MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography

TL;DR

MGMAR introduces a novel metal-guided approach for reducing artifacts in X-ray CT images by leveraging neural representations and prior knowledge, significantly improving diagnostic image quality.

Contribution

The paper presents MGMAR, a new method that uses a neural prior and metal-aware correction to enhance metal artifact reduction in CT scans.

Findings

Achieves state-of-the-art performance with an average score of 0.89 on benchmark.

Effectively suppresses metal artifacts while preserving anatomical details.

Pretraining the neural prior improves robustness and convergence speed.

Abstract

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 the encoder-conditioned INR on paired metal-corrupted and artifact-free CT images, thereby embedding data-driven prior knowledge into the INR parameter space. This prior-embedded initialization reduces sensitivity to random initialization and accelerates convergence during measurement-specific refinement. The encoder takes a metal-corrupted reconstruction together with a recursively constructed metal artifact image, enabling the latent field to capture metal-dependent global artifact patterns. After projection completion using the INR prior, we further suppress residual artifacts using a metal-conditioned correction network, where the metal mask modulates intermediate features via adaptive instance normalization to target metal-dependent secondary artifacts while preserving anatomical structures. Experiments on the public AAPM-MAR benchmark demonstrate that MGMAR achieves state-of-the-art performance, attaining an average final score of 0.89 on 29 clinical test cases.