Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

TL;DR

AXON is a diffusion-based framework that reconstructs high-quality 3D CT volumes from real 2D X-ray images, improving diagnostic resolution and generalizability over existing methods.

Contribution

The paper introduces AXON, a novel multi-stage diffusion model that directly reconstructs 3D CT from real X-rays, incorporating a coarse-to-fine strategy and bi-planar input handling.

Findings

Outperforms state-of-the-art methods with 11.9% higher PSNR.

Achieves 11.0% better SSIM, indicating improved image quality.

Demonstrates strong generalization across different clinical datasets.

Abstract

Computed tomography (CT) provides rich 3D anatomical details but is often constrained by high radiation exposure, substantial costs, and limited availability. While standard chest X-rays are cost-effective and widely accessible, they only provide 2D projections with limited pathological information. Reconstructing 3D CT volumes from 2D X-rays offers a transformative solution to increase diagnostic accessibility, yet existing methods predominantly rely on synthetic X-ray projections, limiting clinical generalization. In this work, we propose AXON, a multi-stage diffusion-based framework that reconstructs high-fidelity 3D CT volumes directly from real X-rays. AXON employs a coarse-to-fine strategy, with a Brownian Bridge diffusion model-based initial stage for global structural synthesis, followed by a ControlNet-based refinement stage for local intensity optimization. It also supports bi-planar X-ray input to mitigate depth ambiguities inherent in 2D-to-3D reconstruction. A super-resolution network is integrated to upscale the generated volumes to achieve diagnostic-grade resolution. Evaluations on both public and external datasets demonstrate that AXON significantly outperforms state-of-the-art baselines, achieving a 11.9% improvement in PSNR and a 11.0% increase in SSIM with robust generalizability across disparate clinical distributions. Our code is available at https://github.com/ai-med/AXON.