A general-purpose Monte Carlo particle transport code based on inverse transform sampling for radiotherapy dose...

Area of Science:

• Medical Physics
• Computational Physics
• Radiotherapy Physics

Background:

• Monte Carlo (MC) methods are crucial for radiotherapy simulations.
• Thread divergence in GPU-accelerated MC codes using acceptance-rejection sampling is a significant challenge.
• Inverse transform sampling offers a solution to thread divergence but was limited to photon transport.

Purpose of the Study:

• To develop and demonstrate a MC package, Particle Transport in Media (PTM), for coupled photon-electron transport simulation.
• To implement inverse transform sampling for both photon and electron transport in PTM.
• To validate the accuracy and efficiency of PTM against established methods and experimental data.

Main Methods:

• Developed the PTM package incorporating inverse transform sampling for coupled photon-electron transport.
• Modeled photon interactions including Rayleigh scattering, Compton scattering, photoelectric effect, and pair production.
• Simulated electron transport using a class II condensed history scheme with explicit treatment of catastrophic events and grouped subthreshold interactions.
• Employed a random-hinge electron step correction and a modified PRESTA boundary crossing algorithm for enhanced accuracy.

Main Results:

• Achieved high accuracy in coupled photon-electron transport simulations using inverse transform sampling.
• Benchmark studies against EGSnrc and experimental measurements showed excellent agreement.
• Gamma indices for dose distributions exceeded 99.6% for all scenarios under the 2%/2 mm criteria.

Conclusions:

• Successfully implemented inverse transform sampling for coupled photon-electron transport simulation in the PTM package.
• PTM demonstrates a viable and accurate method for accelerating MC simulations in radiotherapy.
• The developed algorithms improve the accuracy and efficiency of MC-based radiotherapy treatment planning.