X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. However, the limited availability of such data poses challenges for developing robust deep learning models. In this work, we present the first framework for generating dark-field images directly from standard attenuation chest X-rays using an Uncertainty-Guided Progressive Generative Adversarial Network. The model incorporates both aleatoric and epistemic uncertainty to improve interpretability and reliability. Experiments demonstrate high structural fidelity of the generated images, with consistent improvement of quantitative metrics across stages. Furthermore, out-of-distribution evaluation confirms that the proposed model generalizes well. Our results indicate that uncertainty-guided generative modeling enables realistic dark-field image synthesis and provides a reliable foundation for future clinical applications.

翻译：X射线暗场成像通过小角度散射可视化微观组织结构变化，为传统衰减成像提供了补充性的诊断信息。然而，此类数据的有限获取对开发稳健的深度学习模型构成了挑战。本研究提出了首个直接从标准衰减式胸部X射线生成暗场图像的框架，该框架采用不确定性引导的渐进生成对抗网络。该模型同时融入了偶然不确定性和认知不确定性，以提升可解释性与可靠性。实验表明，所生成图像具有较高的结构保真度，各阶段定量指标均获得持续改进。此外，分布外评估证实了所提模型具备良好的泛化能力。我们的研究结果表明，不确定性引导的生成建模能够实现逼真的暗场图像合成，并为未来的临床应用提供了可靠基础。