DiffusionXRay: A Diffusion and GAN-Based Approach for Enhancing Digitally Reconstructed Chest Radiographs

2026-03-02

AI Summary

This study addresses the challenge of digitally reconstructed radiograph (DRR) enhancement for chest X-ray imaging, where anatomical blurring and loss of lung field details often hinder fine-grained diagnostic tasks such as pulmonary nodule detection. To overcome this limitation, the authors propose a two-stage enhancement framework that synergistically integrates a denoising diffusion probabilistic model (DDPM) with an MUNIT-based generative adversarial network (GAN). In the first stage, unpaired image-to-image translation generates diverse low-quality X-ray images to alleviate data scarcity; in the second stage, a DDPM is trained on paired data to reconstruct high-fidelity DRRs. This work presents the first approach to jointly leverage DDPM and GAN architectures for DRR enhancement, significantly improving image clarity and contrast while preserving clinically critical features such as subtle lesions. Experimental results demonstrate superior performance over existing methods in both quantitative metrics and radiologist evaluations.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Deep learning-based automated diagnosis of lung cancer has emerged as a crucial advancement that enables healthcare professionals to detect and initiate treatment earlier. However, these models require extensive training datasets with diverse case-specific properties. High-quality annotated data is particularly challenging to obtain, especially for cases with subtle pulmonary nodules that are difficult to detect even for experienced radiologists. This scarcity of well-labeled datasets can limit model performance and generalization across different patient populations. Digitally reconstructed radiographs (DRR) using CT-Scan to generate synthetic frontal chest X-rays with artificially inserted lung nodules offers one potential solution. However, this approach suffers from significant image quality degradation, particularly in the form of blurred anatomical features and loss of fine lung field structures. To overcome this, we introduce DiffusionXRay, a novel image restoration pipeline for Chest X-ray images that synergistically leverages denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs). DiffusionXRay incorporates a unique two-stage training process: First, we investigate two independent approaches, DDPM-LQ and GAN-based MUNIT-LQ, to generate low-quality CXRs, addressing the challenge of training data scarcity, posing this as a style transfer problem. Subsequently, we train a DDPM-based model on paired low-quality and high-quality images, enabling it to learn the nuances of X-ray image restoration. Our method demonstrates promising results in enhancing image clarity, contrast, and overall diagnostic value of chest X-rays while preserving subtle yet clinically significant artifacts, validated by both quantitative metrics and expert radiological assessment.

Problem

• digitally reconstructed radiographs
• image quality degradation
• lung nodule detection
• chest X-ray enhancement
• data scarcity

Innovation

• Diffusion Models
• GANs
• Digitally Reconstructed Radiographs
• Image Restoration
• Chest X-ray Enhancement