Uncertainty-guided Generation of Dark-field Radiographs

TL;DR

This paper introduces an uncertainty-guided generative adversarial network that synthesizes dark-field X-ray images from standard chest X-rays, enhancing interpretability and clinical potential.

Contribution

It presents the first framework leveraging uncertainty-guided GANs to generate dark-field images from attenuation X-rays, improving image fidelity and generalization.

Findings

High structural fidelity of generated images

Consistent improvement in quantitative metrics

Good generalization to out-of-distribution data

Abstract

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.