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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

11.1K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Beam-hardening correction in clinical x-ray dark-field chest radiography using deep-learning-based bone segmentation.

Lennard Kaster1,2,3, Maximilian E Lochschmidt1,2,3, Anne M Bauer1,2,3

  • 1Chair of Biomedical Physics, Department of Physics, TUM School of Natural Sciences, Technical University of Munich, Garching, Germany.

Medical Physics
|April 3, 2026
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Summary

This study introduces a deep learning method to reduce bone artifacts in dark-field chest radiography. The new technique improves the accuracy of visualizing lung microstructure for better disease diagnosis.

Keywords:
chest radiographydark‐field imagingdeep learningsegmentationx‐ray imaging

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Area of Science:

  • Medical Imaging
  • Radiography
  • Pulmonary Diagnostics

Background:

  • Dark-field radiography offers advanced lung microstructural imaging.
  • It uses a Talbot-Lau interferometer for simultaneous attenuation and dark-field images.
  • Polychromatic X-rays cause beam hardening, creating artifacts from bones like ribs and clavicles.

Purpose of the Study:

  • To reduce bone-induced artifacts in dark-field chest radiography.
  • To improve the reliability of clinical dark-field imaging.
  • To suppress artificial dark-field signals from beam hardening.

Main Methods:

  • Developed a segmentation-based beam-hardening correction (BHC) using deep learning.
  • Utilized dual-layer detector CT data for attenuation-contribution masks.
  • Trained models on chest radiographs and applied them to dark-field images and spectral CT scans.

Main Results:

  • Significantly reduced bone-induced artifacts in dark-field images.
  • Enhanced the homogeneity of the lung dark-field signal.
  • Diminished cross-talk between attenuation and dark-field channels, improving interpretation.

Conclusions:

  • The BHC method effectively suppresses artificial dark-field signals from polychromatic X-rays.
  • Combines deep learning segmentation with material-specific weighting.
  • Enables more reliable assessment of pulmonary microstructure in clinical settings.