标题： SV-DRR：使用扩散模型的高保真新视角X射线合成 显示英文标题

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

摘要： X射线成像是一种快速且经济的工具，用于可视化人体内部解剖结构。 虽然多视角X射线成像提供了补充信息以增强诊断、干预和教育，但从多个角度获取图像会增加辐射暴露并使临床流程复杂化。 为解决这些挑战，我们提出了一种新颖的视图条件扩散模型，用于从单个视角合成多视角X射线图像。 与之前的方法不同，我们的方法不受角度范围、分辨率和图像质量的限制，利用扩散变压器来保留细节，并采用弱到强的训练策略以实现稳定的高分辨率图像生成。 实验结果表明，我们的方法生成了更高分辨率的输出，并提高了对视角的控制能力。 这一能力不仅对临床应用具有重要意义，还对医学教育和数据扩展有重要影响，能够创建多样化的高质量数据集用于训练和分析。 我们的代码可在GitHub上获得。 显示英文摘要

摘要： 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 GitHub.