Artificial intelligence supported single detector multi-energy proton radiography system

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Authors

Institutes & Expert groups

  • UCL - Université catholique de Louvain
  • KUL - Katholieke Universiteit Leuven

Documents & links

DOI

Abstract

Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specificCT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4%.±.0.4% in our test dataset. For one example patient, detector dose toWETconversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determinedWETaccuracy was compared with respect to the referenceWETby idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3%.±.4.1%for WET(ITotal), 2.2%.±.1.4%for WET(IPrimary), and 2.5%.±.2.0% for WET(ICorrected).

Details

Original languageEnglish
Article number105001
Pages (from-to)1-12
Number of pages12
JournalPhysics in Medicine and Biology
Volume66
DOIs
Publication statusPublished - 4 May 2021

Keywords

  • Proton radiography, Artificial intelligence, Deep learning, ProtonDSE

ID: 7410758