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X-ray Imaging01:24

X-ray Imaging

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 X-rays, and by 1900, X-ray was widely...
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Related Experiment Video

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Estimating Total Lung Volume from Pixel-Level Thickness Maps of Chest Radiographs Using Deep Learning.

Tina Dorosti1,2,3, Manuel Schultheiß1,2,3, Philipp Schmette1

  • 1Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.

Radiology. Artificial Intelligence
|May 28, 2025
PubMed
Summary

A U-Net deep learning model accurately estimates total lung volume (TLV) from frontal chest radiographs using pixel-level lung thickness maps. This method shows strong correlations and low errors, proving effective for both synthetic and real radiographic data.

Keywords:
Frontal Chest RadiographsLung Thickness MapPixel-LevelTotal Lung VolumeU-Net

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Area of Science:

  • Radiology
  • Medical Imaging
  • Deep Learning

Background:

  • Accurate estimation of total lung volume (TLV) is crucial for diagnosing and managing respiratory diseases.
  • Traditional methods for TLV estimation often rely on computed tomography (CT), which is resource-intensive.
  • Developing non-invasive methods using standard frontal chest radiographs is highly desirable.

Purpose of the Study:

  • To estimate total lung volume (TLV) from frontal chest radiographs using pixel-level lung thickness maps.
  • To utilize a U-Net deep learning model for generating these lung thickness maps.
  • To validate the model's performance on both synthetic and real radiographic data.

Main Methods:

  • A retrospective study utilized chest CT scans from public datasets (Luna16, PE Detection Challenge) and a clinical dataset (Klinikum Rechts der Isar).
  • Synthetic frontal chest radiographs and lung thickness maps were generated via forward projection of CT scans.
  • A U-Net model was trained on synthetic data to predict lung thickness maps and subsequently estimate TLV, with performance assessed by MSE and Pearson correlation.

Main Results:

  • The U-Net model demonstrated low error rates and strong correlations between predicted and CT-derived TLV across synthetic and real datasets (e.g., r=0.91, P < .001 for real data).
  • The model achieved the highest performance on the Luna16 test dataset, showing the lowest MSE (0.09 L²) and strongest correlation (r=0.99, P < .001).
  • Pixel-level lung thickness maps generated by the U-Net model successfully estimated TLV.

Conclusions:

  • U-Net-generated pixel-level lung thickness maps provide a viable method for estimating total lung volume from frontal chest radiographs.
  • The approach is effective for both synthetic and real radiographic images, offering a promising tool for clinical applications.
  • This deep learning-based method enhances the utility of standard chest radiographs for lung volume assessment.