AngioDiff: Structure-Preserving and 3D-Consistent Diffusion for CT Angiography Synthesis

Computed tomography angiography (CTA) plays a crucial role in the diagnosis of thoracic vascular diseases, yet the need for iodinated contrast agents raises safety and accessibility concerns. Generating synthetic contrast-enhanced CT (CECT) from non-contrast CT (NCCT) offers a promising solution to these challenges. However, existing generative methods often struggle to preserve fine vascular details and continuity along the anisotropic z-axis in volumetric data. In this work, we present AngioDiff, a novel diffusion-based framework for high-fidelity CTA synthesis from NCCT. Our method leverages a conditional diffusion model with a mean reverting prior and incorporates a sliding window mechanism with asynchronous denoising to effectively model large-scale 3D CT volumes. To further stream-line the process and address edge artifacts, we introduce a sequence padding strategy that simplifies training and sampling while enhancing structural continuity. Furthermore, our network design combines spatial and axial attention modules to adequately capture intra-slice and inter-slice dependencies. Comprehensive experiments on multi-center datasets demonstrate that AngioDiff consistently outperforms state-of-the-art methods in both 2D slice-based and 3D volume-based quantitative metrics, achieving superior anatomical fidelity and volumetric consistency. This work highlights the clinical potential of diffusion-based models for agent-free CTA, offering a safer and more accessible alter-native to traditional imaging protocols.