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Implemented via a Talbot\u2013Lau interferometer integrated into a conventional x\u2010ray system, it permits simultaneous acquisition of perfectly registered attenuation and dark\u2010field radiographs. Clinical studies have shown that dark\u2010field radiography outperforms conventional radiography in diagnosing and staging pulmonary diseases, yet the polychromatic nature of medical x\u2010ray sources causes beam hardening and introduces structured artifacts, especially from ribs and\u00a0clavicles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address the artificial dark\u2010field signal arising from beam\u2010hardening and thereby improve the reliability of clinical dark\u2010field chest radiography by suppressing bone\u2010induced\u00a0artifacts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A segmentation\u2010based beam\u2010hardening correction (BHC) was developed that employs deep learning to segment ribs and clavicles and uses attenuation\u2010contribution masks derived from dual\u2010layer detector computed\u2010tomography data to refine the material distribution and estimate beam\u2010hardening effects. The rib segmentation network was trained on 196 chest radiographs with 49 validation images (VinDr\u2010RibCXR), and a clavicle network was trained on 56 images with 12 validation and 12 test cases. The trained models were applied to 174 dark\u2010field chest radiographs (51 chronic obstructive pulmonary disease, 86 COVID\u201019, 37 healthy) and spectral CT scans from two patients; input data consisted of attenuation and dark\u2010field images and outputs were corrected dark\u2010field images and derived lung\u2010signal metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed method markedly reduced bone\u2010induced artifacts and improved the homogeneity of the lung dark\u2010field signal. In comparative analyses, the corrected images exhibited diminished structured cross\u2010talk between attenuation and dark\u2010field channels, enhancing both visual interpretation and quantitative consistency across\u00a0cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>By combining deep\u2010learning\u2010based anatomical segmentation with material\u2010specific attenuation weighting, the proposed BHC suppresses the artificial dark\u2010field signal caused by polychromatic x\u2010ray spectra, leading to more reliable assessment of pulmonary microstructure in clinical dark\u2010field chest\u00a0radiography.<\/jats:p>\n                  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