Kinetic-Diffusion-Rotation Algorithm for Dose Estimation in Electron Beam Therapy

TL;DR

This paper introduces a kinetic-diffusion algorithm that accelerates dose estimation in electron beam therapy by switching between kinetic and random walk motions, achieving significant speedups with minimal accuracy loss.

Contribution

It presents a novel kinetic-diffusion scheme for radiotherapy dose calculations, combining explicit kinetic simulation with a random walk approach in high-collisional regimes.

Findings

33 times faster than kinetic simulation

Maintains accuracy with small modeling error

Effective in 2D lung CT dose estimation

Abstract

Monte Carlo methods are state-of-the-art when it comes to dosimetric computations in radiotherapy. However, the execution time of these methods suffers in high-collisional regimes. We address this problem by introducing a kinetic-diffusion particle tracing scheme. This algorithm, first proposed in the context of neutral transport in fusion energy, relies on explicit simulation of the kinetic motion in low-collisional regimes and dynamically switches to motion based on a random walk in high-collisional regimes. The random walk motion maintains the first two moments (mean and variance) of the kinetic motion. We derive an analytic formula for the mean kinetic motion and discuss the addition of a multiple scattering distribution to the algorithm. In contrast to neutral transport, the radiation transfer setting does not readily admit to an analytical expression for the variance of the kinetic motion, and we therefore resort to the use of a lookup table. We test the algorithm for dosimetric computations in radiation therapy on a 2D CT scan of a lung patient. Using a simple particle model, our Python implementation of the algorithm is nearly 33 times faster than an equivalent kinetic simulation at the cost of a small modeling error.