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Sub-millimeter precise photon interaction position determination in large monolithic scintillators via convolutional

M Kawula1, T M Binder1,2, S Liprandi1

  • 1Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching b. München, Germany.

Physics in Medicine and Biology
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A new convolutional neural network (CNN) algorithm precisely determines gamma-ray interaction positions in Compton cameras (CCs) for ion beam range verification. This significantly improves spatial resolution in LaBr3:Ce and CeBr3 detectors, advancing clinical applications.

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Compton camerabeam range monitoringhadron therapymonolithic scintillatorneural networksradiation detectionspatial resolution

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Area of Science:

  • Medical Physics
  • Nuclear Instrumentation
  • Computational Imaging

Background:

  • Compton cameras (CCs) are crucial for ion beam range verification using prompt-gamma imaging.
  • Accurate determination of gamma-ray interaction positions in scintillators is essential for CC performance.
  • Existing algorithms for position determination have limitations in spatial resolution and processing speed.

Purpose of the Study:

  • To develop and apply a convolutional neural network (CNN)-based algorithm for precise gamma-ray interaction position determination.
  • To evaluate the spatial resolution of the CNN algorithm using LaBr3:Ce and CeBr3 scintillation crystals.
  • To assess the potential of the developed algorithm for clinical applications in ion beam range verification.

Main Methods:

  • Development of a CNN algorithm for interaction position reconstruction in large monolithic scintillators.
  • Utilized LaBr3:Ce and CeBr3 crystals (50.8 mm × 50.8 mm × 30 mm) coupled to 8x8 pixel segmented PMTs.
  • Performed 2D detector scans with collimated 137Cs and 60Co photon sources at energies 662 keV, 1.17 MeV, and 1.33 MeV.

Main Results:

  • Achieved spatial resolution below 1.11(8) mm for CeBr3 and 0.98(7) mm for LaBr3:Ce across investigated energies.
  • Improved performance by over 2.5 times compared to the previous categorical average pattern algorithm.
  • The CNN algorithm processes up to 10^4 events/second on a single GPU with a low memory footprint.

Conclusions:

  • The developed CNN algorithm significantly enhances spatial resolution for gamma-ray interaction position determination in CCs.
  • This advancement is critical for the clinical in vivo applicability of CCs in ion beam range verification.
  • The algorithm's efficiency and accuracy pave the way for improved cancer therapy monitoring.