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Artificial intelligence supported single detector multi-energy proton radiography system.

Brent van der Heyden1, Marie Cohilis2, Kevin Souris2

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This study introduces a novel compact proton radiography system and AI (ProtonDSE) to reduce scatter, improving accuracy for proton therapy patient alignment and dose calculations.

Keywords:
ProtonDSEartificial intelligencedeep learningproton radiography

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Area of Science:

  • Medical Physics
  • Radiological Imaging
  • Computational Modeling

Background:

  • Proton radiography offers potential for patient alignment and CT number to stopping-power ratio conversion in proton therapy.
  • Clinical adoption is hindered by system complexity, scattering, and range mixing issues.

Purpose of the Study:

  • To develop a compact multi-energy proton radiography system integrated with an AI network (ProtonDSE).
  • To mitigate proton scatter and improve accuracy in proton radiography for clinical applications.

Main Methods:

  • A Monte Carlo model of a Proteus®One accelerator was used to simulate proton radiography with 200 and 220 MeV beams.
  • The ProtonDSE AI network was trained to predict proton scatter distributions.
  • A calibration procedure derived water equivalent thickness (WET) images from detector dose response.

Main Results:

  • ProtonDSE achieved a mean absolute percentage error below 1.4% ± 0.4% in predicting scatter distributions.
  • WET accuracy using ProtonDSE-corrected dose was 2.5% ± 2.0%, outperforming total dose (4.3% ± 4.1%).

Conclusions:

  • The proposed compact proton radiography system with ProtonDSE effectively reduces proton scatter.
  • This advancement enhances the accuracy of patient-specific dose calculations and alignment in proton therapy.