomography (CBCT) caused by polychromatic X-rays when imaging highly attenuating metallic objects, a challenge inadequately mitigated by conventional monoenergetic reconstruction methods. The work proposes, for the first time, a physics-driven, self-calibrating end-to-end reconstruction framework that integrates Gaussian Splatting for artifact suppression. By jointly modeling a polychromatic projection process, material-dependent attenuation curves, and system response characteristics, the method simultaneously optimizes image parameters and the X-ray energy spectrum—eliminating the need for manual metal masks or strong priors. Evaluated on a high-fidelity synthetic dataset generated via Monte Carlo simulation, the approach demonstrates superior performance over existing techniques on both synthetic and real-world data, achieving state-of-the-art results in artifact reduction and reconstruction accuracy.

Abstract

Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts--especially beam hardening--when imaging materials with high attenuation such as metals. These artifacts arise from the polychromatic nature of X-rays and are not properly addressed by conventional monochromatic reconstruction algorithms. While recent neural representation-based methods offer improved reconstruction quality, they are computationally expensive and often impractical for deployment. We propose a novel physics-inspired, self-calibrating metal artifact reduction method that efficiently reconstructs 3D CBCT volumes while correcting beam hardening artifacts. Our method integrates a polychromatic X-ray projection model, material-dependent attenuation profiles, and system response modeling into a Gaussian Splatting framework. Unlike prior work, we eliminate the need for manual metal masks or strong prior assumptions, and we optimize both reconstruction parameters and X-ray spectral characteristics jointly during training. We further introduce a high-fidelity synthetic CBCT dataset generation pipeline validated on Monte-Carlo x-ray simulation toolbox and release new datasets with severe metal-induced artifacts to support the community. This is the first splat-based method for reducing beam hardening in CBCT. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in artifact suppression and reconstruction accuracy.

Problem

• metal artifact reduction
• beam hardening
• cone-beam CT
• polychromatic X-ray
• artifact suppression

Innovation

• Gaussian Splatting
• Polychromatic Modeling
• Metal Artifact Reduction
• Cone-Beam CT
• Self-Calibrating Reconstruction