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Deep learning-based dual-energy subtraction synthesis from single-energy kV x-ray fluoroscopy for markerless tumor tracking

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This technique improves tumor tracking accuracy in lung cancer patients, indicating potential for clinical use.

Using deep learning for dual-energy synthesis improved tumor tracking accuracy in lung cancer patients.
Errors in tumor tracking decreased from 1.80 mm to 1.68 mm on average with synthesized dual-energy images.
The deep learning model trained on a digital phantom enhanced visibility, addressing hardware limitations of dual-energy imaging.
Synthesized images increased the tracking success rate from 50.2% to 54.9% for tumor movement within a 25% range.

Wang et al. (2025) studied this question.

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View Full Paper

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Authors

This technique improves tumor tracking accuracy in lung cancer patients, indicating potential for clinical use.

Using deep learning for dual-energy synthesis improved tumor tracking accuracy in lung cancer patients.
Errors in tumor tracking decreased from 1.80 mm to 1.68 mm on average with synthesized dual-energy images.
The deep learning model trained on a digital phantom enhanced visibility, addressing hardware limitations of dual-energy imaging.
Synthesized images increased the tracking success rate from 50.2% to 54.9% for tumor movement within a 25% range.

Wang et al. (2025) studied this question.

View Full Paper

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