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 this https URL.
Abstract (translated)
计算机断层扫描（CT）可提供丰富的三维解剖细节，但常受限于高辐射暴露、高昂成本及可及性有限。而标准胸部X光片虽成本效益高且广泛可及，却仅能提供二维投影，病理信息有限。从二维X光重建三维CT体积为实现诊断可及性提升提供了变革性方案，但现有方法多依赖合成X光投影，限制了临床泛化能力。本研究提出AXON——一个基于多阶段扩散模型的框架，可直接从真实X光重建高保真三维CT体积。AXON采用由粗到细策略：首阶段基于布朗桥扩散模型进行全局结构合成，随后通过ControlNet进行局部强度优化。该框架同时支持双平面X光输入以缓解二维至三维重建固有的深度模糊问题，并集成超分辨率网络将生成体积上采样至诊断级分辨率。在公开及外部数据集上的评估表明，AXON显著优于现有最先进方法，在PSNR上提升11.9%，SSIM上提升11.0%，且在不同临床分布中展现出稳健的泛化能力。代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.26509
