Spatial-Differential Guidance and Structure Anchoring for Selective Mild Motion Artifact Correction in Head CT

Head CT scans are critical for diagnosing acute neurological conditions, yet involuntary patient motion during scanning frequently introduces motion artifacts that degrade image quality. A particularly challenging scenario is mild motion artifacts in stationary CT, where artifacts are axially sparse but spatially entangled with normal anatomical structures. Consequently, correction requires both precise artifact localization and preservation of anatomical consistency. To address these challenges, we propose a spatial-differential and structure-anchoring framework for head CT motion artifact correction. The framework introduces an explicit gradient-based discriminative response field (DRF) as a spatial prior, which is incorporated through a spatial-differential attention (SDA) module to guide the correction network toward artifact-corrupted regions. Furthermore, we propose a semantic structure anchoring (SSA) strategy that aligns intermediate representations with artifact-free anatomical features from a pretrained 3D visual encoder, encouraging structural consistency during restoration. Extensive experiments on simulated datasets with varying artifact severity, a public dataset, and a real-world dataset demonstrate our framework's effectiveness. The proposed method achieves improved restoration quality on mild motion artifacts, reducing MAE by 22.7% and achieving 37.62dB PSNR, while maintaining strong performance across diverse evaluation settings.