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.
Choi et al. (2026) studied this question.