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Deep learning-augmented radioluminescence imaging for radiotherapy dose verification.

Mengyu Jia1, Yong Yang1, Yan Wu1

  • 1Department of Radiation Oncology, Stanford University, Stanford, California, USA.

Medical Physics
|September 15, 2021
PubMed
Summary

A new camera-based radioluminescence imaging system (CRIS) with deep learning accurately verifies radiation therapy doses. This novel system converts images to dose maps, improving treatment accuracy and patient safety.

Keywords:
deep learningdosimetryradioluminescence

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Image Processing

Background:

  • Accurate dose verification is critical in radiation therapy.
  • Existing methods can be time-consuming or lack spatial resolution.

Purpose of the Study:

  • To develop and validate a novel dose verification method using a camera-based radioluminescence imaging system (CRIS).
  • To combine CRIS with deep learning for enhanced signal processing and absolute dose prediction.

Main Methods:

  • CRIS utilizes a scintillator-coated chamber, mirror, and camera.
  • A deep learning model performs unsupervised image-to-dose conversion for absolute dose prediction.
  • Validation involved square fields and intensity-modulated radiation therapy (IMRT) cases, comparing results to treatment planning system (TPS) calculations.

Main Results:

  • Mean 2%/2 mm gamma pass rates were 100% for square fields and 97.2% for IMRT fields.
  • Cross-profile gamma pass rates averaged 91% (1%/1 mm), with a 1.15% mean deviation for percentage depth doses (PDDs).

Conclusions:

  • The developed system effectively converts radioluminescence images to water-based dose maps at multiple depths.
  • Achieved spatial resolution is comparable to TPS calculations, offering a promising tool for dose verification.