Virtual Imaging-Guided Harmonization of Chest X-rays Using a Generative Adversarial Neural Network

To reduce variability in chest radiography (CXR) from acquisition and post-processing, and assess whether harmonization improves image quality and down-stream diagnostic performance.

A generative adversarial network (GAN) was trained exclusively on virtual images produced by a Virtual Imaging Trial (VIT). The model maps non-harmonized CXRs to noise-free, unprocessed references using a dual U-Net with contrastive loss. Evaluation spanned virtual, physical-phantom, and clinical data using generic (GIQMs) and chest-specific (CIQMs) metrics. A phantom benchmark compared the method with ComBat harmonization and a conventional denoising algorithm. Clinical generalization was tested on VinDr-CXRs, examining feature compactness via Uniform Manifold Approximation and Projection (UMAP). We also assessed NIH-CXRs, quantifying diagnostic accuracy with bootstrap uncertainty.

Compared with non-harmonized images, harmonized CXRs yielded approximately 89% lower NRMSE, 50% higher PSNR, and 86% higher SSIM. On phantom data, CIQM variability fell from 0.255 to 0.026 and was reduced more consistently than with ComBat or denoising. Clinical analyses on VinDr-CXRs showed tighter UMAP clusters for harmonized features, indicating suppression of acquisition-related variability. On NIH-CXRs, training and testing a classifier in the harmonized domain improved diagnostic accuracy over the non-harmonized domain and lowered cross-domain sensitivity; pleural and cardiac categories showed consistent gains, while texture-dependent labels exhibited task-dependent effects.

VIT-guided training enables a GAN to harmonize CXRs toward a raw reference, improving quantification variability and harmonizing image representations across systems.

The proposed virtual-to-clinical strategy is scalable and generalizable, offering a practical path to standardized CXR appearance and reliable downstream detection across institutions.