A Temporal-Consistency Segmentation-Inpainting Virtual Subtraction for Roadmap Imaging

Digital subtraction angiography (DSA) roadmap imaging is widely used in endovascular interventions, but conventional mask-based subtraction is often affected by device contamination and motion-related misregistration. Existing mask-free methods commonly rely on unconstrained image synthesis, with limited interpretability and temporal stability. This study developed an interpretable virtual subtraction framework that uses temporal information to improve guidewire visualization and background reconstruction without a physical mask. A temporal-consistency segmentation-inpainting framework was developed to generate device-only subtraction images from consecutive fluoroscopic frames. A motion-adaptive spatiotemporal attention module adjusted temporal feature aggregation according to inter-frame guidewire motion. Historical frames were aligned and combined through confidence-weighted aggregation to guide mean-reverting stochastic differential equation-based background inpainting. An explicit temporal-consistency objective was used to reduce fluctuations between consecutive reconstructed backgrounds. The framework was evaluated on clinical fluoroscopic sequences acquired at three hospitals through comparisons with recent guidewire segmentation and synthetic DSA generation methods, component-wise ablation studies, sensitivity analyses, and quantitative image-quality assessment. The proposed segmentation network achieved a Dice coefficient of 0.917 and a guidewire breakage rate of 3.7%. Background reconstruction achieved a peak signal-to-noise ratio of 31.9 dB and a structural similarity index of 0.925. Additional experiments showed that five input frames at 15 fps provided the best balance between segmentation accuracy and guidewire continuity, while five historical reference frames and 50 reverse-process sampling steps provided a favorable balance between reconstruction quality and inference time. The proposed framework enables mask-free virtual subtraction by integrating temporal information into guidewire segmentation and background reconstruction. It improves guidewire continuity and promotes stable background reconstruction while retaining an interpretable and spatially constrained processing pipeline for DSA roadmap imaging.