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Updated: Jul 4, 2025

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A physically constrained Monte Carlo-Neural Network coupling algorithm for BNCT dose calculation.

Yongquan Wang1, Junliang Du1, Huan Lin1

  • 1School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China.

Medical Physics
|February 1, 2024
PubMed
Summary
This summary is machine-generated.

A new physically constrained Monte Carlo-neural network (PCMC-NN) algorithm accelerates Boron Neutron Capture Therapy (BNCT) dose calculations. This method improves accuracy and reduces errors in critical areas like the skin and tumor, enhancing treatment planning.

Keywords:
Monte Carlocoupling algorithmdose calculationneural network

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Computational Biology

Background:

  • Boron Neutron Capture Therapy (BNCT) is a binary radiotherapy requiring accurate dose calculations for treatment planning.
  • Traditional Monte Carlo (MC) methods are highly accurate but computationally intensive, limiting their clinical application.
  • Existing neural network (NN) approaches accelerate calculations but may introduce significant dose errors, particularly in critical organs like the skin.

Purpose of the Study:

  • To develop a physically constrained MC-NN (PCMC-NN) coupling algorithm for rapid and precise 3D therapeutic dose distribution computation in BNCT.
  • To synergize the accuracy of MC methods with the speed of NNs, using physical conservation laws to ensure reliable dose calculations.
  • To overcome the time-consuming nature of MC methods and reduce dose calculation errors in BNCT.

Main Methods:

  • Collected clinical data from 113 glioblastoma patients.
  • Calculated 3D dose distributions using the MC code PHITS for coarse and detailed grids.
  • Trained a 3D-Unet neural network using patient CT data and coarse grid doses to predict detailed dose distributions.

Main Results:

  • The PCMC-NN algorithm demonstrated superior performance compared to traditional NN and interpolation methods.
  • Significantly reduced dose errors in the skin and Gross Tumor Volume (GTV), achieving a Mean Absolute Percentage Error (MAPE) of 1.6%-4.0%.
  • Dose-volume histograms generated by PCMC-NN closely matched MC results, confirming high computational accuracy with a maximum Mean Absolute Error (MAE) of 0.3 Gy (IsoE).

Conclusions:

  • The PCMC-NN algorithm effectively enhances both the speed and accuracy of BNCT dose calculations.
  • This approach integrates the precision of MC simulations with the efficiency of NNs, validated by physical constraints.
  • The PCMC-NN algorithm shows significant potential for clinical implementation in optimizing BNCT treatment planning.