SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model

TL;DR

This paper introduces SV-DRR, a diffusion model that synthesizes high-quality, multi-view X-ray images from a single view, improving resolution and angular control for medical imaging applications.

Contribution

The paper presents a novel view-conditioned diffusion model with a weak-to-strong training strategy for high-resolution multi-view X-ray synthesis from a single image.

Findings

Produces higher-resolution X-ray images with better detail preservation.

Offers improved control over viewing angles in synthesized images.

Enhances clinical, educational, and data augmentation applications.

Abstract

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.