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Dual-energy x-ray imaging for AI-based lung tumor localization using foundation models: a phantom study.

Nawal Alqethami1, Wentao Xie1, Tom Julius Blöcker2

  • 1Faculty of Physics, Chair of Experimental Physics - Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, Bavaria, 85748, Germany.

Physics in Medicine and Biology
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

AI foundation models combined with dual-energy X-ray imaging significantly improve tumor localization accuracy, especially in challenging areas with bone overlap. This advanced technique reduces missed tumor fractions compared to high-energy imaging alone.

Keywords:
AI-based trackingDual energy X-ray imagingFast kVp switchingLung tumor localization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Dual-energy (DE) X-ray imaging offers potential for improved tissue contrast.
  • AI foundation models are increasingly used for medical image analysis.
  • Accurate tumor tracking is crucial for effective treatment planning.

Purpose of the Study:

  • To assess AI-based tumor tracking performance using DE X-ray versus high-energy (HE) X-ray.
  • To evaluate the impact of DE imaging parameters on tumor localization.
  • To compare AI model accuracy with manual ground truth segmentation.

Main Methods:

  • DE planar imaging with fast kVp-switching was performed on a phantom.
  • Weighted logarithmic subtraction generated soft-tissue-enhanced DE images.
  • An AI foundation model was used for frame-by-frame tumor localization and compared to ground truth.

Main Results:

  • Optimal weighting factors for bone suppression ranged from 0.57 to 0.79.
  • Increased tube current improved DE contrast-to-noise ratio (CNR).
  • AI model achieved high localization accuracy (Dice > 0.9, HD95 < 2-3 mm) on both DE and HE images.
  • DE imaging significantly reduced the missed tumor fraction compared to HE images (e.g., <15% for DE vs. 50-55% for HE with 80/120 kVp).

Conclusions:

  • Integrating DE imaging with AI foundation models enhances tumor localization accuracy.
  • This approach is particularly beneficial in anatomically challenging regions with tumor-bone overlap.
  • DE imaging shows superior performance in minimizing missed tumor areas compared to HE imaging.