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  3. Fast Spot-scanning Proton Dose Calculation Method With Uncertainty Quantification Using A Three-dimensional Convolutional Neural Network.
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  3. Fast Spot-scanning Proton Dose Calculation Method With Uncertainty Quantification Using A Three-dimensional Convolutional Neural Network.

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Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional

Yusuke Nomura1, Jeff Wang2,3, Hiroki Shirato2,4

  • 1Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo 060-8638, Japan.

Physics in Medicine and Biology
|July 1, 2020

View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning method using a 3D convolutional neural network (3D-CNN) accurately calculates proton therapy doses in near real-time. This approach also estimates dose uncertainties, improving efficiency and applicability for image-guided proton therapy.

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence in Medicine

Background:

  • Proton therapy offers precise dose delivery but requires accurate dose calculation.
  • Real-time dose calculation and uncertainty estimation are crucial for advanced applications like image-guided proton therapy.
  • Current methods can be computationally intensive, limiting near real-time applications.

Purpose of the Study:

  • To develop a near-real-time spot-scanning proton dose calculation method using a 3D convolutional neural network (3D-CNN).
  • To incorporate probabilistic uncertainty estimation into the dose calculation process.
  • To evaluate the accuracy, efficiency, and adaptability of the 3D-CNN model for proton therapy dose prediction.

Main Methods:

  • A 3D-CNN model was trained and tested on CT images and clinical target volumes from 215 head and neck cancer patients.
  • Spot beam data was converted into a 'peak map' (PM) input for the 3D-CNN.
  • Transfer learning was employed to adapt the model to different beam parameters and calculation algorithms with minimal data.

    • Main Results:

    • The 3D-CNN model achieved accurate 3D proton dose distribution calculations with a mean absolute error of 0.778 cGyE.
    • Predicted uncertainties correlated with dose errors at high contrast edges.
    • Inference time was approximately 0.8 seconds per plan on a consumer-grade GPU, demonstrating near real-time capability.

    Conclusions:

    • The developed 3D-CNN method provides accurate and efficient near-real-time proton dose calculation with uncertainty estimation.
    • The model is adaptable to various beam configurations and calculation settings via transfer learning.
    • This approach holds significant potential for dose verification, image-guided proton therapy, and other clinical applications.