MICCAI 2025

This repository contains the model presented in the paper SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model.

Authors: Chun Xie, Yuichi Yoshii, Itaru Kitahara
University of Tsukuba | Tokyo Medical University Ibaraki Medical Center

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 this https URL .

We propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images up to 1024x1024 resolution from a single view.

The name SV-DRR, short for Single-View DRR, is inspired by Digitally Reconstructed Radiography (DRR).

Unlike DRR, which renders X-ray projections from a 3D CT volume, our method synthesizes novel views directly from a single 2D projection.

The official code and further details can be found on the GitHub repository.

To ensure compatibility and reproducibility, follow these steps to set up the environment:

You can download the pretrained models by either:

This will download all models into the models/ directory. Shared components will be stored in the shared/ folder, and symbolic links will be created in each model folder accordingly.

Important Note: The coordinate system of LIDC-IDRI-DRR is opposite to the intuitive one — the polar angle increases downward, and the azimuth angle increases when rotating to the left. To invert the pose coordinate system, use the --flip_pose option.

For more detailed usage, including dataset inference and training, please refer to the GitHub repository.

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