SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
This addresses a practical issue for medical imaging by enabling safer and more efficient diagnosis and education, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of synthesizing multi-view X-ray images from a single view to reduce radiation exposure and simplify clinical workflows, achieving higher-resolution outputs with improved angular control.
X-ray imaging is a rapid and cost-effective tool for visualizing internal human anatomy. While multi-view X-ray imaging provides complementary information that enhances diagnosis, intervention, and education, acquiring images from multiple angles increases radiation exposure and complicates clinical workflows. To address these challenges, we propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Unlike prior methods, which are limited in angular range, resolution, and image quality, our approach leverages the Diffusion Transformer to preserve fine details and employs a weak-to-strong training strategy for stable high-resolution image generation. Experimental results demonstrate that our method generates higher-resolution outputs with improved control over viewing angles. This capability has significant implications not only for clinical applications but also for medical education and data extension, enabling the creation of diverse, high-quality datasets for training and analysis. Our code is available at https://github.com/xiechun298/SV-DRR.