SCMA: Structure-Conditioned and Metal-Aware Flow Matching for CT Metal Artifact Reduction

2026-07-30

AI Summary

This work addresses severe artifacts in CT imaging caused by metallic implants—such as streaks, dark bands, and structural distortions—arising from beam hardening, photon starvation, and scatter, which critically compromise diagnostic accuracy and quantitative analysis. To tackle this challenge, the authors propose SCMA, a novel framework that integrates sample-specific structural priors, spatially non-uniform metal degradation modeling, and projection consistency constraints into a flow-matching generative model. SCMA leverages linearly interpolated corrected images as structural conditions and employs metal masks along with their distance transforms to generate spatiotemporally aware weights, effectively suppressing artifacts while preserving fine anatomical details. Extensive experiments demonstrate that SCMA consistently outperforms existing methods on both simulated and real clinical CT data, significantly reducing metal-induced artifacts, enhancing structural fidelity, and minimizing hallucinated features inconsistent with the original projection data.

Abstract

In X-ray CT, metallic objects cause beam hardening, photon starvation, and scattering, leading to projection inconsistency, streaks, dark bands, and structural distortions that compromise clinical diagnosis and quantitative analysis. Existing metal artifact reduction (MAR) methods remain limited: optimization-based methods may leave residual artifacts or blur structures, regression networks may generalize poorly across scenarios, and generative models without sample-specific structural guidance and physical constraints may produce anatomically inconsistent structures. Flow Matching learns a continuous-time velocity field that deterministically transports a source distribution to a target distribution, providing a flexible MAR prior. However, standard unconditional Flow Matching does not exploit sample-specific structure, spatially nonuniform metal-induced degradation, or measured projections. To address these limitations, we propose SCMA, a structure-conditioned and metal-aware Flow Matching framework. First, a linear-interpolation-corrected image is fed into the velocity network with the intermediate state as a sample-specific structural condition, guiding inference toward artifact-free CT images while preserving anatomy. Second, time-varying spatial weights from the metal mask and its distance transform are incorporated into the Flow Matching loss to emphasize severe degradation within and around metal regions. Finally, conditional Flow Matching updates alternate with projection-consistency correction during inference, allowing reliable measurements outside metal traces to constrain predictions. Experiments on simulated and real CT data demonstrate that SCMA more effectively suppresses metal artifacts, preserves local anatomical structures, and reduces hallucination-like structures inconsistent with projection measurements than representative MAR methods.

Problem

• metal artifact reduction
• CT imaging
• structural distortion
• projection inconsistency
• anatomical consistency

Innovation

• Flow Matching
• metal artifact reduction
• structure conditioning
• projection consistency
• spatially adaptive weighting