MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography
An X-ray computed tomography (CT), metal artifact reduction (MAR) remains a major challenge because metallic implants violate standard CT forward-model assumptions, producing severe streaking and shadowing artifacts that degrade diagnostic quality. We propose MGMAR, a metal-guided MAR method that explicitly leverages metal-related information throughout the reconstruction pipeline. MGMAR first generates a high-quality prior image by training a conditioned implicit neural representation (INR) using metal-unaffected projections, and then incorporates this prior into a normalized MAR (NMAR) framework for projection completion. To improve robustness under severe metal corruption, we pretrain ...
| Authors | Hyoung Suk Park, Kiwan Jeon |
|---|---|
| Primary category | eess.IV |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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