# XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors

arXiv: https://arxiv.org/abs/2012.02407 · HTML: https://mlsanity.com/paper/2012.02407/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Denoising Diffusion Medical Models](https://mlsanity.com/paper/2304.09383/) (2023)
- [DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays](https://mlsanity.com/paper/2503.17804/) (2025)
- [Neural Radiance Projection](https://mlsanity.com/paper/2203.07658/) (2022)
- [X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols](https://mlsanity.com/paper/2104.04179/) (2021)
- [SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model](https://mlsanity.com/paper/2507.05148/) (2025)
- [X-ray Dissectography Enables Stereotography to Improve Diagnostic Performance](https://mlsanity.com/paper/2111.15040/) (2021)
- [LatXGen: Towards Radiation-Free and Accurate Quantitative Analysis of Sagittal Spinal Alignment Via Cross-Modal Radiographic View Synthesis](https://mlsanity.com/paper/2509.24165/) (2025)
- [PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement](https://mlsanity.com/paper/2606.19867/) (2026)

## Abstract (arXiv)

A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. Hence, radiograph analysis naturally requires physicians to relate the prior about 3D human anatomy to 2D radiographs. Synthesizing novel radiographic views in a small range can assist physicians in interpreting anatomy more reliably; however, radiograph view synthesis is heavily ill-posed, lacking in paired data, and lacking in differentiable operations to leverage learning-based approaches. To address these problems, we use Computed Tomography (CT) for radiograph simulation and design a differentiable projection algorithm, which enables us to achieve geometrically consistent transformations between the radiography and CT domains. Our method, XraySyn, can synthesize novel views on real radiographs through a combination of realistic simulation and finetuning on real radiographs. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.