Beam-hardening correction in clinical x-ray dark-field chest radiography using deep-learning-based bone segmentation

Abstract

Background

Dark-field radiography is a novel x-ray imaging modality that provides complementary diagnostic information by visualising microstructural properties of lung tissue. Implemented via a Talbot–Lau interferometer integrated into a conventional x-ray system, it permits simultaneous acquisition of perfectly registered attenuation and dark-field radiographs. Clinical studies have shown that dark-field radiography outperforms conventional radiography in diagnosing and staging pulmonary diseases, yet the polychromatic nature of medical x-ray sources causes beam hardening and introduces structured artifacts, especially from ribs and clavicles.

Purpose

To address the artificial dark-field signal arising from beam-hardening and thereby improve the reliability of clinical dark-field chest radiography by suppressing bone-induced artifacts.

Methods

A segmentation-based beam-hardening correction (BHC) was developed that employs deep learning to segment ribs and clavicles and uses attenuation-contribution masks derived from dual-layer detector computed-tomography data to refine the material distribution and estimate beam-hardening effects. The rib segmentation network was trained on 196 chest radiographs with 49 validation images (VinDr-RibCXR), and a clavicle network was trained on 56 images with 12 validation and 12 test cases. The trained models were applied to 174 dark-field chest radiographs (51 chronic obstructive pulmonary disease, 86 COVID-19, 37 healthy) and spectral CT scans from two patients; input data consisted of attenuation and dark-field images and outputs were corrected dark-field images and derived lung-signal metrics.

Results

The proposed method markedly reduced bone-induced artifacts and improved the homogeneity of the lung dark-field signal. In comparative analyses, the corrected images exhibited diminished structured cross-talk between attenuation and dark-field channels, enhancing both visual interpretation and quantitative consistency across cohorts.

Conclusions

By combining deep-learning-based anatomical segmentation with material-specific attenuation weighting, the proposed BHC suppresses the artificial dark-field signal caused by polychromatic x-ray spectra, leading to more reliable assessment of pulmonary microstructure in clinical dark-field chest radiography.